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10.1101/2020.07.23.20160762 | Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics | Variation in individual susceptibility or exposure to infection accelerates the rate at which populations acquire immunity by natural infection. Individuals that are more susceptible or more exposed tend to be infected earlier and hence selectively removed from the susceptible pool, decelerating the incidence of new infections. Eventually, susceptible numbers become low enough to prevent epidemic growth or, in other words, the herd immunity threshold is reached. Here we fit epidemiological models, with inbuilt distributions of susceptibility or exposure, to SARS-CoV-2 epidemics in Spain and Portugal, to estimate basic reproduction numbers (R0) alongside coefficients of individual variation (CV) and the effects of control measures. Herd immunity thresholds are then calculated as [Formula] or [Formula], depending on whether variation is in susceptibility or exposure. Our inferences result in lower herd immunity thresholds than what would be expected if population imvmunity was to be induced by random infection or vaccination, 1 - 1/R0. | epidemiology |
10.1101/2020.07.23.20159624 | Genetic surveillance in the Greater Mekong Subregion and South Asia to support malaria control and elimination | National Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures. Samples are processed by high-throughput technologies to genotype several drug resistance markers, species markers and a genomic barcode, delivering reports of genotypes and phenotype predictions, used to map prevalence of resistance to multiple drugs. GenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9,623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces. GenRe-Mekong facilitates data sharing by aggregating results from different countries, enabling cross-border resistance monitoring.
Impact StatementLarge-scale genetic surveillance of malaria implemented by National Malaria Control Programmes informs public health decision makers about the spread of strains resistant to antimalarials.
FundingBill & Melinda Gates Foundation, Wellcome Trust, UK Medical Research Council, UK Department for International Development, NIAID | public and global health |
10.1101/2020.07.18.20156828 | Local public health officials and COVID-19: Evidence from China | Local public health authorities are at the forefront of fighting COVID-19. They monitor its spread in the local population and advise the local government on whether to close schools and businesses. Examining their role in battling COVID-19 will inform the public how best to prepare for a public health emergency. This study examined whether more competent local public health officials in China are more effective in fighting COVID-19, where competence was measured by the public health officials professional background. Only 38% of the heads of the public health departments of Chinese cities have a medical background. Cities with medical professionals as the head of public health departments had lower infection rates and death rates from COVID-19. The results were significant only at the start of the outbreak. Our results suggest that we should staff local public health authorities with competent professionals to better combat a pandemic. | health policy |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 10 February 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 8 April 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 8 April 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 8 April 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 14 July 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 14 July 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 14 July 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 14 July 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 15 December 2021 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.17.20155218 | ISARIC COVID-19 Clinical Data Report: 27 March 2022 | ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries.
The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19.
For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution
C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age
C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time
C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports
C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above
C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%)
C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics
C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids
C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease
C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/ | infectious diseases |
10.1101/2020.07.22.20158352 | Anatomy of digital contact tracing: role of age, transmission setting, adoption and case detection | The efficacy of digital contact tracing against COVID-19 epidemic is debated: smartphone penetration is limited in many countries, non-uniform across age groups, with low coverage among elderly, the most vulnerable to SARS-CoV-2. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact-survey and epidemiological information to describe the risk factors for exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan. | epidemiology |
10.1101/2020.07.21.20158303 | Threshold analyses on rates of testing, transmission, and contact for COVID-19 control in a university setting | We simulated epidemic projections of a potential COVID-19 outbreak in a residential university population in the United States under varying combinations of asymptomatic tests (5% to 33% per day), transmission rates (2.5% to 14%), and contact rates (1 to 25), to identify the contact rate threshold that, if exceeded, would lead to exponential growth in infections. Using this, we extracted contact rate thresholds among non-essential workers, population size thresholds in the absence of vaccines, and vaccine coverage thresholds. We further stream-lined our analyses to transmission rates of 5 to 8%, to correspond to the reported levels of face-mask-use/physical-distancing during the 2020 pandemic.
Our results suggest that, in the absence of vaccines, testing alone without reducing population size would not be sufficient to control an outbreak. If the population size is lowered to 34% (or 44%) of the actual population size to maintain contact rates at 4 (or 7) among non-essential workers, mass tests at 25% (or 33%) per day would help control an outbreak. With the availability of vaccines, the campus can be kept at full population provided at least 95% are vaccinated. If vaccines are partially available such that the coverage is lower than 95%, keeping at full population would require asymptomatic testing, either mass tests at 25% per day if vaccine coverage is at 63-79%, or mass tests at 33% per day if vaccine coverage is at 53-68%. If vaccine coverage is below 53%, to control an outbreak, in addition to mass tests at 33% per day, it would also require lowering the population size to 90%, 75%, and 60%, if vaccine coverage is at 38-53%, 23-38%, and below 23%, respectively.
Threshold estimates from this study, interpolated over the range of transmission rates, can collectively help inform campus level preparedness plans for adoption of face mask/physical-distancing, testing, remote instructions, and personnel scheduling, during non-availability or partial-availability of vaccines, in the event of SARS-Cov2-type disease outbreaks. | infectious diseases |
10.1101/2020.07.21.20158303 | Threshold analyses on combinations of testing, population size, and vaccine coverage for COVID-19 control in a university setting | We simulated epidemic projections of a potential COVID-19 outbreak in a residential university population in the United States under varying combinations of asymptomatic tests (5% to 33% per day), transmission rates (2.5% to 14%), and contact rates (1 to 25), to identify the contact rate threshold that, if exceeded, would lead to exponential growth in infections. Using this, we extracted contact rate thresholds among non-essential workers, population size thresholds in the absence of vaccines, and vaccine coverage thresholds. We further stream-lined our analyses to transmission rates of 5 to 8%, to correspond to the reported levels of face-mask-use/physical-distancing during the 2020 pandemic.
Our results suggest that, in the absence of vaccines, testing alone without reducing population size would not be sufficient to control an outbreak. If the population size is lowered to 34% (or 44%) of the actual population size to maintain contact rates at 4 (or 7) among non-essential workers, mass tests at 25% (or 33%) per day would help control an outbreak. With the availability of vaccines, the campus can be kept at full population provided at least 95% are vaccinated. If vaccines are partially available such that the coverage is lower than 95%, keeping at full population would require asymptomatic testing, either mass tests at 25% per day if vaccine coverage is at 63-79%, or mass tests at 33% per day if vaccine coverage is at 53-68%. If vaccine coverage is below 53%, to control an outbreak, in addition to mass tests at 33% per day, it would also require lowering the population size to 90%, 75%, and 60%, if vaccine coverage is at 38-53%, 23-38%, and below 23%, respectively.
Threshold estimates from this study, interpolated over the range of transmission rates, can collectively help inform campus level preparedness plans for adoption of face mask/physical-distancing, testing, remote instructions, and personnel scheduling, during non-availability or partial-availability of vaccines, in the event of SARS-Cov2-type disease outbreaks. | infectious diseases |
10.1101/2020.07.24.20157982 | Dynamics of SARS-CoV-2 with Waning Immunity in the UK Population | The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48-142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than one year. Using an age-structured, deterministic model, we explore potential immunity dynamics using contact data from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalised individuals, a year for hospitalised individuals, and the effective reproduction number after lockdown ends is 1.2 (our worst case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 387,000 infectious individuals and 125,000 daily new cases; three-fold greater than in a scenario with permanent immunity. Our models suggests that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown in June until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 is expected to increase due to the secondary wave. Overall, our analysis presents considerations for policy makers on the longer term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection as immunity to reinfection is not permanent. | epidemiology |
10.1101/2020.07.21.20157776 | Demethylation and upregulation of an oncogene post hypomethylating treatment | BackgroundWhile hypomethylating agents (HMA) are currently used to treat myelodysplastic syndrome (MDS) and patients with cancer, their effects on reactivation and/or upregulation of oncogenes are generally not well elucidated. SALL4 is a known oncogene that plays an important role in MDS. In this study, we examined the impact of HMA on SALL4 methylation and expression.
MethodsPaired bone marrow samples from a cohort of MDS patients on the BMT-AZA trial, collected before and after four cycles of azacytidine (AZA) treatment, were used to explore the relationship between changes in SALL4 expression, treatment response and clinical outcome with a follow-up of up to 40 months. No/low-SALL4 expressing leukemic cell lines were used to study the relationship between SALL4 methylation and expression. A novel locus-specific demethylation technology, CRISPR-DNMT1-interacting RNA (CRISPR-DiR), was used to identify the CpG island critical for SALL4 expression.
ResultsIn MDS patients, we noted SALL4 upregulation after AZA treatment in 40% of the cases. Significantly, patients with SALL4 upregulation had a worse outcome. Using CRISPR-DiR, we discovered that demethylation of a 500bp CpG island within the 5UTR-Exon1-Intron1 region was critical for SALL4 expression. Importantly, in cell lines and patients, we confirmed that HMA treatment led to demethylation of the same CpG region and upregulation of SALL4 expression.
ConclusionsCRISPR-DiR was useful to define the critical region important for gene activation. Along with analysis of patient samples, we demonstrated that demethylation and upregulation of an oncogene after HMA treatment can indeed occur and should be further studied. | hematology |
10.1101/2020.07.20.20151506 | Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification | To increase situational awareness and support evidence-based policy-making, we formulated a mathematical model for COVID-19 transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating region-specific models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting.
Article Summary LineWe report models for regional COVID-19 epidemics and use of Bayesian inference to quantify uncertainty in daily predictions of expected reporting of new cases, enabling identification of new trends in surveillance data. | infectious diseases |
10.1101/2020.07.25.20161984 | Power Law in COVID-19 Cases in China | The novel coronavirus (COVID-19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID-19 confirmed cases in China--the original epicenter of the outbreak. We show that the upper tail of COVID-19 cases in Chinese cities is well described by a power law distribution, with exponent around one in the early phases of the outbreak (when the number of cases was growing rapidly) and less than one thereafter. This finding is significant because it implies that (i) COVID-19 cases in China is heavy-tailed and disperse; (ii) a few cities account for a disproportionate share of COVID-19 cases; and (iii) the distribution generally has no finite mean or variance. We find that a proportionate random growth model predicated by Gibrats law offers a plausible explanation for the emergence of a power law in the distribution of COVID-19 cases in Chinese cities in the early phases of the outbreak. | infectious diseases |
10.1101/2020.07.25.20156471 | Mapping social distancing measures to the reproduction number for COVID-19 | BackgroundIn the absence of a vaccine, SARS-CoV-2 transmission has been controlled by preventing person-to-person interactions via social distancing measures. In order to re-open parts of society, policy-makers need to consider how combinations of measures will affect transmission and understand the trade-offs between them.
MethodsWe use age-specific social contact data, together with epidemiological data, to quantify the components of the COVID-19 reproduction number. We estimate the impact of social distancing policies on the reproduction number by turning contacts on and off based on context and age. We focus on the impact of re-opening schools against a background of wider social distancing measures.
ResultsWe demonstrate that pre-collected social contact data can be used to provide a time-varying estimate of the reproduction number (R). We find that following lockdown (when R=0.7 (95% CI 0.6, 0.8)), opening primary schools as a modest impact on transmission (R = 0.89 (95%CI: 0.82 - 0.97)) as long as other social interactions are not increased. Opening secondary and primary schools is predicted to have a larger impact (R = 1.22, 95%CI: 1.02 - 1.53)). Contact tracing and COVID security can be used to mitigate the impact of increased social mixing to some extent, however social distancing measures are still required to control transmission.
ConclusionsOur approach has been widely used by policy-makers to project the impact of social distancing measures and assess the trade-offs between them. Effective social distancing, contact tracing and COVID-security are required if all age groups are to return to school while controlling transmission. | infectious diseases |
10.1101/2020.07.23.20069468 | Epidemiological dynamics of enterovirus D68 in the US: implications for acute flaccid myelitis | The lack of active surveillance for enterovirus D68 (EV-D68) in the US has hampered the ability to assess the relationship with predominantly biennial epidemics of acute flaccid myelitis (AFM), a rare but serious neurological condition. Using novel surveillance data from the BioFire(R) Syndromic Trends (Trend) epidemiology network, we characterize the epidemiological dynamics of EV-D68 and demonstrate strong spatiotemporal association with AFM. Although the recent dominant biennial cycles of EV-D68 dynamics may not be stable, we show that a major EV-D68 epidemic, and hence an AFM outbreak, would still be possible in 2020 under normal epidemiological conditions. Significant social distancing due to the ongoing COVID-19 pandemic could reduce the size of an EV-D68 epidemic in 2020, illustrating the potential broader epidemiological impact of the pandemic.
NOTEThis preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. | infectious diseases |
10.1101/2020.07.26.20162420 | Time-dependent heterogeneity leads to transient suppression of COVID-19 epidemic, not herd immunity | Epidemics generally spread through a succession of waves that reflect factors on multiple time-scales. On short time-scales, superspreading events lead to burstiness and overdispersion, while long-term persistent heterogeneity in susceptibility is expected to lead to a reduction in the infection peak and the herd immunity threshold (HIT). Here, we develop a general approach to encompass both time-scales, including time variations in individual social activity, and demonstrate how to incorporate them phenomenologically into a wide class of epidemiological models through parameterization. We derive a non-linear dependence of the effective reproduction number Re on the susceptible population fraction S. We show that a state of transient collective immunity (TCI) emerges well below the HIT during early, high-paced stages of the epidemic. However, this is a fragile state that wanes over time due to changing levels of social activity, and so the infection peak is not an indication of herd immunity: subsequent waves can and will emerge due to behavioral changes in the population, driven (e.g.) by seasonal factors. Transient and long-term levels of heterogeneity are estimated by using empirical data from the COVID-19 epidemic as well as from real-life face-to-face contact networks. These results suggest that the hardest-hit areas, such as NYC, have achieved TCI following the first wave of the epidemic, but likely remain below the long-term HIT. Thus, in contrast to some previous claims, these reqions can still experience subsequent waves.
O_TEXTBOXSignificance Statement
Epidemics generally spread through a succession of waves that reflect factors on multiple time-scales. Here, we develop a general approach to encompass super-spreading and population heterogeneity, and demonstrate that a fragile state of transient collective immunity (TCI) emerges well below the HIT during early, high-paced stages of the epidemic. However, this is not an indication of herd immunity: subsequent waves can and will emerge due to behavioral changes in the population, driven (e.g.) by seasonal factors. Analysis of empirical data suggests that even in locations with strong first waves of COVID-19, subsequent waves will still emerge.
C_TEXTBOX | epidemiology |
10.1101/2020.07.27.20162487 | Body mass index multiple regression formula testable by all nine Bradford Hill causality criteria: Artificial intelligence analytics applied to worldwide ecological BMI and risk factor data to model obesity | BackgroundArtificial intelligence (AI) analytics have not been applied to global burden of disease (GBD) risk factor data to study population health. The comparative risk assessment (CRA) systematic literature review-based methodology for population attributable fractions (PAFs in percents) calculations has not been utilised for quantifying dietary and other risk factors for body mass index kg/M2 (BMI).
MethodsInstitute of Health Metrics and Evaluation (IHME) staff and volunteer collaborators analysed over 12,000 GBD risk factor surveys of people from 195 countries and synthesized the data into representative mean cohort BMI and risk factor values. We formatted IHME GBD data relevant to BMI and associated risk factors. We empirically explored the univariate and multiple regression correlations of BMI risk factors with worldwide BMI to derive a BMI multiple regression formula (BMI formula). Main outcome measures included the performances of the BMI formula when tested with all nine Bradford Hill causality criteria each scored on a 0-5 scale: 0=negative to 5=very strong support.
FindingsThe BMI formula derived, with all foods in kilocalories/day (kcal/day), BMI formula risk factor coefficients were adjusted to equate with their PAFs. BMI increasing foods had "+" signs and BMI decreasing foods "-" signs. Total BMI formula PAF=80.96%. BMI formula=(0.37%*processed meat + 4.23%*red meat + 0.02%*fish + 2.24%*milk + 5.67%*poultry + 1.77%*eggs + 0.34%*alcohol + 0.99%*sugary beverages + 0.04%*corn + 0.72%*potatoes + 8.48%*saturated fatty acids + 3.89%*polyunsaturated fatty acids + 0.27%*trans fatty acids - 2.99%*fruit - 4.07%*vegetables - 0.37%*nuts and seeds - 0.45%*whole grains - 1.49%*legumes - 8.62%*rice - 0.10%*sweet potatoes - 7.45% physical activity (METs/week) - 20.38%*child underweight + 6.02%*sex (male=1, female=2))*0.05012 + 21.77. BMI formula versus BMI: r=0.907, 95% CI: 0.903 to 0.911, p<0.0001. Bradford Hill causality criteria test scores (0-5): (1) strength=5, (2) experimentation=5, (3) consistency=5, (4) dose-response=5, (5) temporality=5, (6) analogy=4, (7), plausibility=5, (8) specificity=5, and (9) coherence=5. Total score=44/45.
InterpretationNine Bradford Hill causality criteria strongly supported a causal relationship between the BMI formula derived and mean BMIs of worldwide cohorts. The artificial intelligence methodology introduced could inform individual, clinical, and public health strategies regarding overweight/obesity prevention/treatment and other health outcomes.
FundingNone
Research in contextO_ST_ABSEvidence before this studyC_ST_ABSComparative risk assessment (CRA) systematic literature review-based methodology has been used in worldwide global burden of disease (GBD) analysis to determine population attributable fraction(s) (PAF(s)) for one or more risk factors for various health outcomes. So far, CRA has not been applied to derive PAFs for dietary and other risk factors for worldwide BMI. Artificial intelligence (AI) analytics has not yet been applied to worldwide GBD data as an alternative to the CRA methodology for determining risk factor PAFs for health outcomes.
Added value of this study{square}A multiple regression derived BMI formula (BMI formula) including PAFs of 20 dietary risk factors, physical activity, childhood severe underweight, and sex satisfied all nine Bradford Hill causality criteria. The BMI formula also plausibly predicted the long-term BMI outcomes related to various dietary and physical activity scenarios. All the BMI formulas 24 risk factor PAFs were consistent in sign (+ or -) with the preponderance of previously published studies on those risk factors related to BMI.
Implications of all the available evidenceThe AI analytics methodology of GBD data modeling of BMI and associated risk factors infers causality of the BMI formula estimates with BMI worldwide and BMIs of subsets. This methodology may enable multiple regression formulas for risk factors of health outcomes for a range of non-communicable diseases--testable by Bradford Hill causality criteria. | public and global health |
10.1101/2020.07.27.20162487 | Body mass index multiple regression formula testable by all nine Bradford Hill causality criteria: Artificial intelligence analytics applied to global burden of disease data relating to the obesity epidemic | BackgroundArtificial intelligence (AI) analytics have not been applied to global burden of disease (GBD) risk factor data to study population health. The comparative risk assessment (CRA) systematic literature review-based methodology for population attributable fractions (PAFs in percents) calculations has not been utilised for quantifying dietary and other risk factors for body mass index kg/M2 (BMI).
MethodsInstitute of Health Metrics and Evaluation (IHME) staff and volunteer collaborators analysed over 12,000 GBD risk factor surveys of people from 195 countries and synthesized the data into representative mean cohort BMI and risk factor values. We formatted IHME GBD data relevant to BMI and associated risk factors. We empirically explored the univariate and multiple regression correlations of BMI risk factors with worldwide BMI to derive a BMI multiple regression formula (BMI formula). Main outcome measures included the performances of the BMI formula when tested with all nine Bradford Hill causality criteria each scored on a 0-5 scale: 0=negative to 5=very strong support.
FindingsThe BMI formula derived, with all foods in kilocalories/day (kcal/day), BMI formula risk factor coefficients were adjusted to equate with their PAFs. BMI increasing foods had "+" signs and BMI decreasing foods "-" signs. Total BMI formula PAF=80.96%. BMI formula=(0.37%*processed meat + 4.23%*red meat + 0.02%*fish + 2.24%*milk + 5.67%*poultry + 1.77%*eggs + 0.34%*alcohol + 0.99%*sugary beverages + 0.04%*corn + 0.72%*potatoes + 8.48%*saturated fatty acids + 3.89%*polyunsaturated fatty acids + 0.27%*trans fatty acids - 2.99%*fruit - 4.07%*vegetables - 0.37%*nuts and seeds - 0.45%*whole grains - 1.49%*legumes - 8.62%*rice - 0.10%*sweet potatoes - 7.45% physical activity (METs/week) - 20.38%*child underweight + 6.02%*sex (male=1, female=2))*0.05012 + 21.77. BMI formula versus BMI: r=0.907, 95% CI: 0.903 to 0.911, p<0.0001. Bradford Hill causality criteria test scores (0-5): (1) strength=5, (2) experimentation=5, (3) consistency=5, (4) dose-response=5, (5) temporality=5, (6) analogy=4, (7), plausibility=5, (8) specificity=5, and (9) coherence=5. Total score=44/45.
InterpretationNine Bradford Hill causality criteria strongly supported a causal relationship between the BMI formula derived and mean BMIs of worldwide cohorts. The artificial intelligence methodology introduced could inform individual, clinical, and public health strategies regarding overweight/obesity prevention/treatment and other health outcomes.
FundingNone
Research in contextO_ST_ABSEvidence before this studyC_ST_ABSComparative risk assessment (CRA) systematic literature review-based methodology has been used in worldwide global burden of disease (GBD) analysis to determine population attributable fraction(s) (PAF(s)) for one or more risk factors for various health outcomes. So far, CRA has not been applied to derive PAFs for dietary and other risk factors for worldwide BMI. Artificial intelligence (AI) analytics has not yet been applied to worldwide GBD data as an alternative to the CRA methodology for determining risk factor PAFs for health outcomes.
Added value of this study{square}A multiple regression derived BMI formula (BMI formula) including PAFs of 20 dietary risk factors, physical activity, childhood severe underweight, and sex satisfied all nine Bradford Hill causality criteria. The BMI formula also plausibly predicted the long-term BMI outcomes related to various dietary and physical activity scenarios. All the BMI formulas 24 risk factor PAFs were consistent in sign (+ or -) with the preponderance of previously published studies on those risk factors related to BMI.
Implications of all the available evidenceThe AI analytics methodology of GBD data modeling of BMI and associated risk factors infers causality of the BMI formula estimates with BMI worldwide and BMIs of subsets. This methodology may enable multiple regression formulas for risk factors of health outcomes for a range of non-communicable diseases--testable by Bradford Hill causality criteria. | public and global health |
10.1101/2020.07.27.20162487 | Artificial intelligence analytics applied to body mass index global burden of disease worldwide cohort data derives a multiple regression formula with population attributable fraction risk factor coefficients testable by all nine Bradford Hill causality criteria | BackgroundArtificial intelligence (AI) analytics have not been applied to global burden of disease (GBD) risk factor data to study population health. The comparative risk assessment (CRA) systematic literature review-based methodology for population attributable fractions (PAFs in percents) calculations has not been utilised for quantifying dietary and other risk factors for body mass index kg/M2 (BMI).
MethodsInstitute of Health Metrics and Evaluation (IHME) staff and volunteer collaborators analysed over 12,000 GBD risk factor surveys of people from 195 countries and synthesized the data into representative mean cohort BMI and risk factor values. We formatted IHME GBD data relevant to BMI and associated risk factors. We empirically explored the univariate and multiple regression correlations of BMI risk factors with worldwide BMI to derive a BMI multiple regression formula (BMI formula). Main outcome measures included the performances of the BMI formula when tested with all nine Bradford Hill causality criteria each scored on a 0-5 scale: 0=negative to 5=very strong support.
FindingsThe BMI formula derived, with all foods in kilocalories/day (kcal/day), BMI formula risk factor coefficients were adjusted to equate with their PAFs. BMI increasing foods had "+" signs and BMI decreasing foods "-" signs. Total BMI formula PAF=80.96%. BMI formula=(0.37%*processed meat + 4.23%*red meat + 0.02%*fish + 2.24%*milk + 5.67%*poultry + 1.77%*eggs + 0.34%*alcohol + 0.99%*sugary beverages + 0.04%*corn + 0.72%*potatoes + 8.48%*saturated fatty acids + 3.89%*polyunsaturated fatty acids + 0.27%*trans fatty acids - 2.99%*fruit - 4.07%*vegetables - 0.37%*nuts and seeds - 0.45%*whole grains - 1.49%*legumes - 8.62%*rice - 0.10%*sweet potatoes - 7.45% physical activity (METs/week) - 20.38%*child underweight + 6.02%*sex (male=1, female=2))*0.05012 + 21.77. BMI formula versus BMI: r=0.907, 95% CI: 0.903 to 0.911, p<0.0001. Bradford Hill causality criteria test scores (0-5): (1) strength=5, (2) experimentation=5, (3) consistency=5, (4) dose-response=5, (5) temporality=5, (6) analogy=4, (7), plausibility=5, (8) specificity=5, and (9) coherence=5. Total score=44/45.
InterpretationNine Bradford Hill causality criteria strongly supported a causal relationship between the BMI formula derived and mean BMIs of worldwide cohorts. The artificial intelligence methodology introduced could inform individual, clinical, and public health strategies regarding overweight/obesity prevention/treatment and other health outcomes.
FundingNone
Research in contextO_ST_ABSEvidence before this studyC_ST_ABSComparative risk assessment (CRA) systematic literature review-based methodology has been used in worldwide global burden of disease (GBD) analysis to determine population attributable fraction(s) (PAF(s)) for one or more risk factors for various health outcomes. So far, CRA has not been applied to derive PAFs for dietary and other risk factors for worldwide BMI. Artificial intelligence (AI) analytics has not yet been applied to worldwide GBD data as an alternative to the CRA methodology for determining risk factor PAFs for health outcomes.
Added value of this study{square}A multiple regression derived BMI formula (BMI formula) including PAFs of 20 dietary risk factors, physical activity, childhood severe underweight, and sex satisfied all nine Bradford Hill causality criteria. The BMI formula also plausibly predicted the long-term BMI outcomes related to various dietary and physical activity scenarios. All the BMI formulas 24 risk factor PAFs were consistent in sign (+ or -) with the preponderance of previously published studies on those risk factors related to BMI.
Implications of all the available evidenceThe AI analytics methodology of GBD data modeling of BMI and associated risk factors infers causality of the BMI formula estimates with BMI worldwide and BMIs of subsets. This methodology may enable multiple regression formulas for risk factors of health outcomes for a range of non-communicable diseases--testable by Bradford Hill causality criteria. | public and global health |
10.1101/2020.07.28.20162941 | Sensitive detection of SARS-CoV-2 seroconversion by flow cytometry reveals the presence of nucleoprotein-reactive antibodies in unexposed individuals | There is an ongoing need of developing sensitive and specific methods for the determination of SARS-CoV-2 seroconversion. For this purpose, we have developed a multiplexed flow cytometric bead array (C19BA) that allows the identification of IgG and IgM antibodies against three immunogenic proteins simultaneously: the spike receptor-binding domain (RBD), the spike protein subunit 1 (S1) and the nucleoprotein (N). Using different cohorts of samples collected before and after the pandemic, we show that this assay is more sensitive than ELISAs performed in our laboratory. The combination of three viral antigens allows for the interrogation of full seroconversion. Importantly, we have detected N-reactive antibodies in COVID-19-negative individuals. Here we present an immunoassay that can be easily implemented and has superior potential to detect low antibody titers compared to current gold standard serology methods. | allergy and immunology |
10.1101/2020.07.28.20164012 | Efficacy of chloroquine and hydroxychloroquine in treating COVID-19 infection: a meta-review of systematic reviews and an updated meta-analysis | ObjectiveTo synthesize findings from systematic reviews and meta-analyses on the efficacy and safety of chloroquine (CQ) and hydroxychloroquine (HCQ) with or without Azithromycin for treating COVID-19, and to update the evidence using a meta-analysis.
MethodsA comprehensive search was carried out in electronic databases for systematic reviews, meta-analyses and experimental studies which investigated the efficacy and safety of CQ, HCQ with or without Azithromycin to treat COVID-19. Findings from the reviews were synthesised using tables and forest plots and the quality effect model was used for the updated meta-analysis. The main outcomes were mortality, the need for intensive care services, disease exacerbation, viral clearance and occurrence of adverse events.
ResultsThirteen reviews with 40 primary studies were included. Two meta-analyses reported a high risk of mortality, with ORs of 2.2 and 3.0, and the two others found no association between HCQ and mortality. Findings from two meta-analyses showed that HCQ with Azithromycin increased the risk of mortality, with similar ORs of 2.5. The updated meta-analysis of experimental studies showed that the drugs were not effective in reducing mortality (RR 1.1, 95%CI 1.0-1.3, I2 =0.0%), need for intensive care services (OR 1.1, 95%CI 0.9-1.4, I2 =0.0%), virological cure (OR 1.5, 95%CI 0.5-4.4, I2 =39.6%) or disease exacerbation (OR 1.2, 95%CI 0.3-5.9, I2 =31.9%) but increased the odds of adverse events (OR 12,3, 95%CI 2.5-59.9, I2 =76.6%).
ConclusionThere is conclusive evidence that CQ and HCQ, with or without Azithromycin are not effective in treating COVID-19 or its exacerbation.
RegistrationPROSPERO: CRD42020191353 | epidemiology |
10.1101/2020.07.29.20162917 | The Effect of Convalescent Plasma Therapy on COVID-19 Patient Mortality: Systematic Review and Meta-analysis | To determine the effect of COVID-19 convalescent plasma on mortality, we aggregated patient outcome data from randomized clinical trials, matched control, case series, and case report studies. Fixed-effects analyses demonstrated that hospitalized COVID-19 patients transfused with convalescent plasma exhibited a ~57% reduction in mortality rate (10%) compared to matched-patients receiving standard treatments (22%; OR: 0.43, P < 0.001). These data provide evidence favouring the efficacy of human convalescent plasma as a therapeutic agent in hospitalized COVID-19 patients. | infectious diseases |
10.1101/2020.07.28.20164038 | Initial evaluation of a mobile SARS-CoV-2 RT-LAMP testing strategy | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control in the United States remains hampered, in part, by testing limitations. We evaluated a simple, outdoor, mobile, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay workflow where self-collected saliva is tested for SARS-CoV-2 RNA. From July 16 to November 19, 2020, 4,704 surveillance samples were collected from volunteers and tested for SARS-CoV-2 at 5 sites. A total of 21 samples tested positive for SARS-CoV-2 by RT-LAMP; 12 were confirmed positive by subsequent quantitative reverse-transcription polymerase chain reaction (qRT-PCR) testing, while 8 were negative for SARS-CoV-2 RNA, and 1 could not be confirmed because the donor did not consent to further molecular testing. We estimated the RT-LAMP assays false-negative rate from July 16 to September 17, 2020 by pooling residual heat-inactivated saliva that was unambiguously negative by RT-LAMP into groups of 6 or less and testing for SARS-CoV-2 RNA by qRT-PCR. We observed a 98.8% concordance between the RT-LAMP and qRT-PCR assays, with only 5 of 421 RT-LAMP negative pools (2,493 samples) testing positive in the more sensitive qRT-PCR assay. Overall, we demonstrate a rapid testing method that can be implemented outside the traditional laboratory setting by individuals with basic molecular biology skills and can effectively identify asymptomatic individuals who would not typically meet the criteria for symptom-based testing modalities. | infectious diseases |
10.1101/2020.07.28.20163899 | Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging | PurposeTo evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging.
Materials and MethodsUsing two large publicly available radiology datasets (SIIM-ACR Pneumothorax Segmentation and RSNA Pneumonia Detection), we quantified the performance of eight commonly used saliency map techniques in regards to their 1) localization utility (segmentation and detection), 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. We compared their performances versus baseline methods and localization network architectures, using area under the precision-recall curve (AUPRC) and structural similarity index (SSIM) as metrics.
ResultsAll eight saliency map techniques fail at least one of the criteria and were inferior in performance compared to localization networks. For pneumothorax segmentation, the AUPRC ranged from 0.024-0.224, while a U-Net achieved a significantly superior AUPRC of 0.404 (p<0.005). For pneumonia detection, the AUPRC ranged from 0.160-0.519, while a RetinaNet achieved a significantly superior AUPRC of 0.596 (p<0.005). Five and two saliency methods (out of eight) failed the model randomization test on the segmentation and detection datasets, respectively, suggesting that these methods are not sensitive to changes in model parameters. The repeatability and reproducibility of the majority of the saliency methods were worse than localization networks for both the segmentation and detection datasets.
ConclusionWe suggest that the use of saliency maps in the high-risk domain of medical imaging warrants additional scrutiny and recommend that detection or segmentation models be used if localization is the desired output of the network.
Supplemental material is available for this article.
SummaryThe use of saliency maps to interpret deep neural networks trained on medical imaging fails several key criteria for utility and robustness, highlighting the need for scrutiny before clinical application.
Key PointsO_LIEight popular saliency map techniques were evaluated for their utility and robustness in interpreting deep neural networks trained on chest radiographs.
C_LIO_LIAll the saliency map techniques fail at least one of the criteria defined in the paper, indicating their use for high-risk medical applications to be problematic.
C_LIO_LIInstead, the use of detection or segmentation models are recommended if localization is the ultimate goal of interpretation.
C_LI | radiology and imaging |
10.1101/2020.07.30.20164855 | Social Learning in a Network Model of Covid-19 | This paper studies the effects of social learning on the transmission of Covid-19 in a network model. We calibrate our model to detailed data for Cape Town, South Africa and show that the inclusion of social learning improves the prediction of excess fatalities, reducing the best-fit squared difference from 19.34 to 11.40. The inclusion of social learning both flattens and shortens the curves for infections, hospitalizations, and excess fatalities, which is qualitatively different from flattening the curve by reducing the contact rate or transmission probability through non-pharmaceutical interventions. While social learning reduces infections, this alone is not sufficient to curb the spread of the virus because learning is slower than the disease spreads. We use our model to study the efficacy of different vaccination strategies and find that vaccinating vulnerable groups first leads to a 72% reduction in fatalities and 5% increase in total infections compared to a random-order benchmark. By contrast, using a contact-based vaccination strategy reduces infections by only 0.9% but results in 42% more fatalities relative to the benchmark. | epidemiology |
10.1101/2020.07.29.20164566 | Predicting and forecasting the impact of local outbreaks of COVID-19: Use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity | BackgroundThe world is at the cusp of experiencing local/regional hot-spots and spikes of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local resurgence and outbreaks to guide the local healthcare demand and capacity, policy making, and public health decisions.
MethodsThe model utilised the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges, and occupancy) from the local NHS hospitals and Covid-19 related weekly deaths in hospitals and other settings in Sussex (population 1-7M), Southeast England. These datasets corresponded to the first wave of COVID-19 infections from 24 March-15 June 2020. The counts of death registrations and regional population estimates were obtained from the Office of National Statistics. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequently validated to make predictions subject to 95% confidence.
FindingsThe inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national datasets. Unlike other predictive models, which are restricted to a couple of days, our model can predict local hospital admissions, discharges (including deaths) and occupancy for the next 10, 20, and 30 days at the local level.
InterpretationWe have demonstrated that by using local/regional data, our predictive and forecasting model can be utilised to guide the local healthcare demand and capacity, policy making, and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organisation of services. Primary care and community services can be guided by the projected number of infectious and recovered patients and hospital admissions/discharges to project discharge pathways to bedded and community settings, thus allowing services to understand their likely load in future spikes/waves. The flexibility of timings in the model, in combination with other early warning systems, produces a timeframe for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities, and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impact of COVID-19 transmission.
FundingThis study was supported by the Higher Education Innovation Fund through the University of Sussex (ECF, JVY, AMa). This work was partly supported by the Global Challenges Research Fund through the Engineering and Physical Sciences Research Council grant number EP/T00410X/1: UK-Africa Postgraduate Advanced Study Institute in Mathematical Sciences (AMa, ECF). ECF is supported by the Wellcome Trust grant number 204833/Z/16/Z.
Research in context Evidence before this studySince the beginning of the COVID-19 pandemic, healthcare managers and policy makers relied on epidemiological models based on national datasets to predict and mitigate the spread of the disease. The performance of these models has not always been validated against the available data, and they depend strongly on the values for the model parameters. Statistical models, e.g. those arising from time-series analysis, lack the temporal dynamics of the compartmentalised epidemiological model for the evolution of the disease and thus fail to capture the evolution far into the future with great accuracy. Compartmental models, on the other hand, capture the underlying dynamics of an infectious disease but typically use parameters estimated using datasets from other regions or countries, thus lacking the ability to capture local demographics and policy and therefore lack predicting local dynamics with accuracy.
Added value of this studyAlthough our compartmental model follows standard SEIR-D model structure, the inference algorithm described and applied in this report is novel, along with the prediction technique used to validate the model. We checked bioRxiv, medRxiv, and arXiv up to the end of August 2020 using the terms "mathematical inference", "COVID-19", and "SIR" and found that there is a substantial use of Bayesian approaches to fit parameters but none that use the combination of statistical approaches with compartmental models, hence the originality of our work. We designed a compartmentalised epidemiological model that captures the basic dynamics of the COVID-19 pandemic and revolves around the data that are available at the local/regional level. We estimated all the parameters in the model using the local surveillance data, and in consequence, our parameters reflect the characteristics of the local population. Furthermore, we validated the predictive power of the model by using only a subset of the available data to fit the parameters. To the best of our knowledge, this is the first study which combines statistical approaches with a compartmental model and as such benefits greatly from the ability to predict and forecast much further into the future using the dynamical structure of the compartmental model with a relatively much higher accuracy than previously presented in the literature. This research sets the gold-standard benchmark by laying the framework for future adaptations to the model when more precise (and comprehensive) datasets are made available.
Implications of all the available evidenceThe predictive power of our model outperforms previously available models for local forecasting of the impact of COVID-19. Using local models, rather than trying to use national models at a local scale, ensures that the model reflects the local demographics and provides reliable local-data-driven predictions to guide the local healthcare demand and capacity, policy making, and public health decisions to mitigate the impact of COVID-19 on the local population. Local authorities can use these results for the planning of local hospital demand as well as death management services by developing scenario-based analysis to which different values of the reproduction number R exiting a COVID-19 lockdown are assumed and results, such as maximum hospital occupancy, are compared to the first wave to establish a potential strain on resources. This can work as an early warning detection system to see what value of R that is currently followed, which in turn informs the relevant capacity and resources needed to mitigate the impact of COVID-19. The Web toolkit developed by us as a result of this study (https://alpha.halogen-health.org) demonstrates the predictive power of our model as well as its flexibility with the scenario-based analysis. Although our model is based on the data from Sussex, using similar variables/data from other regions in our model would derive respective COVID-19 model parameters, and thus enable similar scenario-based investigations to predict and forecast the impact of local resurgence to guide the local healthcare demand and capacity, policy making, and public health decisions. | epidemiology |
10.1101/2020.07.30.20164954 | Access to HIV-prevention in female sex workers in Ukraine between 2009 and 2017: coverage, barriers and facilitators | The provision of comprehensive prevention services is vital for reducing the high burden of HIV amongst Ukrainian female sex workers (FSWs). To identify barriers and facilitators that influence access to HIV prevention amongst this population between 2009 and 2017, we developed a literature-informed conceptual framework and conducted a document analysis to identify the components of the Ukrainian prevention package (PP). Using the Integrated Bio Behavioural Surveillance Surveys, we then conducted descriptive analyses to explore PP coverage from 2009 to 2017 and the influence of factors, identified by our conceptual framework. After increasing over four years, a drop in PP coverage was observed from 2013 onwards. Being a client of a non-governmental organisation, street and highway solicitation, non-condom use, and knowledge of HIV may influence access to HIV prevention in the Ukrainian context. Future interventions should consider barriers and facilitators to HIV prevention and the multiple structural levels on which they operate. | public and global health |
10.1101/2020.07.30.20164962 | Links between gut microbiome composition and fatty liver disease in a large population sample | Fatty liver disease is the most common liver disease in the world. It is characterized by a buildup of excess fat in the liver that can lead to cirrhosis and liver failure. The link between fatty liver disease and gut microbiome has been known for at least 80 years. However, this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample in Finland. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI ([≥] 60, indicates likely liver steatosis) and low FLI (< 60) in our validation set, consisting of 30% of the data not used in model training, with an average AUC of 0.75. In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Pathway analysis of representative genomes of the FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of e.g., ethanol fermentation pathways. Through modeling the fatty liver index, our results provide with high resolution associations between gut microbiota composition and fatty liver in a large representative population cohort and support the role of endogenous ethanol producers in the development of fatty liver. | gastroenterology |
10.1101/2020.07.30.20068114 | A multicentric, randomized, controlled phase III study of centhaquine (Lyfaquin(R)) as a resuscitative agent in hypovolemic shock patients | INTRODUCTIONCenthaquine (Lyfaquin(R)) showed significant safety and efficacy in preclinical and clinical phase I and II studies.
METHODSA prospective, multicentric, randomized phase III study was conducted in patients with hypovolemic shock having systolic blood pressure (SBP) of [≤]90 mm Hg and blood lactate levels of [≥]2 mmol/L. Patients were randomized in a 2:1 ratio, 71 patients to the centhaquine group and 34 patients to the control (saline) group. Every patient received standard of care (SOC) and was followed for 28 days. The study drug (normal saline or centhaquine (0.01 mg/kg)) was administered in 100 mL of normal saline infusion over 1 hour. The primary objectives were to determine changes (mean through 48 hours) in SBP, diastolic blood pressure (DBP), blood lactate levels, and base deficit. The secondary objectives included the amount of fluids, blood products, vasopressors administered in the first 48 hours, duration of hospital stay, time in ICU, time on the ventilator support, change in patients Acute Respiratory Distress Syndrome (ARDS), Multiple Organ Dysfunction Syndrome (MODS) scores, and the proportion of patients with 28-day all-cause mortality.
RESULTSThe demographics of patients and baseline vitals in both groups were comparable. Trauma was the cause of hypovolemic shock in 29.41% of control and 47.06% of centhaquine, gastroenteritis in 44.12% of control, and 29.41% of centhaquine patients. An equal amount of fluids and blood products were administered in both groups during the first 48 hours of resuscitation. A lesser amount of vasopressors was needed in the first 48 hours of resuscitation in the centhaquine group. An increase in SBP from the baseline was consistently higher in the centhaquine group than in the control. A significant increase in pulse pressure in the centhaquine group than the control group suggests improved stroke volume due to centhaquine. The shock index was significantly lower in the centhaquine group than control from 1 hour (p=0.0320) till 4 hours (p=0.0494) of resuscitation. Resuscitation with centhaquine had a significantly greater number of patients with improved blood lactate and the base deficit than the control group. ARDS and MODS improved with centhaquine, and an 8.8% absolute reduction in 28-day all-cause mortality was observed in the centhaquine group.
CONCLUSIONCenthaquine is a highly efficacious resuscitative agent for treating hypovolemic shock. The efficacy of centhaquine in distributive shock due to sepsis and COVID-19 is being explored.
Trial RegistrationClinical Trials Registry, India; ctri.icmr.org.in, CTRI/2019/01/017196; clinicaltrials.gov, NCT04045327.
Key Summary PointsO_LIA multicentric, randomized, controlled trial was conducted to evaluate the efficacy of centhaquine in hypovolemic shock patients.
C_LIO_LIOne hundred and five patients were randomized 2:1 to receive centhaquine or saline. Centhaquine was administered at a dose of 0.01 mg/kg in 100 mL saline and infused over 1 hour. The control group received 100 mL of saline over a 1-hour infusion.
C_LIO_LICenthaquine improved blood pressure, shock index, reduced blood lactate levels, and improved base deficit. Acute Respiratory Distress Syndrome (ARDS) and Multiple Organ Dysfunction Syndrome (MODS) score improved with centhaquine.
C_LIO_LIAn 8.8% absolute reduction in 28-day all-cause mortality was observed in the centhaquine group. There were no drug-related adverse events in the study.
C_LI | intensive care and critical care medicine |
10.1101/2020.07.31.20154070 | False-Negative Mitigation in Group Testing for COVID-19 Screening | After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a groups false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the 2-stage sequential version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the groupMix method is publicly available for laboratories to implement this method. | infectious diseases |
10.1101/2020.08.01.20165142 | Computing the daily reproduction number of COVID-19 by inverting the renewal equation | The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures, or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt. But it estimates Rt with a significant temporal delay. Here, we propose a new method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it. Our signal processing approach to this problem yields both Rt and a restored it corrected for the "weekend effect" by applying a deconvolution + denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open-source and can be run in real-time on every country in the world, and every US state through a web interface at www.ipol.im/epiinvert.
Significance StatementBased on a signal processing approach we propose a method to compute the reproduction number Rt, the transmission potential of an epidemic over time. Rt is estimated by minimizing a functional that enforces: (i) the ability to produce an incidence curve it corrected of the weekly periodic bias produced by the "weekend effect", obtained from Rt through a renewal equation; (ii) the regularity of Rt. A good agreement is found between our Rt estimate and the one provided by the currently accepted method, EpiEstim, except our method predicts Rt several days closer to present. We provide the mathematical arguments for this shift. Both methods, applied every day on each country, can be compared at www.ipol.im/epiinvert. | epidemiology |
10.1101/2020.08.01.20165142 | Computing the daily reproduction number of COVID-19 by inverting the renewal equation | The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures, or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt. But it estimates Rt with a significant temporal delay. Here, we propose a new method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it. Our signal processing approach to this problem yields both Rt and a restored it corrected for the "weekend effect" by applying a deconvolution + denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open-source and can be run in real-time on every country in the world, and every US state through a web interface at www.ipol.im/epiinvert.
Significance StatementBased on a signal processing approach we propose a method to compute the reproduction number Rt, the transmission potential of an epidemic over time. Rt is estimated by minimizing a functional that enforces: (i) the ability to produce an incidence curve it corrected of the weekly periodic bias produced by the "weekend effect", obtained from Rt through a renewal equation; (ii) the regularity of Rt. A good agreement is found between our Rt estimate and the one provided by the currently accepted method, EpiEstim, except our method predicts Rt several days closer to present. We provide the mathematical arguments for this shift. Both methods, applied every day on each country, can be compared at www.ipol.im/epiinvert. | epidemiology |
10.1101/2020.08.01.20165142 | Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique | The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures, or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt. But it estimates Rt with a significant temporal delay. Here, we propose a new method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it. Our signal processing approach to this problem yields both Rt and a restored it corrected for the "weekend effect" by applying a deconvolution + denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open-source and can be run in real-time on every country in the world, and every US state through a web interface at www.ipol.im/epiinvert.
Significance StatementBased on a signal processing approach we propose a method to compute the reproduction number Rt, the transmission potential of an epidemic over time. Rt is estimated by minimizing a functional that enforces: (i) the ability to produce an incidence curve it corrected of the weekly periodic bias produced by the "weekend effect", obtained from Rt through a renewal equation; (ii) the regularity of Rt. A good agreement is found between our Rt estimate and the one provided by the currently accepted method, EpiEstim, except our method predicts Rt several days closer to present. We provide the mathematical arguments for this shift. Both methods, applied every day on each country, can be compared at www.ipol.im/epiinvert. | epidemiology |
10.1101/2020.07.31.20166348 | Containing the Spread of Infectious Disease on College Campuses | College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model allows for stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. However, one needs to be careful about the assumptions implicit in the model to ensure that the estimated parameters have a reasonable interpretation. This modeling approach could be applied to data from other outbreaks in college campuses and similar small-population settings. | epidemiology |
10.1101/2020.07.31.20166348 | Containing the Spread of Infectious Disease on College Campuses | College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model allows for stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. However, one needs to be careful about the assumptions implicit in the model to ensure that the estimated parameters have a reasonable interpretation. This modeling approach could be applied to data from other outbreaks in college campuses and similar small-population settings. | epidemiology |
10.1101/2020.07.31.20166116 | Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials | AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSTo examine the effect of face mask intervention in respiratory infections across different exposure settings and age groups.
DesignSystematic review and meta-analysis.
Data sourcesPubMed, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials investigating the effect of face masks on respiratory infections published by November 18th, 2020. We followed PRISMA guidelines.
Eligibility criteria for selecting studiesRandomized controlled trials investigating face masks in respiratory infections across different exposure settings. Two reviewers performed the search, extracted data, and assessed the risk of bias. Random effects meta-analysis with risk ratio, adjusted odds ratios, and number needed to treat were performed. Findings by source control or wearer protection, age groups, exposure settings, and role of non-compliance were evaluated.
ResultsSeventeen studies were included, (N=11,601 cases and N=10,286 controls, follow-up from 4 days to 19 months). Fourteen trials included adults and children and three trials included children only. Twelve studies showed non-compliance in treatment and eleven in control group. Four studies supported the use of face masks. Meta-analysis across all studies with risk ratios found no association with number of infections (RR=0.957 [0.810 - 1.131], p=0.608). Meta-analysis using odds ratios adjusted for age, sex, and vaccination (when available) showed protective effect of face masks (OR=0.850 [0.736 - 0.982], p=0.027). Subgroup meta-analysis with adjusted odds ratios found a decrease in respiratory infections among adults (14 studies, OR = 0.829 [0.709 - 0.969], p=0.019) in source control setting (OR = 0.845 [0.7375 - 0.969], p=0.0159) and when face masks were used together with hand hygiene OR = 0.690 [0.568 - 0.838], p=0.0002). Overall between-study heterogeneity was large also in the subgroup analyses.
ConclusionDespite the large between study heterogeneity, compliance bias and differences by environmental settings, the findings support the use of face masks to prevent respiratory infections. PROSPERO registration number CRD42020205523. | epidemiology |
10.1101/2020.08.02.20159418 | Timely Epidemic Monitoring in the Presence of Reporting Delays: Anticipating the COVID-19 Surge in New York City, September 2020 | During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response. We describe a nonparametric statistical method, originally applied to the reporting of AIDS cases in the 1980s, to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City during the late summer and early fall of 2020. During August 15 - September 26, the estimated mean delay in reporting was 3.3 days, with 87 percent of cases reported by 5 days from diagnosis. Relying upon the estimated reporting-delay distribution, we projected COVID-19 incidence during the most recent three weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. Applying our delay-corrected estimates to case counts reported as of September 26, we projected a surge in new diagnoses that had already occurred but had yet to be reported. Our projections were consistent with counts of confirmed cases subsequently reported by November 7. The resulting estimate of recently diagnosed cases could have had an impact on timely policy decisions to tighten social distancing measures. While the recent advent of widespread rapid antigen testing has changed the diagnostic testing landscape considerably, delays in public reporting of SARS-CoV-2 case counts remain an important barrier to effective public health policy. | epidemiology |
10.1101/2020.08.02.20165092 | Prediction of type 2 diabetes mellitus onset using logistic regression-based scoreboards | Type 2 diabetes mellitus (T2DM) accounts for [~]90% of all cases of diabetes which are estimated with an annual world death rate of 1.6 million in 2016. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate yet accessible prediction models based on non-laboratory parameters. This paper introduces one non-laboratory model which is highly accessible to the general population and one highly precise yet simple laboratory model. Both models are provided as an accessible scoreboard form and also as a logistic regression model. We based the models on data from 44,879 non-diabetic, UK Biobank participants, aged 40-65, predicting the risk of T2D onset within the next 7.3 years (SD 2.3). The non-laboratory prediction model for T2DM onset probability incorporated the following covariates: sex, age, weight, height, waist, hips-circumferences, waist-to-hip Ratio (WHR) and Body-Mass Index (BMI). This logistic regression model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.85 95% CI) and an odds ratio (OR) between the upper and lower prevalence deciles of x77 (28-98). We further analysed the contribution of laboratory-based parameters and devised a blood-test model based on just five blood tests. In this model, we included age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset. This logistic-regression model achieved an auROC of 0.89 (0.86-0.91) and a deciles OR of x87 (27-152). Using the scoreboard results, the Anthropometrics model classified three risk groups, a group with 1%(1-2%); a group with 9% (7-11%) probability, and a group with a 15% (7-23%) risk of developing T2D. The Five blood tests scoreboard model, further classified into four risk groups: 0.9% (0.7%-1%); 8%(6-11%); 18%(14-22%) and a high risk group of 38%(23-54%) of developing T2D. We analysed several more comprehensive models which included genotyping data and other environmental factors and found that it did not provide cost efficient benefits over the five blood tests model. The Five blood tests and anthropometric models, both in their logistic regression form and scoreboard form, outperform the commonly used non-laboratory models, the Finnish Diabetes Risk Score (FINDRISC) and the German Diabetes Risk Score (GDRS). When trained using our data, the FINDRISC achieved an auROC of 0.75 (0.71-0.78), and the GDRS auROC resulted in 0.58 (0.54-0.62), respectively. | endocrinology |
10.1101/2020.08.04.20163782 | Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number | The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R < 1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks.
Here, focusing on the dynamics of the first-wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence. | infectious diseases |
10.1101/2020.08.04.20168112 | Seroprevalence of SARS-CoV-2 in Niger State: A Pilot Cross Sectional Study | BackgroundCoronavirus Disease 2019 (COVID-19) Pandemic caused by SARS-CoV-2 is ongoing causing human and socioeconomic losses.
ObjectiveTo know how far the virus has spread in Niger State, Nigeria, a pilot study was carried out to determine the SARS-CoV-2 seroprevalence, patterns, dynamics, and risk factors in the state.
MethodsA cross sectional study design and Clustered-Stratified-Random sampling strategy were used to select 185 test participants across the state. SARS-CoV-2 IgG and IgM Rapid Test Kits (Colloidal gold immunochromatography lateral flow system) were used to determine the presence or absence of the antibodies to the virus in the blood of sampled participants across Niger State as from 26th June 2020 to 30th June 2020. The test kits were validated using the blood samples of some of the Nigeria Center for Disease Control (NCDC) confirmed positive and negative COVID-19 cases in the State. SARS-CoV-2 IgG and IgM Test results were entered into the EPIINFO questionnaire administered simultaneously with each test. EPIINFO was then used for to calculate arithmetic mean and percentage, odd ratio, chi-square, and regression at 95% Confidence Interval of the data generated.
ResultsThe seroprevalence of SARS-CoV-2 in Niger State was found to be 25.41% and 2.16% for the positive IgG and IgM respectively. Seroprevalence among age groups, gender and by occupation varied widely. COVID-19 asymptomatic rate in the state was found to be 46.81%. The risk analyses showed that the chances of infection are almost the same for both urban and rural dwellers in the state. However, health care workers, those that experienced flu-like symptoms and those that have had contact with person (s) that travelled out of Nigeria in the last six (6) months (February -June 2020) are twice (2 times) at risk of being infected with the virus. More than half (54.59%) of the participants in this study did not practice social distancing at any time since the pandemic started. Discussions about knowledge, practice and attitude of the participants are included.
ConclusionThe observed Niger State SARS-CoV-2 seroprevalence and infection patterns means that the virus is widely spread, far more SARS CoV-2 infections occurred than the reported cases and high asymptomatic COVID-19 across the state. | epidemiology |
10.1101/2020.08.03.20155671 | Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts | BackgroundNeurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed and the relationships between androgen exposure and behavior remain unclear.
MethodsHere, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N=763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores.
ResultsWe found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all p < 0.05), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ({rho} = 0.19, p = 0.004;{rho} = 0.2, p = 0.004, respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels (t = 4.0, p = 8.8 x 10-5), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels (t = -3.3, p = 0.001). Finally, using the SPARK cohort (N=9,419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning (t = -2.3, p = 0.02, and t = 4.2, p = 3.2 x 10-5 for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: -0.22 {+/-} 0.05; testosterone: -0.35 {+/-} 0.15 from 0.1%-ile to 99.9%-ile; SHBG: 0.64 {+/-} 0.15 from 0.1%-ile to 99.9%-ile).
ConclusionsThese findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females. | psychiatry and clinical psychology |
10.1101/2020.08.03.20155671 | Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts | BackgroundNeurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed and the relationships between androgen exposure and behavior remain unclear.
MethodsHere, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N=763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores.
ResultsWe found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all p < 0.05), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ({rho} = 0.19, p = 0.004;{rho} = 0.2, p = 0.004, respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels (t = 4.0, p = 8.8 x 10-5), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels (t = -3.3, p = 0.001). Finally, using the SPARK cohort (N=9,419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning (t = -2.3, p = 0.02, and t = 4.2, p = 3.2 x 10-5 for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: -0.22 {+/-} 0.05; testosterone: -0.35 {+/-} 0.15 from 0.1%-ile to 99.9%-ile; SHBG: 0.64 {+/-} 0.15 from 0.1%-ile to 99.9%-ile).
ConclusionsThese findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females. | psychiatry and clinical psychology |
10.1101/2020.08.05.20169086 | An Examination of School Reopening Strategies during the SARS-CoV-2 Pandemic | The SARS-CoV-2 pandemic led to closure of nearly all K-12 schools in the United States of America in March 2020. Although reopening K-12 schools for in-person schooling is desirable for many reasons, officials understand that risk reduction strategies and detection of cases are imperative in creating a safe return to school. Furthermore, consequences of reclosing recently opened schools are substantial and impact teachers, parents, and ultimately educational experiences in children. To address competing interests in meeting educational needs with public safety, we compare the impact of physical separation through school cohorts on SARS-CoV-2 infections against policies acting at the level of individual contacts within classrooms. Using an age-stratified Susceptible-Exposed-Infected-Removed model, we explore influences of reduced class density, transmission mitigation, and viral detection on cumulative prevalence. We consider several scenarios over a 6-month period including (1) multiple rotating cohorts in which students cycle through in-person instruction on a weekly basis, (2) parallel cohorts with in-person and remote learning tracks, (3) the impact of a hypothetical testing program with ideal and imperfect detection, and (4) varying levels of aggregate transmission reduction. Our mathematical model predicts that reducing the number of contacts through cohorts produces a larger effect than diminishing transmission rates per contact. Specifically, the latter approach requires dramatic reduction in transmission rates in order to achieve a comparable effect in minimizing infections over time. Further, our model indicates that surveillance programs using less sensitive tests may be adequate in monitoring infections within a school community by both keeping infections low and allowing for a longer period of instruction. Lastly, we underscore the importance of factoring infection prevalence in deciding when a local outbreak of infection is serious enough to require reverting to remote learning. | epidemiology |
10.1101/2020.08.05.20169151 | Time-to-event estimation of birth year prevalence trends: a method to enable investigating the etiology of childhood disorders including autism | An unbiased, widely accepted estimate of the rate of occurrence of new cases of autism over time would facilitate progress in understanding the causes of autism. The same may also apply to other disorders. While incidence is a widely used measure of occurrence, birth prevalence--the proportion of each birth year cohort with the disorder--is the appropriate measure for disorders and diseases of early childhood. Studies of autism epidemiology commonly speculate that estimates showing strong increases in rate of autism cases result from an increase in diagnosis rates rather than a true increase in cases. Unfortunately, current methods are not sufficient to provide a definitive resolution to this controversy. Prominent experts have written that it is virtually impossible to solve.
This paper presents a novel method, time-to-event birth prevalence estimation (TTEPE), to provide accurate estimates of birth prevalence properly adjusted for changing diagnostic factors. It addresses the shortcomings of prior methods. TTEPE is based on well-known time-to-event (survival) analysis techniques. A discrete survival process models the rates of incident diagnoses by birth year and age. Diagnostic factors drive the probability of diagnosis as a function of the year of diagnosis. TTEPE models changes in diagnostic criteria, which can modify the effective birth prevalence when new criteria take effect. TTEPE incorporates the development of diagnosable symptoms with age. General-purpose optimization software estimates all parameters, forming a non-linear regression. The paper specifies all assumptions underlying the analysis and explores potential deviations from assumptions and optional additional analyses.
A simulation study shows that TTEPE produces accurate parameter estimates, including trends in both birth prevalence and the probability of diagnosis in the presence of sampling effects from finite populations. TTEPE provides high power to resolve small differences in parameter values by utilizing all available data points. | epidemiology |
10.1101/2020.08.05.20169151 | Time-to-event estimation of birth year prevalence trends: a method to enable investigating the etiology of childhood disorders including autism | An unbiased, widely accepted estimate of the rate of occurrence of new cases of autism over time would facilitate progress in understanding the causes of autism. The same may also apply to other disorders. While incidence is a widely used measure of occurrence, birth prevalence--the proportion of each birth year cohort with the disorder--is the appropriate measure for disorders and diseases of early childhood. Studies of autism epidemiology commonly speculate that estimates showing strong increases in rate of autism cases result from an increase in diagnosis rates rather than a true increase in cases. Unfortunately, current methods are not sufficient to provide a definitive resolution to this controversy. Prominent experts have written that it is virtually impossible to solve.
This paper presents a novel method, time-to-event birth prevalence estimation (TTEPE), to provide accurate estimates of birth prevalence properly adjusted for changing diagnostic factors. It addresses the shortcomings of prior methods. TTEPE is based on well-known time-to-event (survival) analysis techniques. A discrete survival process models the rates of incident diagnoses by birth year and age. Diagnostic factors drive the probability of diagnosis as a function of the year of diagnosis. TTEPE models changes in diagnostic criteria, which can modify the effective birth prevalence when new criteria take effect. TTEPE incorporates the development of diagnosable symptoms with age. General-purpose optimization software estimates all parameters, forming a non-linear regression. The paper specifies all assumptions underlying the analysis and explores potential deviations from assumptions and optional additional analyses.
A simulation study shows that TTEPE produces accurate parameter estimates, including trends in both birth prevalence and the probability of diagnosis in the presence of sampling effects from finite populations. TTEPE provides high power to resolve small differences in parameter values by utilizing all available data points. | epidemiology |
10.1101/2020.08.04.20168518 | Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations | BackgroundNew Zealand, Australia, Iceland, and Taiwan all saw success at controlling the first wave of the COVID-19 pandemic. As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19.
MethodsWe employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of SARS-CoV-2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contract tracing strategies.
FindingsWe estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic, and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country, and that introductions slowed down markedly following the reduction of international travel in mid March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data.
InterpretationWe have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics, and for contact tracing.
FundingThis research was funded by the Health Research Council of New Zealand, the Ministry of Business, Innovation, and Employment, the Royal Society of New Zealand, and the New Zealand Ministry of Health.
Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSOur study looks at the early months of the COVID-19 pandemic, a period in which the first wave was controlled in four "island" nations - New Zealand, Australia, Taiwan, and Iceland. All prior data used in this study was collected from late 2019 until the end of April 2020. This includes over 3000 SARS-CoV-2 genomic sequences which were collected in this period (and subsequently deposited into GISAID), as well as arrival and departure information (provided by official statistics from each country), human mobility data collected from mobile phones (by Apple), and COVID-19 case data (released by the World Health Organisation). Even early on during the COVID-19 pandemic, the properties of SARS-CoV-2 - including the reproduction number and mutation rate - were well characterised, and a range of these estimates have been covered in our article. Our Bayesian phylodynamic models, including their prior distributions, are informed by all of the above sources of information. Finally, we have incorporated all of the available information on COVID-19 transmission clusters identified by the New Zealand Ministry of Health during this period.
Added value of this studyWe quantified the decline in the reproduction number of SARS-CoV-2, following the decline in human mobility, in four "island" countries. We also demonstrated how importation events of SARS-CoV-2 into each considered country declined markedly following the reduction of international travel. Our results shed a different light on these patterns because of (i) our locations of choice - the four countries had success in dealing with the first pandemic wave, with their geographic isolation contributing to cleaner signals of human mobility, and (ii) our novel and empirically driven phylodynamic model, which we built from explicitly modelling mobile phone data in the four islands. Furthermore, by crossing epidemiological against ge3nomic data, our paper quantitatively assesses the ability of contact tracing, as implemented by the New Zealand Ministry of Health (NZMH), in identifying COVID-19 transmission clusters. We find evidence for a high efficacy of the specific measures taken - and when they were taken - by the NZMH in identifying transmission clusters, considered worldwide to have been successful in its response to the pandemic. Our analyses also illustrate the power of viral genomic data in assisting contact tracing.
Implications of all the available evidenceThe conclusions drawn from this research inform effective policy for locations pursuing an elimination strategy. We confirm the accuracy of standard contact tracing methods at identifying clusters and show how these methods are improved using genomic data. We demonstrate how the overseas introduction rates and domestic transmission rates of an infectious viral agent can be surveilled using genomic data, and the important role each plays in overall transmission. Specifically, we have quantified these processes for four countries and have shown that they did decline significantly following declines in human travel and mobility. The phylodynamic methods used in this work is shown to be robust and applicable to a range of scenarios where appropriate subsampling is used. | epidemiology |
10.1101/2020.08.04.20167874 | Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing | The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1, 2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens. | infectious diseases |
10.1101/2020.08.04.20167874 | Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing | The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1, 2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens. | infectious diseases |
10.1101/2020.08.06.20164848 | The innate and adaptive immune landscape of SARS-CoV-2-associated Multisystem Inflammatory Syndrome in Children (MIS-C) from acute disease to recovery. | Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening disease occurring several weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. MIS-C has overlapping clinical features with Kawasaki Disease (KD), a rare childhood vasculitis. MIS-C therapy is largely based on KD treatment protocols but whether these diseases share underpinning immunological perturbations is unknown. We performed deep immune profiling on blood samples from healthy children and patients with MIS-C or KD. Acute MIS-C patients had highly activated neutrophils, classical monocytes and memory CD8+ T-cells; increased frequencies of B-cell plasmablasts and CD27-IgD-double-negative B-cells; and increased levels of pro-inflammatory (IL6, IL18, IP10, MCP1) but also anti-inflammatory (IL-10, IL1-RA, sTNFR1, sTNFR2) cytokines. Increased neutrophil count correlated with inflammation,cardiac dysfunction and disease severity. Two days after intravenous immunoglobulin (IVIG) treatment, MIS-C patients had increased CD163 expression on monocytes, expansion of a novel population of immature neutrophils, and decreased levels of pro- and anti-inflammatory cytokines in the blood accompanied by a transient increase in arginase in some patients. Our data show MIS-C and KD share substantial immunopathology and identify potential new mechanisms of action for IVIG, a widely used anti-inflammatory drug used to treat MIS-C, KD and other inflammatory diseases. | pediatrics |
10.1101/2020.08.06.20169672 | Automated measurement of the foveal avascular zone in healthy eyes on Heidelberg Spectralis Optical Coherence Tomography Angiography | PurposeTo develop and evaluate an automated method to measure the foveal avascular zone (FAZ) area in healthy eyes on Heidelberg Spectralis Optical Coherence Tomography Angiography (HS-OCTA). This method is referred to as the modified Kanno-Saitama macro (mKSM) and it is an evolution of the original Kanno-Saitama macro (KSM) approach.
MethodsThis cross-sectional study included 29 eyes of 25 healthy volunteers who underwent HS-OCTA at the macular area twice at the same time. Regardless of the quality of the images, all of them were included. Macular data on the superficial vascular plexus, intermediate capillary plexus and deep capillary plexus were processed by mKSM. The FAZ area was measured twice automatically using the mKSM and KSM and twice manually by two independent examiners.
ResultsFrom 174 images, KSM could not measure correctly 31% while mKSM could successfully measure all of them. Intrascan intraclass coefficient ranged from 0,948 to 0,993 for manual measurements and was 1 for mKSM method, which means that mKSM FAZ area value is always the same for the same OCTA image. Despite that the difference between human examiners is smaller than between human examiners and mKSM according to Bland-Altman plots, the scatterplots show a strong correlation between human and automatic measurements. The best results are obtained in intermediate capillary plexus.
ConclusionsWith mKSM, the automated determination of the FAZ area in HS-OCTA is feasible and less human-dependent. It solves the inability of KSM to measure the FAZ area in suboptimal quality images which are frequent in daily clinical practice. Therefore, the mKSM processing could contribute to our understanding of the three vascular plexuses. | ophthalmology |
10.1101/2020.08.06.20169334 | The effect of a specialist paramedic primary care rotation on appropriate non-conveyance decisions (SPRAINED) study: a controlled interrupted time series analysis | IntroductionNHS ambulance service conveyance rates in the UK are almost 70%, despite an increase in non-emergency cases. This is increasing the demands on crowded emergency departments (ED) and contributes to increased ambulance turnaround times. Yorkshire Ambulance Service introduced a specialist paramedic (SP) role to try and address this, but non-conveyance rates in this group have not been as high as expected.
MethodsWe conducted a controlled interrupted time series analysis using data from incidents between June 2017 and December 2019, to study appropriate non-conveyance rates before and after a GP placement. A costing analysis examined the average cost per appropriate non-conveyance achieved for patients receiving care from intervention group SPs pre- and post-placement was also conducted.
Results7349 incidents attended by intervention group SPs were eligible for inclusion. Following removal of cases with missing data, 5537/7349 (75.3%) cases remained. Post-placement, the intervention group demonstrated an increase in appropriate non-conveyance rate of 35.0% (95%CI 23.8-46.2%, p<0.001), and a reduction in the trend of appropriate non-conveyance of -1.2% (95%CI -2.8-0.5%, p=0.156), relative to the control group.
Post-placement, the cost per appropriate non-conveyance for intervention group SPs was a mean of {pound}509.38 (95% bootstrapped CI {pound}455.32-{pound}564.59) versus {pound}1258.04 (95% bootstrapped CI {pound}1232.64-{pound}1284.04) for the same group in the pre-placement phase. This represents a mean saving of {pound}748.66 per appropriate non-conveyance (95% bootstrapped CI {pound}719.45-{pound}777.32) and a cost-effectiveness ratio of {pound}2141.15 per percentage increase in appropriate non-conveyance (95% bootstrapped CI {pound}2057.62-{pound}2223.12).
ConclusionIn this single UK NHS ambulance service study, we found a clinically important and statistically significant increase in appropriate non-conveyance rates by specialist paramedics who had completed a 10-week GP placement. This improvement persisted for the 12-month period following the placement and demonstrated cost savings compared to usual care.
What this study addsO_ST_ABSWhat is known about this subjectC_ST_ABSO_LIUK ambulance service conveyance rates are almost 70% despite an increase in the number of non-emergency cases
C_LIO_LIHealth Education England funded a pilot in 2018 to rotate paramedics into a range of health settings to improve patient care and relieve pressures on primary care services.
C_LI
What this study addsO_LIClinically important and statistically significant increases in appropriate non-conveyance rates can be achieved by specialist paramedics who complete a 10-week primary care placement.
C_LIO_LIThis improvement is sustained for at least 12-months following the placement cost-effective.
C_LI | emergency medicine |
10.1101/2020.08.05.20160499 | COVID-19 and Mental Health: A Study of its Impact on Students | The purpose of this study was to identify and analyze the personal, social and psychological impact of COVID -19 on the mental health of students of age group 16 to 25. A response from N= 351 students (from the most affected state in India), provided a comparative analysis based on the gender, and background via t-test with significance factor of p[≤]0.5, to understand the pattern in issues related to mental health during the pandemic. The results show that female students are more concerned about health, and future, and are more prone to psychological issues like feelings of uncertainty, helplessness and outbursts than male students. Urban students population is more mentally affected than their rural counterparts, however time spent on the internet is almost the same despite the difference in infrastructure and resources. Also, there is an increase in need for solitude, being withdrawn and self-harm in male students require attention. A shift in perception from seeing family as a source of support to that of a restriction is indicated, although the benefits of a collectivistic society are undisputed. The results indicate that there is overall increased awareness about mental health among the student population and with programs/strategies focusing on background and gender, a significant improvement is attainable.
Impact StatementThis study performs an analysis of the students response to questions based on social and self-perception as a result of COVID-19. It also discusses the nature of adaptive strategies espoused by them and their effectiveness in dealing with the pandemic, isolation, and the new normal.
FundingNot applicable
Conflicts of interest/Competing interestsThere was no conflict of interest | health informatics |
10.1101/2020.08.05.20160499 | COVID-19 and Mental Health: A Study of its Impact on Students | The purpose of this study was to identify and analyze the personal, social and psychological impact of COVID -19 on the mental health of students of age group 16 to 25. A response from N= 351 students (from the most affected state in India), provided a comparative analysis based on the gender, and background via t-test with significance factor of p[≤]0.5, to understand the pattern in issues related to mental health during the pandemic. The results show that female students are more concerned about health, and future, and are more prone to psychological issues like feelings of uncertainty, helplessness and outbursts than male students. Urban students population is more mentally affected than their rural counterparts, however time spent on the internet is almost the same despite the difference in infrastructure and resources. Also, there is an increase in need for solitude, being withdrawn and self-harm in male students require attention. A shift in perception from seeing family as a source of support to that of a restriction is indicated, although the benefits of a collectivistic society are undisputed. The results indicate that there is overall increased awareness about mental health among the student population and with programs/strategies focusing on background and gender, a significant improvement is attainable.
Impact StatementThis study performs an analysis of the students response to questions based on social and self-perception as a result of COVID-19. It also discusses the nature of adaptive strategies espoused by them and their effectiveness in dealing with the pandemic, isolation, and the new normal.
FundingNot applicable
Conflicts of interest/Competing interestsThere was no conflict of interest | health informatics |
10.1101/2020.08.06.20169300 | Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis | BackgroundAmyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the diseases complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cells physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification.
MethodsWe analyzed plasma-derived EVs of ALS patients (n= 106) and controls (n=96), and SOD1G93A and TDP-43Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis.
ResultsOur procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. There were more particles in the plasma of ALS patients and the two mouse models of ALS while their average diameter was smaller. HSP90 was differentially represented in ALS patients and mice compared to the controls. In terms of disease progression, the levels of cyclophilin A, with the EV size distribution, distinguished fast and slow disease progressors, suggesting a new means for patient stratification. We also measured the levels of phosphorylated TDP-43 and showed that is not an intravesicular cargo of plasma-derived EVs.
ConclusionsOur analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis. | neurology |
10.1101/2020.08.07.20139261 | Genome-Wide Association Identifies the First Risk Loci for Psychosis in Alzheimer Disease | Psychotic symptoms, defined as the occurrence of delusions or hallucinations, are frequent in Alzheimer disease (AD with psychosis, AD+P). AD+P affects [~]50% of individuals with AD, identifies a subgroup with poor outcomes, and is associated with a greater degree of cognitive impairment and depressive symptoms, compared to subjects without psychosis (AD-P). Although the estimated heritability of AD+P is 61%, genetic sources of risk are unknown. We report a genome-wide meta-analysis of 12,317 AD subjects, 5,445 AD+P. Results showed common genetic variation accounted for a significant portion of heritability. Two loci, one in ENPP6 (rs9994623, O.R. (95%CI) 1.16 (1.10, 1.22), p=1.26x10-8) and one spanning the 3-UTR of an alternatively spliced transcript of SUMF1 (rs201109606, O.R. 0.65 (0.56-0.76), p=3.24x10-8), had genome-wide significant associations with AD+P. Gene-based analysis identified a significant association with APOE, due to the APOE risk haplotype {varepsilon}4. AD+P demonstrated negative genetic correlations with cognitive and educational attainment and positive genetic correlation with depressive symptoms. We previously observed a negative genetic correlation with schizophrenia; instead, we now found a stronger negative correlation with the related phenotype of bipolar disorder. Analysis of polygenic risk scores supported this genetic correlation and documented a positive genetic correlation with risk variation for AD, beyond the effect of {varepsilon}4. We also document a small set of SNPs likely to affect risk for AD+P and AD or schizophrenia. These findings provide the first unbiased identification of the association of psychosis in AD with common genetic variation and provide insights into its genetic architecture. | psychiatry and clinical psychology |
10.1101/2020.08.07.20170142 | Bone health risk assessment in a clinical setting: an evaluation of a new screening tool for active populations | IntroductionRisk factors for poor bone health are not restricted to older, sedentary populations for whom current screening is focused. Furthermore, access to dual X-ray absorptiometry scanning can be limited in clinical practice. The purpose of the current study was to develop a bone health-screening tool suitable for inclusion of both younger and active populations, combined with radiofrequency echographic multi spectrometry technology (REMS).
Methodology88 participants attending a physiotherapy clinic in the UK were recruited to the study: 71 women (mean age 41.5 SD 14.0 years); 17 men (mean age 40.2 SD 14.9 years). Participants completed an online bone health-screening questionnaire developed specifically for this study covering a range of lifestyle, physiological factors, combined with medical interview and received bone mineral density (BMD) measurement at the lumbar spine and femoral neck using REMS.
ResultsScoring of the bone health-screening questionnaire produced a distribution of bone health scores, with lower scores suggesting a higher risk for poor bone health. In women, scores ranged from -10 to +12, mean score 2.2 (SD 4.8). In men, scores ranged from 0 to 12, mean score 6.9 (SD 3.2). A positive correlation was observed between the bone health score derived from the questionnaire and lumbar spine and femoral neck BMD Z-scores (p<0.01).
ConclusionsThis new and comprehensive bone health-screening questionnaire with interview was effective in identifying active individuals at risk of bone fragility, who might be missed by current screening methods. The use of REMS technology to measure bone health, was feasible in the clinical setting. | sports medicine |
10.1101/2020.08.10.20172338 | IL-1 and IL-6 inhibitor hypersensitivity link to common HLA-DRB1*15 alleles | In Saper et al (2019), we described systemic JIA patients who developed a high-fatality diffuse lung disease (DLD) while on IL-1 or IL-6 inhibitors. We observed severe delayed drug hypersensitivity reactions (DHR) in a significant subset. Because alleles of the human leukocyte antigen (HLA) loci can mediate DHR, we investigated HLA genotype association with these DHR. We typed subjects treated with these inhibitors: 34 sJIA/DHR/DLD, 11 sJIA/DHR without DLD, 18 drug-tolerant sJIA, and 19 Kawasaki disease (KD) patients in an anti-IL-1(anakinra) trial. We also accessed genotypes from a large sJIA case/control cohort.
We first compared White subjects with sJIA/DHR to 550 ancestry-matched sJIA subjects. We found striking enrichments of HLA-DRB1*15:01, HLA-DQA1*01:02, and DQB1*06:02, alleles in near-complete linkage (White individuals). HLA-DRB 1*15:01 (as haplotype proxy) was increased in White sJIA subjects with DHR/DLD versus sJIA drug-tolerant controls and was observed upon inclusion of sJIA+DHR-only and KD+DHR White subjects. In our entire cohort regardless of ancestry, 75% carried HLA-DRB 1*15:01 or the structurally related DRB1*15:03 and DRB1*15:06, which were absent among drug-tolerant subjects (p=5 x 10-13; Odds Ratio lower bound=20.11).
Patients who harbor HLA-DRB1*15 alleles are at high risk of developing DHR to anti-IL-1/IL-6. Our data also suggest DHR maybe a trigger/enhancer of DLD in sJIA patients and support performing prospective HLA screening in sJIA, its adult-onset counterpart, and other inflammatory conditions where these drugs are used, such as KD. | allergy and immunology |
10.1101/2020.08.10.20172049 | Application of Optimal Control to Long Term Dynamics of COVID-19 Disease in South Africa | SARS-CoV-2 (COVID-19) belongs to the beta-coronavirus family, which include: the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV). Since its outbreak in South Africa in March 2020, it has lead to high mortality and thousands of people contracting the virus. Mathematical analysis of a model without controls was done and the basic reproduction number ([R]0) of the COVID-19 for the South African pandemic determined. We introduced permissible controls and formulate an optimal control problem using the Pontraygain Maximum Principle. Our numerical findings suggest that joint implementation of effective mask usage, physical distancing and active screening and testing, are effective measures to curtail the spread of the disease in the human population. The results obtained in this paper are of public health importance in the control and management of the spread for the novel coronavirus, SARS-CoV-2, in South Africa. | epidemiology |
10.1101/2020.08.10.20171363 | Genetic landscape of rare autoinflammatory disease variants in Qatar and Middle Eastern populations through the integration of whole-genome and exome datasets | Rare monogenic autoinflammatory diseases are a group of recurrent inflammatory genetic disorders caused due to genetic variants in over 37 genes. While a number of these disorders have been identified and reported from the Middle Eastern populations, the carrier frequency of these genetic variants in the Middle Eastern populations is not known. The availability of whole-genome and exome datasets of over a thousand individuals from Qatar persuaded us to explore the genetic epidemiology of rare autoinflammatory genetic variants. We have systematically analyzed genetic variants in genome-scale datasets from Qatar with a compendium of variants associated with autoinflammatory diseases. The variants were systematically reclassified according to the American College of Medical Genetics and Genomics guidelines for interpretation of variant pathogenicity. Our analysis identified 7 pathogenic and likely pathogenic variants with significant differences in their allele frequencies compared to the global population. The cumulative carrier frequency of these variants was found to be 2.58%. Furthermore, our analysis revealed that 5 genes implicated in rare autoinflammatory diseases were under natural selection. To our best knowledge, this is the first and comprehensive study on the population-scale analysis and genetic epidemiology for genetic variants causing rare autoinflammatory disease in Middle Eastern populations. | rheumatology |
10.1101/2020.08.10.20171454 | How long does it take to eliminate an epidemic without herd immunity? | The global response to the SARS-Cov-2 pandemic has consisted of two main strategies both involving non-pharmaceutical interventions to control spread: mitigation, ultimately relying on herd immunity from vaccination, and elimination of infections locally. While simple theory for controlling an epidemic through herd immunity exist, there is no corresponding simple theory for the strategy of elimination with non-pharmaceutical interventions. Here we quantify an important aspect of the elimination strategy: the time to extinction without herd immunity, based solely on non-pharmaceutical interventions. Using a simple well-mixed stochastic SIR model, we find two new results: 1) using random walk theory we calculate a simple approximation of the mean extinction time and 2) using branching process theory the full distribution of times to extinction, which we show is given by the extreme value Gumbel distribution. We compare these results against complex spatially-resolved stochastic simulations to show very good quantitative agreement, demonstrating the validity of this simple approach. Overall, for SARS-Cov-2 our results predict rapid extinction -- of order months -- of an epidemic or pandemic if the reproductive number is kept to Re < 0.5; in a counterfactual scenario with global adoption of an elimination strategy in June 2020, SARS-Cov-2 could have been eliminated world-wide by early January 2021. | epidemiology |
10.1101/2020.08.09.20171264 | The Mean Unfulfilled Lifespan (MUL): A New Indicator of the Impact of Mortality Shocks on the Individual Lifespan | Declines in period life expectancy at birth (PLEB) provide intuitive indicators of the impact of a cause of death on the individual lifespan. Derived under the assumption that future mortality conditions will remain indefinitely those observed during a reference period, however, the intuitive interpretation of a PLEB becomes problematic when that period conditions reflect a temporary mortality "shock", resulting from a natural disaster or the diffusion of a new epidemic in the population for instance.
Rather than to make assumptions about future mortality, I propose measuring the difference between a period average age at death and the average expected age at death of the same individuals (death cohort): the Mean Unfulfilled Lifespan (MUL). For fine-grained tracking of the mortality impact of an epidemic, I also provide an empirical shortcut to MUL estimation for small areas or short periods.
For illustration, quarterly MUL values in 2020 are derived from estimates of COVID-19 deaths in 159 national populations and 122 sub-national populations in Italy, Mexico, Spain and the US. The highest quarterly values in national populations are obtained for Ecuador (5.12 years, second quarter) and Peru (4.56 years, third quarter) and, in sub-national populations, for New York (5.52 years), New Jersey (5.56 years, second quarter) and Baja California (5.19 years, fourth quarter). Using a seven-day rolling window, the empirical shortcut suggests the MUL peaked at 9.12 years in Madrid, 9.20 years in New York, and 9.15 years in Baja California, and in Guayas (Ecuador) it even reached 12.6 years for the entire month of April.
Based on reported COVID-19 deaths that might substantially underestimate overall mortality change in affected populations, these results nonetheless illustrate how the MUL tracks the mortality impact of the pandemic, or any mortality shock, retaining the intuitive metric of differences in PLEB, without their problematic underlying assumptions. | public and global health |
10.1101/2020.08.10.20170522 | A multi-task convolutional deep learning method for HLA allelic imputation and its application to trans-ethnic MHC fine-mapping of type 1 diabetes | Conventional HLA imputation methods drop their performance for infrequent alleles, which reduces reliability of trans-ethnic MHC fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We developed DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,112), DEEP*HLA achieved the highest accuracies in both datasets (0.987 and 0.976) especially for low-frequency and rare alleles. DEEP*HLA was less dependent of distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We applied DEEP*HLA to type 1 diabetes GWAS data of BioBank Japan (n = 62,387) and UK Biobank (n = 356,855), and successfully disentangled independently associated class I and II HLA variants with shared risk between diverse populations (the top signal at HLA-DR{beta}1 amino acid position 71; P = 6.2 x10-119). Our study illustrates a value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping. | genetic and genomic medicine |
10.1101/2020.08.10.20148502 | Cumulative survival profiling: a new PAP-based method for detecting heteroresistance in staphylococcal clinical isolates | The area under the population analysis profile (PAP) is used in the gold standard method for detecting heteroresistance in staphylococci. We tested the hypothesis that the initial inoculum strongly influences the area under the population analysis profile. We sought to interpret this dependence and develop a new metric that lacks this dependence to retrospectively detect heteroresistance to vancomycin in coagulase-negative staphylococcal (CoNS) isolates.
We tested our hypothesis on 20 PAPs from the heteroresistant positive control isolate (Mu3) and 7 PAPs from one CoNS isolate which is associated with poor clinical response. The area under the PAP depended linearly (p<0.001) on the initial inoculum. We interpreted the slope to be the cumulative survival under vancomycine concentration gradient. The statistical distribution of the cumulative survival for Mu3 and the CoNS isolate constituted the cumulative survival profiles for each. The profiles reflect ed spectrum of response under vancomycine gradient with the left-tail of CoNS isolate profile located near the median of Mu3 profile indicating the heteroresistance of the CoNS isolate and that the most resistant in the spectrum are likely to be associated with poor clinical response. We estimated that about two-third of the CoNS from unique participants are heteroresistant with 80% of heteroresistant isolates may be associated with a poor clinical response. | infectious diseases |
10.1101/2020.08.07.20170191 | A Review of AI and Data Science Support for Cancer Management | IntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.
ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.
MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patients state from it and deliver coaching/behavior change interventions.
ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.
ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients. | health informatics |
10.1101/2020.08.11.20172833 | Lockdown Measures and their Impact on Single- and Two-age-structured Epidemic Model for the COVID-19 Outbreak in Mexico | The role of lockdown measures in mitigating COVID-19 in Mexico is investigated using a comprehensive nonlinear ODE model. The model includes both asymptomatic and presymptomatic populations with the latter leading to sickness (with recovery, hospitalization and death possibilities). We consider situations involving the application of social-distancing and other intervention measures in the time series of interest. We find optimal parametric fits to the time series of deaths (only), as well as to the time series of deaths and cumulative infections. We discuss the merits and disadvantages of each approach, we interpret the parameters of the model and assess the realistic nature of the parameters resulting from the optimization procedure. Importantly, we explore a model involving two sub-populations (younger and older than a specific age), to more accurately reflect the observed impact as concerns symptoms and behavior in different age groups. For definiteness and to separate people that are (typically) in the active workforce, our partition of population is with respect to members younger vs. older than the age of 65. The basic reproduction number of the model is computed for both the single- and the two-population variant. Finally, we consider what would be the impact of partial lockdown (involving only the older population) and full lockdown (involving the entire population) on the number of deaths and cumulative infections. | epidemiology |
10.1101/2020.08.13.20173757 | LAMP-BEAC: Detection of SARS-CoV-2 RNA Using RT-LAMP and Molecular Beacons | BackgroundRapid spread of SARS-CoV-2 has led to a global pandemic, resulting in the need for rapid assays to allow diagnosis and prevention of transmission. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) provides a gold standard assay for SARS-CoV-2 RNA, but tests are expensive and supply chains are potentially fragile, motivating interest in additional assay methods. Reverse Transcription and Loop-Mediated Isothermal Amplification (RT-LAMP) provides an alternative that uses orthogonal and often less expensive reagents without the need for thermocyclers. The presence of SARS-CoV-2 RNA is typically detected using dyes to report bulk amplification of DNA; however, a common artifact is nonspecific DNA amplification, which complicates detection.
ResultsHere we describe the design and testing of molecular beacons, which allow sequence-specific detection of SARS-CoV-2 genomes with improved discrimination in simple reaction mixtures. To optimize beacons for RT-LAMP, multiple locked nucleic acid monomers were incorporated to elevate melting temperatures. We also show how beacons with different fluorescent labels can allow convenient multiplex detection of several amplicons in "single pot" reactions, including incorporation of a human RNA LAMP-BEAC assay to confirm sample integrity. Comparison of LAMP-BEAC and RT-qPCR on clinical saliva samples showed good concordance between assays. To facilitate implementation, we developed custom polymerases for LAMP-BEAC and inexpensive purification procedures, which also facilitates increasing sensitivity by increasing reaction volumes.
ConclusionsLAMP-BEAC thus provides an affordable and simple SARS-CoV-2 RNA assay suitable for population screening; implementation of the assay has allowed robust screening of thousands of saliva samples per week. | infectious diseases |
10.1101/2020.08.13.20174011 | Inequality indices to monitor geographic differences in incidence, mortality and fatality rates over time during the COVID-19 pandemic. | BackgroundIt is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time.
MethodsThe relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size.
ResultsComparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices).
ConclusionsThe use of inequality indices to monitor changes in geographic in-equality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics. | infectious diseases |
10.1101/2020.08.12.20173047 | Examining face-mask usage as an effective strategy to control COVID-19 spread | COVID-19s high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, we show that practicing social distance is less efficacious than the widespread usage of face masks and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.
Author summaryThe COVID-19 outbreak has created an enormous burden on the worldwide population. Among the various ways of preventing the spread of the virus, face masks have been proposed as a main way of reducing transmission. Yet, the interplay between the usage of face mask and other forms of Non-Pharmaceutical Intervention is still not completely clear. In this paper we introduce a stochastic individual-based model which aims at producing realistic scenarios of disease spread when mask wearing with different inward and outward efficacy and social distancing are enforced. The model elucidates the conditions that makes the two forms of intervention synergistic in preventing the spread of the disease. | epidemiology |
10.1101/2020.08.13.20171876 | Detection of cognitive decline using a single-channel EEG with an interactive assessment tool | BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel biomarkers for detection. EEG biomarkers based on machine learning may offer a noninvasive low-coast approach for identifying cognitive decline. However, most studies use multi-electrode systems which are less accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with an interactive assessment tool.
MethodsThis pilot study included data collection from 82 participants who performed a cognitive assessment while being recorded with a single-channel EEG system. Seniors in different clinical stages of cognitive decline (healthy to mild dementia) and young healthy participants were included. Seniors MMSE scores were used to allocate groups with cutoff scores of 24 and 27. Data analysis included correlation analysis as well as linear mixed model analysis with several EEG variables including frequency bands and three novel cognitive biomarkers previously extracted from a different dataset.
ResultsMMSE scores correlated significantly with reaction times, as well as two EEG biomarkers: A0 and ST4. Both biomarkers showed significant separation between study groups: ST4 separated between the healthy senior group and the low-MMSE group. A0 differentiated between the healthy senior group and the other three groups, showing different cognitive patterns between different stages of cognitive decline as well as different patterns between young and senior healthy participants. In the healthy young group, activity of Theta, Delta, A0 and VC9 biomarkers significantly separated between high and low levels of cognitive load, consistent with previous reports. VC9 and Theta showed a finer separation between low cognitive load levels and resting state.
ConclusionsThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use portable single-channel EEG device with an interactive cognitive assessment. The short set-up time and novel biomarkers enable objective and easy assessment of cognitive decline. Future studies should explore potential usefulness of this tool in characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention. | neurology |
10.1101/2020.08.13.20171876 | Detection of cognitive decline using a single-channel EEG system with an interactive assessment tool | BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel biomarkers for detection. EEG biomarkers based on machine learning may offer a noninvasive low-coast approach for identifying cognitive decline. However, most studies use multi-electrode systems which are less accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with an interactive assessment tool.
MethodsThis pilot study included data collection from 82 participants who performed a cognitive assessment while being recorded with a single-channel EEG system. Seniors in different clinical stages of cognitive decline (healthy to mild dementia) and young healthy participants were included. Seniors MMSE scores were used to allocate groups with cutoff scores of 24 and 27. Data analysis included correlation analysis as well as linear mixed model analysis with several EEG variables including frequency bands and three novel cognitive biomarkers previously extracted from a different dataset.
ResultsMMSE scores correlated significantly with reaction times, as well as two EEG biomarkers: A0 and ST4. Both biomarkers showed significant separation between study groups: ST4 separated between the healthy senior group and the low-MMSE group. A0 differentiated between the healthy senior group and the other three groups, showing different cognitive patterns between different stages of cognitive decline as well as different patterns between young and senior healthy participants. In the healthy young group, activity of Theta, Delta, A0 and VC9 biomarkers significantly separated between high and low levels of cognitive load, consistent with previous reports. VC9 and Theta showed a finer separation between low cognitive load levels and resting state.
ConclusionsThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use portable single-channel EEG device with an interactive cognitive assessment. The short set-up time and novel biomarkers enable objective and easy assessment of cognitive decline. Future studies should explore potential usefulness of this tool in characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention. | neurology |
10.1101/2020.08.14.20174920 | The clinical and prognostic significance of Protein Arginine Deiminase 2 and 4 (PADI2 & PADI4) in colorectal cancer | AimsProtein arginine deiminase (PADs) are a family of enzymes that catalyse the post translational modification (PTM) of proteins. Association between PAD expression with clinicopathology, protein expression and outcome was determined.
MethodsPADI2 and PADI4 expression was assessed immunohistochemically in a cohort of CRC patients.
ResultsCRC tissues expressed variable levels of PADI2 which was mainly localised in the cytoplasm and correlated with patient survival (p=0.005); high expression increased survival time from 43.5 to 67.6 months. Expression of cytoplasmic PADI2 correlated with expression of nuclear {beta} catenin, PADI4 and alpha-enolase. In contrast expression of nuclear PADI2 correlated with a decrease in survival (p=0.010), with high expression decreasing survival from 76.4 to 42.9 months. CRC tissues expressed variable levels of PADI4 in both the nucleus and cytoplasm. Expression of cytoplasmic PADI4 correlated with survival (p=0.001) with high expression increasing survival time from 48.1 to 71.8 months. Expression of cytoplasmic PADI4 correlated with expression of, nuclear {beta} catenin, alpha-enolase (p[≤]0.0001, p=0.002) and the apoptotic related protein, Bcl-2. Expression of nuclear PADI4 also correlated with survival (p=0.011) with high expression of nuclear PADI4 increasing survival time from 55.4 to 74 months. Expression of nuclear PADI4 correlated with p53, alpha-enolase and Bcl-2. Multivariate analysis showed that TNM stage, cytoplasmic PADI2 and PADI4 remained independent prognostic factors in CRC. Both PADI2 and PADI4 are good prognostic factors in CRC.
ConclusionsHigh expression of cytoplasmic PADI2, PADI4 and nuclear PADI4 were associated with an increase in overall survival. | oncology |
10.1101/2020.08.16.20167577 | Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network | In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimers disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trials study protocol to identify the study population, treatment regimen assignments, and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher SAE rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23mg arm than the 10 mg standard-of-care arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials design. Nevertheless, such approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted. | epidemiology |
10.1101/2020.08.18.20176693 | Exhaled SARS-CoV-2 quantified by face-mask sampling in hospitalised patients with covid-19 | BackgroundHuman to human transmission of SARS-CoV-2 is driven by the respiratory route but little is known about the pattern and quantity of virus output from exhaled breath. We have previously shown that face-mask sampling (FMS) can detect exhaled tubercle bacilli and have adapted its use to quantify exhaled SARS-CoV-2 RNA in patients admitted to hospital with covid-19.
MethodsBetween May and December 2020, we took two concomitant FMS and nasopharyngeal samples (NPS) over two days, starting within 24 hours of a routine virus positive NPS in patients hospitalised with covid-19, at University Hospitals of Leicester NHS Trust, UK. Participants were asked to wear a modified duckbilled facemask for 30 minutes, followed by a nasopharyngeal swab. Demographic, clinical, and radiological data, as well as International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) mortality and deterioration scores were obtained.
Exposed masks were processed by removal, dissolution and analysis of sampling matrix strips fixed within the mask by RT-qPCR. Viral genome copy numbers were determined and results classified as Negative; Low: [≤]999 copies; Medium: 1,000-99,999 copies and High [≥] 100,000 copies per strip for FMS or per 100{micro}l for NPS.
Results102 FMS and NPS were collected from 66 routinely positive patients; median age: 61 (IQR 49 - 77), of which FMS was positive in 37% of individuals and concomitant NPS was positive in 50%. Positive FMS viral loads varied over five orders of magnitude (<10-3.3 x 106 genome copies/strip); 21 (32%) patients were asymptomatic at the time of sampling. High FMS viral load was associated with respiratory symptoms at time of sampling and shorter interval between sampling and symptom onset (FMS High: median (IQR) 2 days (2-3) vs FMS Negative: 7 days (7-10), p=0.002). On multivariable linear regression analysis, higher FMS viral loads were associated with higher ISARIC mortality (Medium FMS vs Negative FMS gave an adjusted coefficient of 15.7, 95% CI 3.7-27.7, p=0.01) and deterioration scores (High FMS vs Negative FMS gave an adjusted coefficient of 37.6, 95% CI 14.0 to 61.3, p=0.002), while NPS viral loads showed no significant association.
ConclusionWe demonstrate a simple and effective method for detecting and quantifying exhaled SARS-CoV-2 in hospitalised patients with covid-19. Higher FMS viral loads were more likely to be associated with developing severe disease compared to NPS viral loads. Similar to NPS, FMS viral load was highest in early disease and in those with active respiratory symptoms, highlighting the potential role of FMS in understanding infectivity. | infectious diseases |
10.1101/2020.08.18.20177626 | A Bayesian estimate of the COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey | We infer the infection fatality ratio (IFR) of SARS-CoV-2 in Brazil by combining three datasets. We compute the prevalence via the population-based seroprevalence survey EPICOVID19-BR. For the fatalities we obtain the absolute number using the public Painel Coronavirus dataset and the age-relative number using the public SIVEP-Gripe dataset. The time delay between the development of antibodies and subsequent fatality is estimated via the SIVEP-Gripe dataset. We obtain the IFR via Bayesian inference for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the time after contagion at which the test gives a negative result with a flat prior on the interval [40, 80] days. We infer a country-wide average IFR (maximum posterior and 95% CI) of 0.97% (0.82-1.14%) and age-specific IFR: 0.028% (0.024-0.036%) [< 30 years], 0.21% (0.17-0.25%) [30-49 years], 1.06% (0.88-1.31%) [50-69 years], 2.9% (2.5-3.7%) [[≥] 70 years]. | epidemiology |
10.1101/2020.08.18.20177451 | Investigating dynamics of COVID-19 spread and containment with agent-based modeling | AO_SCPLOWBSTRACTC_SCPLOWGovernments, policy makers and officials around the globe are trying to mitigate the effects and progress of the COVID-19 pandemic by making decisions which will save the most lives and impose the least costs. Making these decisions needs a comprehensive understanding about the dynamics by which the disease spreads. In this work, we propose an epidemic agent-based model that simulates the spread of the disease. We show that the model is able to generate an important aspect of the pandemic: multiple waves of infection. A key point in the model description is the aspect of fear which can govern how agents behave under different conditions. We also show that the model provides an appropriate test-bed to apply different containment strategies and this work presents the results of applying two such strategies: testing, contact tracing, and travel restriction. The results show that while both strategies could result in flattening the epidemic curve and significantly reduce the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions. | epidemiology |
10.1101/2020.08.18.20177113 | Individualizing deep dynamic models for psychological resilience data | Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, and a differing number of observations per individual. This technique allows us to subsequently investigate individual reactions to stimuli, such as the mental health impact of stressors. A potentially large number of baseline characteristics can then be linked to this individual response by regularized regression, e.g., for identifying resilience factors. Thus, our new method provides a way of connecting different kinds of complex longitudinal and baseline measures via individualized, dynamic models. The promising results obtained in the exemplary resilience application indicate that our proposal for dynamic deep learning might also be more generally useful for other application domains. | psychiatry and clinical psychology |
10.1101/2020.08.19.20178343 | The Effects of Coronavirus Victimization Distress and Coronavirus Racial Bias on Mental Health Among Black, Indigenous and Latinx Young Adults in the United States | RationaleU.S. Racial/ethnic minorities have been disproportionately impacted by the COVID-19 pandemic in rates of infection and morbidity. Pre-pandemic racial discrimination has been associated with depression and general anxiety. However, the effect of Coronavirus specific forms of discrimination on mental health have not been examined. This study assessed the effect of previously identified social determinants of mental health and COVID-19 specific victimization and racial bias beliefs on depression and anxiety among young adults of color in the U.S.
MethodsA national online survey of 399 AIAN, Asian, Black, and Latinx adults (18 - 25 years) included demographic variables, COVID-19 health risks, and standardized measures of depression, anxiety, Coronavirus related victimization distress and perceptions of Coronavirus-related racial bias across a range of contexts.
ResultsEmployment, financial and prescription insecurity, COVID-19 health risks, Coronavirus victimization distress and Coronavirus racial bias beliefs were positively correlated with depression and anxiety. Scores on the Coronavirus racial bias scale were significantly higher among Asian and Black respondents. Structural equation modeling controlling for race/ethnicity and demographic variables indicated perceived Coronavirus racial bias mediated the effect of Coronavirus victimization distress on both mental health indices.
ConclusionResults suggest the COVID-19 pandemic has created new pathways to mental health disparities among young adults of color by reversing formerly protective factors such as employment, and by exacerbating structural and societal inequities linked to race. Findings highlight the necessity of creating mental health services tailored to the specific needs of racial/ethnic minorities during the current and future health crises. | psychiatry and clinical psychology |
10.1101/2020.08.19.20178343 | The Effects of Coronavirus Victimization Distress and Coronavirus Racial Bias on Mental Health Among AIAN, Asian, Black, and Latinx Young Adults | RationaleU.S. Racial/ethnic minorities have been disproportionately impacted by the COVID-19 pandemic in rates of infection and morbidity. Pre-pandemic racial discrimination has been associated with depression and general anxiety. However, the effect of Coronavirus specific forms of discrimination on mental health have not been examined. This study assessed the effect of previously identified social determinants of mental health and COVID-19 specific victimization and racial bias beliefs on depression and anxiety among young adults of color in the U.S.
MethodsA national online survey of 399 AIAN, Asian, Black, and Latinx adults (18 - 25 years) included demographic variables, COVID-19 health risks, and standardized measures of depression, anxiety, Coronavirus related victimization distress and perceptions of Coronavirus-related racial bias across a range of contexts.
ResultsEmployment, financial and prescription insecurity, COVID-19 health risks, Coronavirus victimization distress and Coronavirus racial bias beliefs were positively correlated with depression and anxiety. Scores on the Coronavirus racial bias scale were significantly higher among Asian and Black respondents. Structural equation modeling controlling for race/ethnicity and demographic variables indicated perceived Coronavirus racial bias mediated the effect of Coronavirus victimization distress on both mental health indices.
ConclusionResults suggest the COVID-19 pandemic has created new pathways to mental health disparities among young adults of color by reversing formerly protective factors such as employment, and by exacerbating structural and societal inequities linked to race. Findings highlight the necessity of creating mental health services tailored to the specific needs of racial/ethnic minorities during the current and future health crises. | psychiatry and clinical psychology |
10.1101/2020.08.20.20178525 | Clinical Characteristics and Outcomes of Diabetic COVID-19 patients in Kuwait | BackgroundCOVID-19 has a highly variable clinical presentation, ranging from asymptomatic to severe respiratory symptoms and death. Diabetes seems to be one of the main comorbidities contributing to a worse COVID-19 outcome.
ObjectiveIn here we analyze the clinical characteristics and outcomes of diabetic COVID-19 patients Kuwait.
MethodsIn this single-center, retrospective study of 417 consecutive COVID-19 patients, we analyze and compare disease severity, outcome, associated complications, and clinical laboratory findings between diabetic and non-diabetic COVID-19 patients.
ResultsCOVID-19 patients with diabetes had more ICU admission than non-diabetic COVID-19 patients (20.1% vs. 16.8%, p<0.001). Diabetic COVID-19 patients also recorded higher mortality in comparison to non-diabetic COVID-19 patients (16.7% vs. 12.1%, p<0.001). Diabetic COVID-19 patients had significantly higher prevalence of comorbidities, such as hypertension. Laboratory investigations also highlighted notably higher levels of C-reactive protein in diabetic COVID019 patients and lower estimated glomerular filtration rate. They also showed a higher incidence of complications. logistic regression analysis showed that every 1 mmol/L increase in fasting blood glucose in COVID-19 patients is associated with 1.52 (95% CI: 1.34 - 1.72, p<0.001) times the odds of dying from COVID-19.
ConclusionDiabetes is a major contributor to worsening outcomes in COVID-19 patients. Understanding the pathophysiology underlining these findings could provide insight into better management and improved outcome of such cases.
Highlights of the StudyO_LIA significantly higher proportion of COVID-19 patients with diabetes mellitus required admission to the ICU.
C_LIO_LIHigher fasting blood glucose was associated with higher risk of COVID-19 associated mortality.
C_LIO_LICOVID-19 patients with diabetes mellitus had significantly higher incidence of complications including sepsis, ARDS, cardiac failure and renal failure.
C_LI | infectious diseases |
10.1101/2020.08.20.20178699 | Systematic review and patient-level meta-analysis of SARS-CoV-2 viral dynamics to model response to antiviral therapies | SARS-CoV-2 viral loads change rapidly following symptom onset so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient-level meta-analysis of SARS-CoV-2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to-date. This systematic review identified case reports, case series and clinical trial data from publications between 1/1/2020 and 31/5/2020 following PRISMA guidelines. A multivariable Cox proportional hazards regression model (Cox-PH) of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed-effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modelling of respiratory viral dynamics was performed to quantify time dependent drug effects. Patient-level data from 645 individuals (age 1 month-100 years) with 6316 viral loads were extracted. Model-based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions and breast milk were generated. Cox-PH modelling showed longer time to viral clearance in older patients, males and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, p<0.001), as well as interferon, particularly when combined with ribavirin (AHR = 2.2, p=0.015; AHR = 6.04, p = 0.006). Combination therapy should be further investigated. A viral dynamic dataset and NLME model for designing and analysing antiviral trials has been established. | infectious diseases |
10.1101/2020.08.20.20178657 | Cohort profile: COVID-19 in a cohort of pregnant women and their descendants, the MOACC-19 study | PurposeThe Mother and Child Covid-19 study is a cohort recruiting pregnant women and their children in Cantabria, North of Spain, during COVID-19 pandemic in order to ascertain Consequences of SARS-CoV-2 infection on pregnant women and their descendants. This article reports the cohort profile and preliminary results as recruitment is still open.
ParticipantsThree sub-cohorts can be identified at recruitment. Sub-cohort 1 includes women giving birth between 23rd March and 25th May 2020; they have been retrospectively recruited and could have been exposed to COVID-19 only in their third trimester of pregnancy. Sub-cohort 2 includes women giving birth from 26th May 2020 on; they are being prospectively recruited and could have been exposed to COVID-19 in both their second and third trimesters of pregnancy. Sub-cohort 3 includes women in their 12th week of pregnancy prospectively recruited from 26th May 2020 on; they could have been exposed to COVID-19 anytime in their pregnancy. All women are being tested for SARS-CoV-2 infection using both RT-PCR for RNA detection and ELISA for anti-SARS-CoV-2 antibodies. All neonates are being tested for antibodies using immunochemoluminiscency tests; if the mother is tested positive for SARS-CoV-2 RNA, a naso-pharyngeal swab is also obtained from the child for RT-PCR analysis.
Findings to dateAs of 22nd October, 1167 women have been recruited (266, 354 and 547 for sub-cohorts 1, 2 and 3, respectively). Fourteen women tested positive to SARS-CoV-2 RNA by the day of delivery. All fourteen children born from these women tested negative for SARS-CoV-2 RNA.
Future plansChildren from women included in sub-cohort 3 are expected to be recruited by the end of 2020. Children will be followed-up for one year in order to ascertain the effect that COVID-19 on their development.
ARTICLE SUMMARYStrengths and limitations
StrengthsO_LIThis cohort would ascertain the effect of COVID-19 in both mother and children whatever the trimester of the infection.
C_LIO_LIIt would also compare health care provided to pregnant women during the COVID-19 pandemic with that provided in the same hospital before the emergence of COVID-19.
C_LIO_LIThe cohort is recruited in Spain, one of the developed countries earlier and more affected by COVID-19.
C_LI
LimitationsO_LIThe study could be underpowered according to the prevalence reported in a Spanish national study.
C_LIO_LIInformation regarding exposure to people infected by SARS-CoV-2 or risk activities is self-reported.
C_LI | epidemiology |
10.1101/2020.08.20.20178608 | Effects of an urban sanitation intervention on childhood enteric infection and diarrhea in Maputo, Mozambique: a controlled before-and-after trial | We conducted a controlled before-and-after trial to evaluate the impact of an onsite urban sanitation intervention on the prevalence of enteric infection, soil transmitted helminth re-infection, and diarrhea among children in Maputo, Mozambique. A non-governmental organization replaced existing poor-quality latrines with pour-flush toilets with septic tanks serving household clusters. We enrolled children aged 1-48 months at baseline and measured outcomes before and 12 and 24 months after the intervention, with concurrent measurement among children in a comparable control arm. Despite nearly exclusive use, we found no evidence that intervention affected the prevalence of any measured outcome after 12 or 24 months of exposure. Among children born into study sites after intervention, we observed a reduced prevalence of Trichuris and Shigella infection relative to the same age group at baseline (<2 years old). Protection from birth may be important to reduce exposure to and infection with enteric pathogens in this setting. | public and global health |
10.1101/2020.08.21.20179580 | User testing of a Diagnostic Decision Support System with Machine-assisted Chart Review to Facilitate Clinical Genomic Diagnosis | BackgroundThere is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic re-analysis of results, and can utilize information from the electronic health record to do so. Such systems should be built using the concepts of human-centered design, fit within clinical workflows, and provide solutions to priority problems.
MethodsWe adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user testing. A semi-structured interview guide was created to illuminate factors relevant to human factors in CDS design and organizational implementation.
ResultsSix individuals completed the user testing process. Tester responses were positive and noted good fit with real-world clinical genetics workflow. Technical issues related to interface, interaction, and design were minor and fixable. Testers suggested solving issues related to terminology and usability through training and infobuttons. Time savings was estimated at 30-50% and additional uses such as in-house clinical variant analysis were suggested for increase fit with workflow and to further address priority problems.
ConclusionThis study provides preliminary evidence for usability, workflow fit, acceptability, and implementation potential of a modified DDSS that includes machine-assisted chart review. Continued development and testing using principles from human-centered design and implementation science are necessary to improve technical functionality and acceptability for multiple stakeholders and organizational implementation potential to improve the genomic diagnosis process.
SUMMARYO_ST_ABSWhat is already known?C_ST_ABSO_LIThere is a need in clinical genomics for tools that assist in analysis of genomic information and can do so using information from the electronic health record.
C_LIO_LISuch tools should be easy to use, fit within clinical workflows, and provide solutions to priority problems as defined by clinician end-users.
C_LIO_LINatural language processing (NLP) is a useful tool to read patient records and extract findings.
C_LI
What does this paper add?O_LIWe demonstrated the use of Human-centered design and implementation science principles in a simulated environment for assessment of a new version of a decision support tool prior to large-scale implementation.
C_LIO_LIThis study provides preliminary evidence that a clinical decision support tool with machine-assisted chart review is acceptable to clinical end-users, fits within the clinical workflow, and addresses perceived needs within the differential diagnosis process across all Mendelian genetic disorders.
C_LIO_LITerminology codes for DDSSs should have levels of granularity tuned to the sensitivity and specificity appropriate to its various functions, e.g., NLP versus chart documentation.
C_LI | health informatics |
10.1101/2020.08.21.20179580 | User testing of a Diagnostic Decision Support System with Machine-assisted Chart Review to Facilitate Clinical Genomic Diagnosis | BackgroundThere is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic re-analysis of results, and can utilize information from the electronic health record to do so. Such systems should be built using the concepts of human-centered design, fit within clinical workflows, and provide solutions to priority problems.
MethodsWe adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user testing. A semi-structured interview guide was created to illuminate factors relevant to human factors in CDS design and organizational implementation.
ResultsSix individuals completed the user testing process. Tester responses were positive and noted good fit with real-world clinical genetics workflow. Technical issues related to interface, interaction, and design were minor and fixable. Testers suggested solving issues related to terminology and usability through training and infobuttons. Time savings was estimated at 30-50% and additional uses such as in-house clinical variant analysis were suggested for increase fit with workflow and to further address priority problems.
ConclusionThis study provides preliminary evidence for usability, workflow fit, acceptability, and implementation potential of a modified DDSS that includes machine-assisted chart review. Continued development and testing using principles from human-centered design and implementation science are necessary to improve technical functionality and acceptability for multiple stakeholders and organizational implementation potential to improve the genomic diagnosis process.
SUMMARYO_ST_ABSWhat is already known?C_ST_ABSO_LIThere is a need in clinical genomics for tools that assist in analysis of genomic information and can do so using information from the electronic health record.
C_LIO_LISuch tools should be easy to use, fit within clinical workflows, and provide solutions to priority problems as defined by clinician end-users.
C_LIO_LINatural language processing (NLP) is a useful tool to read patient records and extract findings.
C_LI
What does this paper add?O_LIWe demonstrated the use of Human-centered design and implementation science principles in a simulated environment for assessment of a new version of a decision support tool prior to large-scale implementation.
C_LIO_LIThis study provides preliminary evidence that a clinical decision support tool with machine-assisted chart review is acceptable to clinical end-users, fits within the clinical workflow, and addresses perceived needs within the differential diagnosis process across all Mendelian genetic disorders.
C_LIO_LITerminology codes for DDSSs should have levels of granularity tuned to the sensitivity and specificity appropriate to its various functions, e.g., NLP versus chart documentation.
C_LI | health informatics |
10.1101/2020.08.22.20179960 | Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA | In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50%, 75%, and 100%) and three levels of face-mask adherence in schools (50%, 75%, and 100%). Under a scenario in which schools operated remotely, we projected 45,579 (95% CrI: 14,109-132,546) infections and 790 (95% CrI: 176-1680) deaths statewide between August 24 and December 31. Reopening at 100% capacity with 50% face-mask adherence in schools resulted in a proportional increase of 42.9 (95% CrI: 41.3-44.3) and 9.2 (95% CrI: 8.9-9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50% capacity with 100% face-mask adherence, the number of infections and deaths were 22% (95% CrI: 16%-28%) and 11% (95% CrI: 5%-18%) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80-95%) and intermediate levels of face-mask adherence (40-70%) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities. | epidemiology |
10.1101/2020.08.23.20180133 | Current and emerging polymyxin resistance diagnostics: a systematic review of established and novel detection methods | BackgroundThe emergence of polymyxin resistance, due to transferable mcr-genes, threatens public and animal health as there are limited therapeutic options. As polymyxin is one of the last-line antibiotics, there is a need to contain the spread of its resistance to conserve its efficacy. Herein, we describe current and emerging polymyxin resistance diagnostics to inform faster clinical diagnostic choices.
MethodsA literature search in diverse databases for studies published between 2016 and 2020 was performed. English articles evaluating colistin resistance methods/diagnostics were included.
ResultsScreening resulted in the inclusion of 93 journal articles. Current colistin resistance diagnostics are either phenotypic or molecular. Broth microdilution (BMD) is currently the only gold standard for determining colistin MICs (minimum inhibitory concentration).
Phenotypic methods comprise of agar-based methods such as CHROMagar Col-APSE, SuperPolymyxin, ChromID(R) Colistin R, LBJMR, and LB medium; manual MIC-determiners viz., UMIC, MICRONAUT MIC-Strip (MMS), and ComASP Colistin; automated antimicrobial susceptibility testing (AST) systems such as BD Phoenix, MICRONAUT-S, MicroScan, Sensititre and Vitek 2; MCR-detectors such as lateral flow immunoassay (LFI) and chelator-based assays including EDTA- and DPA-based tests i.e. combined disk test (CDT), modified colistin broth-disk elution (CBDE), Colispot, and Colistin MAC test as well as biochemical colorimetric tests i.e. Rapid Polymyxin NP test and Rapid ResaPolymyxin NP test. Molecular methods only characterize mobile colistin resistance; they include PCR, LAMP, and whole-genome sequencing (WGS).
ConclusionDue to the faster turnaround time ([≤]3h), improved sensitivity (84-100%), and specificity (93.3-100%) of the Rapid ResaPolymyxin NP test, we recommend this test for initial screening of colistin-resistant isolates. This can be followed by CBDE with EDTA or the LFI as they both have 100% sensitivity and a specificity of [≥] 94.3% for the rapid screening of mcr-genes. However, molecular assays such as LAMP and PCR may be considered in well-equipped clinical laboratories.
Author summary/highlights/importanceO_LIPolymyxin resistance is rapidly increasing, threatening public and veterinary healthcare.
C_LIO_LIAs one of the last-line antibiotics, polymyxin must be conserved by containing the spread of polymyxin resistance.
C_LIO_LIDetecting colistin resistance relies on determining colistin MIC values by standard broth microdilution, which is labour-intensive with longer turnaround time (TAT).
C_LIO_LIOther polymyxin resistance diagnostics have been developed to augment or replace the broth microdilution with faster TAT.
C_LIO_LIBased on their respective sensitivities, specificities, TAT, skill, and cost, selected phenotypic and molecular assays are recommended for laboratories, according to their financial strengths, to enhance colistin resistance surveillance and control.
C_LI | infectious diseases |
10.1101/2020.08.24.20180844 | Inferring person-to-person networks of Plasmodium falciparum transmission: is routine surveillance data up to the task? | Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. We evaluated the influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria. We found that these data types have limited utility for inferring transmission networks and may overestimate transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, Rc. Applying this approach to data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution. | epidemiology |
10.1101/2020.08.24.20180455 | Asynchronous influenza vaccination and adverse maternal-child health outcomes in the Brazilian semiarid, 2013 to 2018: the INFLUEN-SA Study | Recent models indicate seasonal influenza transmission in Brazil begins annually in the semiarid state of Ceara (pop. 8.8M)--before vaccine campaigns begin. To assess the extent and maternal-child health consequences of this misalignment, we tracked severe acute respiratory infections (SARI), influenza, and influenza immunizations from 2013-2018. Of 3,297 SARI cases, 145 (4%) occurred in pregnancy. Vaccine coverage was >80%; however, campaigns often occurred during or after peak influenza. Birth weights nadired and prematurity increased 30-40 weeks following peak influenza, by a magnitude of 40g and 10.7% to 15.5%, respectively. We identified 61 babies of mothers with gestational SARI; they weighed 10% less at birth (P = 0{middle dot}019) and were more often premature (OR: 2.944; 95% CI: 1.100 - 7.879) relative to controls (n=122). Mistiming of influenza vaccination adversely impacts pregnancy and birth outcomes in Ceara, with critical implications for influenza transmission dynamics nationally. | public and global health |
10.1101/2020.08.24.20181339 | Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning | The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. | health informatics |
10.1101/2020.08.25.20181487 | COVID-19 hospitalisation rates rise exponentially with age, inversely proportional to thymic T-cell production | Here we report that COVID-19 hospitalisation rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R2=0.98). This mirrors the well studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by MRSA, MERS-CoV, West Nile virus, Streptococcus Pneumonia and certain cancers, such as chronic myeloid leukemia and brain cancers. In addition, incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesised that the age-dependence does not come from social-mixing patterns, i.e. that the probability of hospitalisation given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalisations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have less contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of less contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49-75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age-dependence of hospitalisation rates is the probability of hospitalisation given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output, and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well. | infectious diseases |
10.1101/2020.08.24.20181248 | Preliminary report on the development of a colorimetric loop-mediated isothermal amplification diagnostic assay for deer tick virus | Deer tick virus (DTV) is an emerging pathogen in North America. This virus can cause nervous system complications such as encephalitis in humans. Further, no data has been surmounted around long-term effects of infection from DTV patients across variable age groups. Diagnostic tools of DTV used by government laboratories are based on RT-PCR using patient serum or ticks. This paper explores the feasibility of a colorimetric loop-mediated isothermal amplification (LAMP) assay to create a point-of-care diagnostic methodology for use in field and in primary care. LAMP consists of six primers that bind to target DNA and amplifies variable length nucleotide strands that can be visualized through side reactions or via electrophoresis. First, a viable LAMP primer set, and a primer set that dimerizes and amplifies DNA regardless of compatibility were created in silico and validated in vitro. Then, a specific LAMP assay was developed. Our findings showed his method can be performed within 30 minutes and can measure with limits of detection comparable to PCR. | infectious diseases |
10.1101/2020.08.25.20182071 | Effectiveness of Localized Lockdowns in the SARS-CoV-2 Pandemic | Non-pharmaceutical interventions, such as social distancing and lockdowns, have been essential to control the COVID-19 pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention to prevent viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared to larger-scale suppression strategies. Using an integrated dataset from Chile (March 3 through June 15, 2020) and a novel synthetic control approach, in this paper we estimate the effect of localized lockdowns, disentangling its direct and indirect causal effects on SARS-CoV-2 transmission. Our results show that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down the pandemic; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas. | epidemiology |
10.1101/2020.08.25.20182071 | Effectiveness of Localized Lockdowns in the SARS-CoV-2 Pandemic | Non-pharmaceutical interventions, such as social distancing and lockdowns, have been essential to control the COVID-19 pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention to prevent viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared to larger-scale suppression strategies. Using an integrated dataset from Chile (March 3 through June 15, 2020) and a novel synthetic control approach, in this paper we estimate the effect of localized lockdowns, disentangling its direct and indirect causal effects on SARS-CoV-2 transmission. Our results show that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down the pandemic; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas. | epidemiology |