COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios.We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs.COVID-19 clinical management costs vary greatly by country, ranging between <0.1%–12% of GDP and 0.4%–223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10–US$1.32.We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed. The COVID-19 pandemic has ravaged every corner of the world and resource needs of clinical management are estimated to be high: published global annual estimates range from US$130 billion to US$1 trillion and monthly estimates for low-income and middle-income countries (LMICs) range from US$33 to US$61 billion.While useful for budgeting purposes, currently available cost estimates of clinical management largely report incremental financial costs based on normative guidelines which are not adequate for priority setting.We present the first country-specific set of full economic ‘real-world’ costs of COVID-19 clinical management for 79 LMICs which are urgently needed for cost-effectiveness analyses of vaccines, as well as to inform the redesign of essential services packages and to feed into discussions on the balance between broader macroeconomic costs of mitigation strategies and the costs to the health system.Average COVID-19 costs vary greatly by country and scenario of social distancing, making up between <0.1%–12% of gross domestic product and 0.4%–223% of total annual health expenditure (excluding out-of-pocket payments).Without mitigation policies, average COVID-19 clinical management costs per capita range from US$43.39 to US$75.57, surpassing total annual health expenditure in 22 out of 76 LMICs; in a scenario of moderate social distancing, these drop to US$38.52—US$58.08 and in one of stringent social distancing to US$1.10–US$1.32.Health sector costs of COVID-19 are substantial in LMCIs, even when assuming lower-cost critical care options.High levels of social distancing by the general population throughout the year would greatly reduce costs to the health system; as social distancing is relaxed, emergency financial support will be needed.COVID-19 was declared a public health emergency of international concern by the World Health Organization (WHO) in January 2020.A limited number of studies have explored country-specific unit costs and total costs of COVID-19 clinical management in high-income settings14–16While the abovementioned global studies are key to mobilising resources for COVID-19, they either present incremental financial costs, exclude broader health systems costs or assume a normative approach to resource use unlikely to be followed in LMICs faced with severe resource constraints. Full economic costs of a broader COVID-19 health system response, including ‘real-world’ plausible estimates of service delivery in LMICs under different mitigation scenarios, are urgently needed to inform the priority-setting process. These costs will be required to understand the cost-effectiveness of novel COVID-19 curative and preventive interventions, including those focused on vaccination, as well as to define the extent to which essential services can be maintained during the pandemic. Further, full economic cost data are needed to inform policy choices seeking to balance the broader macroeconomic costs of mitigation strategies against the costs to the health system from the disease. Lastly, country-specific resource estimates are needed to highlight the gaps between currently available financial resources and those which would be required for adequate care and treatment of COVID-19 in LMICs. Such estimates may further contribute to country-specific resource mobilisation efforts.This paper presents the first estimates of full economic costs of the COVID-19 response to health systems in LMICs taking a ‘real-world’ approach under different pandemic mitigation scenarios over a 12-month period.We used country-specific epidemiological projections from a dynamic transmission model which estimated total numbers of cases, days of hospitalisations and deaths under different mitigation scenarios. We defined the health sector response for three different LMICs in detail , using a combination of guidelines and expert opinion. We used local resource use and price data from a range of primary and secondary data sources. We then extrapolated costs across LMICs at similar income levels. Lastly, we compared cost estimates against country-specific measures of gross domestic product (GDP) and health expenditure. Greater details on our methods can be found in the 10.1136/bmjgh-2021-005759.supp1Supplementary datahttps://cmmid.github.io/topics/COVID-19/LMIC-projection-reports.html). For each country, the model produces different projections of the number of clinical cases, number of required days in hospital for severe cases  and critical cases (intensive care unit (ICU)) and deaths for 57 distinct mitigation scenarios that may occur over a 1-year period.17Our estimates of COVID-19 cases for different scenarios come from the CovidM epidemiological model, produced by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine, which projects the health impact of COVID-19 for 92 LMICs : (a) emergency response mechanisms at the national level; (b) risk communication and community engagement; (c) case finding, contact tracing and management; (d) surveillance; (e) public health measures (hygiene education); (f) screening and diagnosis  and (g) case management.To estimate the average unit costs for each activity, we used an ingredients-based costing approach.We conducted our costing based on global guidelines. However, we adapted the level of COVID-19-specific resource needs to take into account feasibility in LMIC contexts by a combination of reviewing COVID-19 resource planning tools and budgets and scoping literature searches for primary data on clinical care practices in LMICs. Clinical management resource use estimates were adapted based on informal consultations on low-cost critical care options, including an estimation of oxygen therapy needs see . While wTo generate costs for other LMICs, we extrapolated our detailed unit cost estimates for Ethiopia, Pakistan and South Africa to LICs, lower-MICs and upper-MICs, respectively, based on country-specific epidemiological and health systems data and standard approaches to adjusting prices. In effect, the one constant element between countries is the model of care, with all other aspects of costs adjusted using national-level data in each of the 79 countries.Each cost input in the ingredients costing was classified as a tradeable good, non-tradeable good or staff cost.Non-tradeable goods cannot be easily traded in international markets and generally need to be consumed in the country where they have been produced . To convert these, we multiplied the proportion of the unit cost that was defined as non-tradeable (in 2019 US$) by the ratio between the 2019 GDP per capita  of the second country and the 2019 GDP per capita (adjusted for PPP) of the base country. Data on GDP per capita (adjusted for PPP) were found in the World Bank database.26To convert staff costs from a base country to a second country, we used conversion rates from a regression analysis on wages of health workers for 193 countries to predict wages by country income category relative to GDP per capita. We estimated the number of working hours for nurses, doctors and other medical staff and applied GDP per capita multipliers in order to value their time.27We calculated unit costs per activity for a total of 129 LMICs, as well as a mean unit cost per activity per country income category  weighted by population.Unit costs per activity were multiplied by the number of activities expected in each country, in some cases driven by the epidemiological estimates  and in others by fixed time and geographical area . Since scenario 1 models an unmitigated epidemic only clinical management costs were included. See While an effort was made to ensure that the resource use costed is feasible in LMICs, our total cost estimates assume that every patient with severe or critical disease will be hospitalised regardless of existing hospital bed capacity. In other words, we estimate total resource needs regardless of current country-specific non-financial constraints.Total country-level costs were estimated for 79 LMICs where we had epidemiological estimates. The 79 countries have a combined population of more than 3.98 billion people, which accounts for 60% of the total population of LMICs.Estimates of annual cost per capita of each scenario in each country were estimated by dividing total costs by the population of each country. These were compared against: (1) country-specific GDP per capita, (2) national health spending  per capita, (3) national health spending (including OOP expenditure) per capita and (4) government health expenditure per capita in 76 LMICs where relevant data were available,Data on the percentage of symptomatic cases tested with PCR were unavailable and so our assumed base case estimate (10%) was considered highly uncertain. Consequently, we performed a deterministic sensitivity analysis by increasing this value between 20% and 100%.While there was no patient involvement in our research, we consulted several actors involved in policy-making in LMICs to ensure our work was useful in national-level decision-making.Country-specific unit costs can be found in 10.1136/bmjgh-2021-005759.supp2Supplementary dataMean costs per capita per country income group weighted by population are presented in In all scenarios, the largest cost drivers were screening and diagnosis and case management. Costs of screening and diagnosis were particularly substantial for LICs, accounting for 51.62%–59.47% of total costs, and less substantial for upper-MICs, accounting for 20.10%–26.45% of total costs. Conversely, the costs of case management were particularly substantial for upper-MICs , and less so for LICs (37.75%–48.38%). Most of the costs of case management are related to hospital-based critical care (>76% across country income groups and scenarios). Case finding, contact tracing and surveillance and public health measures in contrast made up less than 4% of the total response costs (in scenarios 2–4), see The maps in COVID-19 costs per capita as a percentage of GDP per capita are highest in scenario 1 (unmitigated epidemic) and scenario 4  across all countries, ranging from 1.43% in Eswatini to 11.85% in Burundi (both scenario 4). They were consistently lowest in scenario 3 : between 0.03% in Angola, Bolivia and Ghana and 0.83% in Zimbabwe.Likewise, COVID-19 costs as a percentage of health expenditure were highest in all countries in scenarios 1 and 4: 23.35%–216.36% and 23.42%–222.34% of total national health spending excluding OOP payments, respectively; 14.68%–171.29% and 15.68%–183.51% of total national health spending including OOP payments, respectively, and 35.15%–1344.28% and 38.24%–1451.34% of government health spending, respectively. Lowest proportions were observed in scenario 3: 0.38%–18.96% of total national health spending excluding OOP payments, 0.26%–15.64% of total national health spending including OOP payments and 0.68%–40.78% of government health spending.We found that estimates were highly sensitive to our assumptions on the number of symptomatic cases tested for scenarios 2–4 see . IncreasWe provide the first set of country-specific full economic cost estimates of COVID-19 management in LMICs, from a health sector perspective. The countries included in our study account for 60% of the total population of LMICs.We find that the costs to the health sector of responding to COVID-19 are substantial in LMICs, even when assuming lower-cost critical care options. High levels of social distancing by the general population throughout the year (scenario 3) would greatly reduce costs compared with a policy of allowing the pandemic to proceed unmitigated (scenario 1), but also, importantly, compared with scenarios leading to moderate levels of social distancing (scenarios 2 and 4).Our findings suggest that the total number of cases is highest in a policy scenario of no mitigation (scenario 1). However, the highest total costs vary by country between scenario 1 and scenario 4 (30-day lockdown followed by minor reductions in contacts). While scenario 4 has a lower number of total cases, costs are comparable to those in scenario 1 because scenario 1 only accounts for clinical management costs and excludes the costs of any mitigation strategies, including testing beyond severe and critical cases in hospital. These results should not be taken as an endorsement that it is preferable, from a financial perspective, to have no mitigation strategy over a strategy of limited social distancing. A no mitigation strategy may result in slightly lower costs of COVID-19 management in some settings but would have substantial knock-on effects on costs and outcomes for other health interventions not quantified in our study.We compared total COVID-19 costs per country across the four epidemiological scenarios against GDP per capita and three metrics of national-level health expenditure per capita: total national health expenditure with and without OOP payments and government health expenditure. We found that while some countries are likely able to absorb the costs (particularly in scenario 3), even moderate levels of social distancing would lead to high levels of the health spending being directed towards COVID-19 in nearly all countries. In scenario 2, for example, 74 out of 76 countries would need to direct more than 20% of their total national health expenditure (excluding OOP costs) to COVID-19.COVID-19-related costs would exceed total health spending in several countries, although this varies by expenditure metric examined and epidemiological scenario. COVID-19 costs could exceed total expenditure in between 8 and 11 countries out of 76 when compared against total national health expenditure including OOP. This figure increases to 18–23 countries when compared against total national health expenditure excluding OOP and to 52–54 countries when compared against government health expenditure only. This highlights that in many countries, OOP expenditure and non-government sources of health expenditure, such as donor funding, may play a critical role in covering the costs of the COVID-19 pandemic. As the global macroeconomic situation deteriorates due to the pandemic and overseas development aid is reduced, international agencies and donor nations need to be aware of the potentially catastrophic consequences of reducing funding towards health services in LMICs and the consequent impact on OOP expenditure.No country is expected to exceed total health expenditure under scenario 3. However, in eight countries, COVID-19 costs are projected to exceed spending across all three health expenditures metrics for scenarios 1, 2 and 4: Burundi, Central African Republic, Democratic Republic of Congo, Ethiopia, The Gambia, Madagascar, Mozambique and Tanzania. These countries, all LICs located in sub-Saharan Africa, are therefore particularly vulnerable and will likely require considerable financial support.Further research is required to better understand the effect that shocks of this magnitude have on the health system and, particularly, on essential services. It is crucial to understand which services are most vulnerable to being displaced and the levels of funding required to ensure their continued provision, as well as identifying which non-urgent services can be temporarily delayed without causing lasting impact.We carried out a costing from the perspective of the health sector focusing exclusively on COVID-19. We have not quantified the health impacts or costs of deferring other key health sector activities. Further, while high levels of social distancing would lead to better COVID-19-related health outcomes and lower health sector costs, they may also imply economic losses in other sectors as well as social and other non-economic consequences. Decision-makers should consider all these factors when debating COVID-19 mitigation policies.et al,et al included a larger population in their analysis . While the scope of our costing was narrow and focused largely on clinical activities, Tan-Torres Edejer et al also included non-clinical interventions . Lastly, Tan-Torres Edejer et al assumed higher remuneration of staff during the pandemic by including hazard pay as per international guidelines, whereas we assumed staff salaries would remain constant with prepandemic salaries.Our unit cost estimates per day of hospitalisation for severe and critical cases are broadly in line with those published in the literature for LMICs.The two activities with highest proportion of costs in our analysis were screening and diagnosis and case management. Screening and diagnosis costs accounted for a particularly high percentage of total costs in LICs (over 50% of in some scenarios). We assumed all testing across all settings would be PCR based, which led to relatively high unit costs. As the pandemic evolves, we expect less resource intensive diagnostic technologies with adequate accuracy to replace PCR, leading to lower overall costs. Our total costs were highly sensitive to variations in testing scale-up: a 10% increase in the proportion of symptomatic cases tested led to increases of up to 18% in total costs. Country-specific data on numbers of people tested are needed to better calibrate our cost model.Case management costs were high across settings, particularly in upper-MICs. Healthcare staff salaries made up a large proportion of the costs per day in hospital, particularly for critical cases; higher unit costs in upper-MICs are a product of comparatively higher staff salaries in these settings. Our cost model already assumes a conservative staff-to-patient ratio. We do not expect costs per day of hospitalisation to drop considerably unless this ratio is further reduced. With the advancement of new therapeutics, costs per day of hospitalisation may increase; however, the costs per case treated may decrease if new therapeutics allow for faster patient recovery and reduce length of hospital stay required.We aimed to calculate overall resource needs, so our total costs assume that all severe and critical patients will be hospitalised. However, this is currently unlikely in certain settings, particularly in LICs, as not all those who need care will be able to access it; ICU capacity remains extremely limited in many settings.We did not account for country-specific short-run health system constraints. Accurately quantifying resources constraints, particularly those related to critical care at a global level, is difficult as many components are required . It is important for policy-makers to measure and consider these constraints in a country-specific manner when making allocative decisions between different health needs, acknowledging that some resources  cannot normally be relaxed at scale in the short term, even with additional funding.While we did not factor access to care in our calculations, we did estimate resource use levels that were considered feasible in LMICs: we assumed, for example, that only one-third of critical cases would receive mechanical ventilation and the other two-thirds would receive other methods of oxygen supplementation. However, such respiratory support is complex and many LMICs may struggle to provide it even in lower quantities. What constitutes a ‘feasible strategies of service delivery’ will inevitably vary between LMICs, but our estimates suggest that, in many settings, even service delivery that is comparatively less resource intensive would lead to very high health sector costs.Other essential (and less costly) critical care options may be more realistic in certain settings.Our methods are subject to several important limitations. First, we rely on epidemiological projections modelled at the start of the epidemic. However, recent data from several settings, notably sub-Saharan Africa, suggest lower numbers of cases and fewer deaths than projected by models see .2 The exFurther, the case numbers that our estimates rely on cover a range of possible epidemic dynamics; one given scenario is unlikely to match the real case numbers currently being observed across all LMICs. Differences between reported and modelled estimates may also be explained by the fact that mitigation policies in place through the pandemic have varied over time in most countries,Second, ‘real-world’ costs are ideally estimated by collecting extensive primary cost data on actual service delivery. We have not been able to do this for COVID-19 and therefore relied on data collected for other purposes and on expert opinion from LMICs to make key assumptions on how services may be delivered. We only used three countries estimates to extrapolate to other settings.The length of hospital stays necessary for severe and critical cases used in the epidemiological models were based on evidence from early in the pandemic. As more data become available on length of stay and treatment options improve, epidemiological and costing models should be revisited. Service uptake and health-seeking behaviour may also differ by setting and should be considered in further work.Third, our work focuses on a narrow set of health sector COVID-19 interventions. We do not include the costs of protecting healthcare workers delivering other essential services outside the COVID-19 response  or COVID-19-related costs beyond the health sector , which may be considerable but were beyond the scope of our analysis.Despite these limitations, our work provides several critical qualitative recommendations for those working in COVID-19 policy-making. First, it is imperative that global agencies and funders continue to ensure sufficient targeted resources are available for LMICs to respond as the pandemic evolves, with most LMICs expected to shift substantial amounts of funding to COVID-19, even with policies of moderate social distancing in place. While much of the focus is on the macroeconomic impact and mortality impact of COVID-19, the fiscal impact on the health sector is likely to be substantial. LICs are particularly vulnerable and some will struggle to cope with almost any COVID-19 scenario. When thinking through mitigation strategies, decision-makers should consider the macroeconomic implications alongside associated potential reductions in healthcare-related costs, including patient costs.Second, in thinking through resource needs, it is important for countries to re-evaluate interventions and adapt response measures in ways that are context appropriate, affordable and sustainable, particularly in relation to high-cost activities, namely screening and testing and hospital-based care. This could include intervention delivery re-design and adaptations such as integration of care, leveraging of community health workers and home-based care, better targeting of interventions such as testing, and lower cost diagnostic approaches and critical care, among other ideas.Finally, while our results reflect the myriad decisions about care, protection and patient experience that are required to plan resource use, there is little discussion or data on what is feasible in LMICs. This is a task that cannot be met using a global perspective but needs country-specific inputs to reflect the health system characteristics of each country. We therefore also call for urgent support to encourage interaction of economists, planners, service managers and epidemiological modellers to inform COVID-19 policy at the country level across LMICs.We present the first dataset of COVID-19 clinical management costs across LMICs. These data can be used for cost-effectiveness analyses of prevention strategies, notably vaccines, and can assist policy-makers understand trade-offs between essential services as well as inform discussions on the balance between broader macroeconomic costs of mitigation strategies and health sector costs. We find that COVID-19 clinical management costs are substantial in LMICs, even in scenarios of moderate social distancing and assuming lower-cost critical care options. LICs are particularly vulnerable and some will struggle to cope with almost any COVID-19 scenario. As social distancing is relaxed, emergency financial support will be needed. The choices facing LMICs are likely to remain stark.


Enterovirus genus of the Picornaviridae family, inter-species recombination is rarely observed in nature. Recent studies have shown recombination is a ubiquitous process, resulting in a wide range of recombinant genomes and progeny viruses. While not all recombinant genomes yield infectious progeny virus, their existence and continued evolution during replication have critical implications for the evolution of the virus population. In this study, we utilised an in vitro recombination assay to demonstrate inter-species recombination events between viruses from four enterovirus species, A-D. We show that inter-species recombinant genomes are generated in vitro with polymerase template-switching events occurring within the virus polyprotein coding region. However, these genomes did not yield infectious progeny virus. Analysis and attempted recovery of a constructed recombinant cDNA revealed a restriction in positive-strand but not negative-strand RNA synthesis, indicating a significant block in replication. This study demonstrates the propensity for inter-species recombination at the genome level but suggests that significant sequence plasticity would be required in order to overcome blocks in the virus life cycle and allow for the production of infectious viruses.Positive-strand RNA virus evolution is partly attributed to the process of recombination. Although common between closely genetically related viruses, such as within species of the  Recombination is a common process amongst positive-strand RNA viruses and is a strong driver of virus evolution through the exchange of genomic sequences that can be directly advantageous or result in the removal of deleterious mutations ,2,3. TheEnterovirus genera are the largest of the Picornaviridae family, consisting of 15 species—Enterovirus A-L and Rhinovirus A-C—and over 300 characterised viruses [pro, 2B, 2C and 3A, 3BVPg, 3Cpro, and 3Dpol from P2 and P3, respectively. Poliovirus (PV), the prototype Enterovirus C species, is a well-established tool for the study of recombination. The virus undergoes rapid recombination between serotypes 1 and 3 (PV1 and PV3) in recipients of the live-attenuated oral vaccine [Enterovirus genera can occur [The  viruses . All ent vaccine ,8, as we vaccine ,10. Theran occur ,12, and an occur , althougan occur ,15.cis-acting RNA structure in the 2C coding region [As recombinants are relatively rare, when compared with the non-recombinant progeny of parental viruses, studying the process requires the ability to preferentially isolate recombinants. To this end, we developed a poliovirus-based in vitro recombination assay—the CRE-REP assay—that only allows the recovery of recombinant progeny viruses ,16,17,18g region  that reng region . The dong region  demonstrIn this study, we have utilised the CRE-REP assay to investigate the capacity for inter-species recombination within the coding region. Using assays for four different enterovirus species—A to D—we show inter-species recombination occurs readily at the VP1/2A (P1/P2) gene boundary. However, we found that the generation of recombinant genomes was not reflected in the production of infectious progeny virus. Using a constructed cDNA of one of the inter-species recombinants identified, we then demonstrated that the inability to yield progeny virus was due to a restriction in de novo positive-sense RNA synthesis. We propose that recombination can occur freely at the genome level and is not bound by species classification, but the production of a viable virus is restricted by the incompatibility of one or more components of the virus replication cycle.2, in Dulbecco’s modified Eagle medium  supplemented with 10% heat-inactivated FBS .MRC-5  and H1299  cells were maintained at 37 °C, 5% COPfu polymerase  with an initial denaturing at 95 °C for 1 min, followed by 30 cycles of 95 °C for 30 s, 50 °C for 30 s and 72 °C for 2 min/kb, and a final extension at 72 °C for 10 min.The CRE-REP assay used to generate recombinant viruses has been described previously  and was 50 on H1299 cells.Plasmids were linearised at the 3′ end of the virus sequence and RNA transcribed using a HiScribe T7 High Yield RNA Synthesis Kit , following the manufacturer’s protocol. RNA transcripts were DNaseI  treated to remove residual template DNA and column purified using a GeneJET RNA Purification Kit  prior to spectrophotometric quantification. For all CRE-REP assays, equimolar amounts of both template RNAs (based on 250 ng of acceptor) were prepared with Lipofectamine 2000  in a 3:1 Lipofectamine 2000:RNA ratio as per manufacturers’ protocol and transfected into 80% confluent MRC-5 or H1299 cell monolayers. The supernatant was recovered at 72 h post-transfection for virus quantification by plaque assay on MRC-5 cells, or TCIDg for 3 min. RNA was reverse transcribed at 42 °C using oligo dT, or EV68 specific primer (EV68(-)), and SuperScript II reverse transcriptase  as per manufacturers’ protocol. The region of recombination (VP1 to 2C) was amplified using virus-specific primers  following 3 cycles of freeze–thawing and clarification by centrifugation at 10,000×  primers  and Taq PCR products of recombinants from CRE-REP assays were analysed by Sanger sequencing , and the recombination junctions were determined by aligning against parental reference sequences using SnapGene software v4.0.8.PCR products were cloned using the pGEM-T Easy Vector System  as per manufacturers’ instructions, with positive colonies selected by blue/white screening and sequenced with M13 forward and reverse primers.The extreme rarity of inter-species recombinants isolated in nature suggests that recombination outside of the 5′ UTR is severely restricted between viruses of different species. We sought to formally test this theory and investigate the potential for inter-species recombination using CRE-REP assays adapted for multiple enteroviruses. We utilised a range of available replicon donor and CRE mutant acceptor templates covering enterovirus species A–D, while CRE mutants representing species A and species D were constructed de novo for this study . The nom50 assay on fresh MRC-5 or H1299 cells, respectively. Cells were subsequently co-transfected with equimolar amounts of template RNAs, in duplicate, and incubated for 72 h prior to harvest of cellular supernatant and the presence of infectious recombinant progeny virus was determined by assay, as above.With the five donor and four acceptor templates available, a total of five intra-species control assays, and fifteen inter-species CRE-REP assays could be performed. Of the enteroviruses selected for this study, wild-type EV-D68 and EV-D70 have an optimal growth temperature of 33 °C, compared with 37 °C for all other selected viruses. In order to provide the maximal conditions for the growth of potential recombinant viruses, CRE-REP assays were carried out at both 33 °C and 37 °C, and in two cell lines—MRC-5 and H1299 cells—previously tested and demonstrated to support the full replication cycle of each of the wild-type enteroviruses. As expected from previous studies demonstrating that CRE mutations do not revert , transfe50 assay. The absence of infectious progeny was supported by the inability to amplify recombinant sequences from purified extracellular RNA using RT-PCR and virus-specific primers that target the P1/P2 junction  of poteTo further investigate the potential recombinant RNA populations, we first focused on the species D intra-species assay of EV70/rep + EV68/CRE, which did not generate a viable virus. Although there is only approximately 75% nucleotide sequence identity between EV-D70 and EV-D68, this is not dissimilar to the ~80% identity between PV1 and PV3 which recombine freely [To analyse potential recombinant RNAs, we utilised PCR cloning to isolate products following RT-PCR. Due to the variety of products amplified for some samples, and the presence of multiple low-molecular-weight products that would impact the range of DNA fragments cloned, we limited our analysis to samples showing more prominent bands at the approximate expected size for precise, genome-length recombinants which, in the case of the primer pair used for EV-D70 and EV-D68, was 1.3 Kb. Entire PCR reactions, amplified from MRC-5 cells at 33 °C, transfected with EV70/rep and EV68/CRE RNA, were TA cloned, blue/white screened for positive colonies, and subject to repeat PCR amplification of the recombination region. Products under 500 bp were excluded from further analysis, as these were deemed unlikely to correspond to viable recombinants. Sanger sequencing of a small number of positive clones identified the presence of three unique recombinant sequences between EV-D68 and EV-D70 .The genomes of all three recombinants had imprecise junctions spanning the VP1/2A boundary and contained insertions of 210, 363, and 420 nt, respectively, meaning that all genomes were in-frame and therefore, theoretically, replication competent. The junction of the first identified recombinant, EV68/EV70-1, occurred at a short region—3 nt—of complete sequence identity between the parental RNA templates, resulting in an ambiguous junction in which it was not possible to determine the exact position at which crossover occurred , polyprotein processing defects, or an inability to undergo packaging. We investigated one of these potential reasons and found that for the species D/C recombinant EV68/PV1-1 A, positiOur analysis of inter-species enterovirus recombination was not intended to be exhaustive but to instead investigate the presence and basic features of any recombinant genomes generated. We have clearly demonstrated that recombinant genomes with crossovers within the coding region are generated even though infectious progeny virus is absent. Based on the data available, including the analysis of EV68/PV1-1, it is likely that interspecies recombinant genomes are, at least partially, replication competent. Firstly, it was notable that all recombinant genomes were located at the VP1/2A boundary separating the P1 and P2/3 ‘modules’ that encode the structural and non-structural proteins, respectively. This is compared with our previous studies in which we found 55–70% of junctions spanned the VP1/2A boundary with the remainder located at the 2A/2B gene boundary ,27 and iThe isolation of recombinants with junctions at the boundary between the structural and non-structural modules suggests there may be a risk of generating novel viruses with altered tropism, as was determined for SARS coronavirus ,36. Of c


Indirect costs, based on productivity losses by participants or caregivers, accounted for 52% of the cost of illness. Inherited ataxia is associated with significant health and social care costs. Further funding for inherited ataxia to ease the financial burden on patients, caregivers and healthcare system and improve standards of care compliance is warranted.Inherited ataxias are a heterogenous group of neurodegenerative disorders characterised by progressive impairment of balance and coordination, typically leading to permanent and progressive disability. Diagnosis and management of these disorders incurs a range of direct and indirect financial costs. The aim of this study was to collect individual ataxia-related healthcare resources in a large cohort of individuals with different subtypes of inherited ataxia and calculate the associated cost of illness in the Republic of Ireland. One hundred twenty-nine respondents completed a cross-sectional study on healthcare resource utilisation for progressive ataxia in Ireland. Costs were calculated using a prevalence-based approach and bottom-up methodology. The COI for inherited ataxia in 2016 was €59,993 per person per year. Results were similar between participants with Friedreich’s ataxia (FRDA,  The inherited ataxias are rare, clinically and genetically heterogeneous neurodegenerative disorders, characterised by slowly progressive gait instability, often in combination with additional neurological and non-neurological clinical features. These features often necessitate extensive personal and medical care.In Ireland, a small number of specialist neurologists manage the majority of people with inherited ataxia. Provision of disease monitoring and clinical care can therefore be challenging. Friedreich’s ataxia (FRDA), the most common inherited ataxia , has an Establishing an inherited ataxia diagnosis is often challenging and requires comprehensive clinical assessment and investigations. These include magnetic resonance imaging (MRI), basic and advanced genetic testing and other investigations as clinically indicated  . These iFollowing initial presentation and diagnostic evaluation, the disease typically progresses and costs continue to grow. Patients require regular specialist review and investigations such as blood tests and echocardiography. Input from other healthcare professionals (OHPs) including physiotherapy (PT), occupational therapy (OT), speech and language therapy (SALT) is commonplace. Physical disability results in high personal care requirements and many patients ultimately become wheelchair-bound. Associated comorbidities such as diabetes, cardiomyopathy and scoliosis require care under various medical and surgical specialties and patients are at high risk of hospital admission due to acute illness.The appropriate care of any person with inherited ataxia therefore places a significant burden on health and social care systems, as well as the quality of life of patients and caregivers. This burden translates into financial costs, both direct (due to necessary utilisation of services) and indirect (productivity losses due to patients and caregivers being unable to work).The aim of this study is to estimate the COI of inherited ataxia in the Republic of Ireland by collecting real-life data from a large cohort of patients on their utilisation of healthcare resources. These results are intended to direct management strategies aimed towards the appropriate allocation of resources and minimising of financial burden on the healthcare system and individuals with this chronic progressive condition.This cross-sectional observational study was conducted in 2016. Participants were recruited in the National Ataxia Clinic at Tallaght University Hospital, through surveys posted to Ataxia Ireland members (a charitable organisation for people with inherited ataxia) or distributed at patient meetings nationwide and through a link available via the Ataxia Ireland website. Completion of the survey could be assisted by another individual, where appropriate. Participants of any age with suspected or confirmed genetic ataxia were included. Those with acquired forms of ataxia were excluded. Participants were assigned to one of the three subgroups: FRDA, Non-FRDA (for those who reported confirmed genetic diagnosis different from FRDA) and Unknown .n = 5). Modifications were made on the basis of participant feedback to improve survey’s ease of use.The survey was designed and tested in a pilot study , utilisation of services and claiming of financial supports. Illness-related absences from work were assessed using the Work Productivity and Activity Impairment questionnaire (WPAI) .The recall period was 12 months, apart from the frequency of clinical investigations and the use of assisted transport, which were for a period of 6 months. The recall period for the WPAI was 7 days.Frequency of service use was recorded in pre-defined frequency options .The unit costs were estimated through the sources denoted in Table General Practitioner (GP) visit costs for participants with medical cards were based on capitation rates, formed on age and gender, and not frequency of visits . For patMedication use was recorded according to medication class . Costs of medication classes were calculated based on clinical judgement, taking conservative estimates on the most commonly prescribed and cheapest medication likely to be used in each class, assigning generic prices where possible.Costs of genetic testing, where the exact test was not specified, were based on common clinical practice. FRDA and Non-FRDA participants (i.e. those with a known genotype) were assumed to have been tested for that specific gene. Participants in the ‘Unknown’ group were assumed to have undergone initial screening tests for the most common ataxias  .OHP visit costs were taken from the median point on relevant salary scales , assuminThe costs of walking aids and medical devices were annualised assuming a 5-year lifespan and a 4% discount rate .The cost of car modifications was based on the average value added tax refund claim on the disabled driver and passenger scheme .The cost of long-term care facility residence was based on the average national expenditure on care home beds in 2016 .Productivity losses were calculated using a human capital approach. Hours lost as a result of absenteeism or inability to work due to ataxia over the previous week was costed according to the national gross mean hourly wage in Ireland. Those forced to retire due to ataxia were also assumed to incur 40 hours a week of productivity losses unless they were over the retirement age of 65. Informal care costs were calculated similarly, based on the number of hours of care provided, capped at 40 hours per week. The age of caregivers was not recorded so all were assumed to incur indirect costs.The hourly cost of professional care was based on national budget projections . If careUnit costs were inflated to 2016 prices where necessary, using the consumer price index available through the Central Statistics Office of Ireland.The mean COI for all participants was calculated using a prevalence-based approach and bottom-up methodology, i.e. by first measuring and quantifying the utilisation of individual services over a 12-month period, then multiplying these quantities by the associated unit costs for each participant . If a frFor recall periods less than a year, costs were annualised on the assumption that requirements were similar throughout the remaining year.Costs were distributed between public (i.e. paid for by the state) and private (paid for by the participants) funding. Participants with medical cards, a means-tested support which covers costs of medical care, were assumed to have free access to all medical services and medications. Those without medical cards were assumed to have paid contributions towards inpatient hospital stays, emergency department (ED) visits and medications. Participants reported whether their home modifications were funded privately or publicly. Long-term care facility costs were assumed to be publicly funded as these costs could not be accurately distributed according to means testing. Transport costs to clinic were assumed to be funded privately. Indirect costs were assumed to be funded privately, minus any financial benefits  which were costed according to 2016 rates . Other bStatistical analysis was performed using IBM® SPSS® version 25. The data was checked and cleaned. Descriptive statistics regarding frequency of service usage are presented using means or medians and 95% confidence intervals. Mean costs are presented for individual cost-unit categories and for overall direct , indirect and total costs. 95% confidence intervals for mean costs were calculated using non-parametric bootstrapping due to the skewed nature of data distribution.Demographic differences between ataxia subgroups and differences in cost according to selected demographic predictors were assessed for significance at the 0.05 level using appropriate statistical tests and post hoc analyses.Missing data was excluded unless it could reasonably be inferred for the purposes of a more accurate cost analysis. Nine of 36 hospital stays did not have a documented length of stay (LOS). Therefore, these cases were assigned the average LOS of other visits. Three participants did not report the number of hours per day that they received homecare. They were assumed to have received 1 hour per day.Costs of informal or combined informal-professional care were reduced by 20.2%, the proportion of registered informal carers nationwide greater than 65 years of age in 2016 .All informal care hours were assumed to be 26.15 hour per week, the average number of paid work hours lost by informal carers to FRDA patients in a similar UK study . PatientThe €75,000 cap to care costs was removed.Each hospital inpatient stay was costed at €6841, the national average in 2016 , rather Home and car modification costs were varied by 25%.The following sensitivity analyses were performed on variables in which there was felt to be a significant degree of uncertainty:Ethical approval was obtained from the Tallaght University Hospital/St. James’ Hospital Joint Research Ethics Committee.n = 4), ataxia with oculomotor ataxia type 1 (n = 2), X-linked tremor/ataxia syndrome (FXTAS) (n = 2), episodic ataxia (n = 1), hereditary spastic paraplegia , SPG54-spastic ataxia (2) and 1 unspecified), SCA  and SCAR10 (n = 1). Of the 129, 55 were classified as Unknown.One hundred thirty-four surveys were returned, 85 on paper and 49 online. Five who reported acquired ataxia were excluded. Of the remaining 129, 56 (43%) had FRDA and 18 (14%) had a different type of inherited ataxia (Non-FRDA): these included ataxia-telangiectasia , were younger (p = 0.031) and more likely to be wheelchair-bound (p < 0.001) compared to the Unknown group.Key demographics are shown in Table Table n = 95, 73.6%; mean number of visits = 1.81), followed by cardiology  and ophthalmology . OHP visits over the previous year are expressed as a median  due to a small number of right-sided outliers . The most commonly attended OHP was PT , followed by OT  and SALT . These results were similar for the FRDA group: PT , OT  and SALT .Of the 129, 109 (84.5%) participants attended a hospital consultant clinic an average of 4.74 times. The specialty attended by the greatest number of participants was neurology  and indirect. Only one participant incurred no costs over the 12-month period.Eighteen participants reported receiving both professional and informal care and one participant did not specify the caregiver types. Cost of home care could therefore not be distributed as direct or indirect for these 19 cases and they were excluded from indirect and direct cost subtotals. All cases were included in the final COI calculation as this included all direct and indirect costs.p < 0.001, Wilcoxon signed-rank test) and accounted for 51.9% of total costs. Of the 54 (41.86%) individuals under the retirement age who reported missing work hours due to illness, all but 2  reported being unable to work at all due to ataxia.Indirect costs were significantly higher than direct costs , the COI would be greater than €16 million.Longer disease duration, inability to work due to ataxia, long-term care facility residence and wheelchair use were all associated with greater costs, suggesting that costs increase as disease progresses, and disability levels increase.direct COI for a FRDA patient in North America in 2010 was reported as 14,144 USD  in the USA and 38,373 CAD  PPPY in Canada . T2, but leThe costs of inherited ataxia place a high financial burden not only on the Irish healthcare system, but on patients and caregivers themselves. The majority of participants in our study held medical cards which entitled them to free GP and public hospital care. Eligibility for medical cards is based on household income, and while certain chronic illnesses qualify individuals for free medication and equipment related to their illness, ataxia does not confer such a qualification. Nineteen percent of direct costs were still paid privately by participants, and while this may be subsidised by private health insurance or support from charitable organisations, these will not eliminate costs entirely. Furthermore, this proportion is almost certainly an underestimate as public funding of long-term care facilities is only available on a means-tested basis. Some participants may also have acquired services privately due to long waiting lists and high demand in the public system.While various financial supports from the state subsidise the indirect costs from productivity losses, almost 75% of these societal costs still fall to patients and caregivers. Improving employment rates and productivity amongst people with inherited ataxia and other neurological disabilities may help to relieve the financial burden on society and patients while also benefiting quality of life .A major strength of this study is the sample size. This is, in part, due to the inclusion of all subtypes of inherited ataxia in our cohort, reflecting the range of cases seen in specialist ataxia clinics. Nevertheless, our data selection methods do carry risks of selection bias. Recruitment from clinics and society meetings may be biased towards less disabled patients who are fit to travel. The online survey and option for carers to complete surveys on a participant’s behalf aimed to reduce this bias, but assumes internet access and active engagement in patient organisations. It is difficult to assess the extent of these biases as there are no published epidemiological studies of ataxia in Ireland.Attaining a sample of this size requires an accessible method of data collection and our survey was designed with this in mind. However, the retrospective participant-reported nature of the survey without the use of medical records or face-to-face interview carries the limitation of recall bias and limits the complexity of questions that could reasonably be asked (e.g. names and doses of medications). The categorical nature of questioning may lead to inaccuracies in the data .Variable unit costs such as home and car modifications had to be based on best estimates and may be less accurate than individually reported expenses. Modification costs in our study are higher than but comparable to the average reimbursement on modifications for people with disabilities in 2016  . This fiSeveral assumptions of our study methods have been tested in sensitivity analysis. While reducing informal care costs to account for carers aged over 65 had an unsurprisingly significant effect on total cost, this cost estimate is likely to be at the very lower limit since the reduction was also applied to those receiving combined care. It could also be argued that caregivers who would otherwise be retired from employment should still incur costs when using a human capital as, in their absence, care may be needed to be provided by professional or employed informal caregivers.Informal care requirements were similar to those reported by Giunti and colleagues  in the UNational data is not broken down into daily costs and presumably also incorporates the costs of inpatient investigations which in our study were calculated separately . So, whiIt is reasonable to assume that participants with a known diagnosis (i.e. FRDA and Non-FRDA groups) are accurately reporting their diagnoses, though there is a small risk that participants are mistaken. However, the group with unknown ataxia will contain a wide range of ataxia subtypes and may include cases of FRDA and more likely Non-FRDA, in whom the diagnosis has not yet been made or is not known to the patient. Furthermore, comparisons with the relatively small Non-FRDA group carry a risk of type II error. Thus, comparisons drawn between the subgroups of this study should be viewed with caution.Inherited ataxias carry high financial costs to the health system, patients, caregivers and society as a whole. Costs are similar between FRDA and other forms of inherited ataxia and grow as disability increases over the course of the illness. Indirect costs or ‘productivity losses’ make up half the COI and place a significant financial burden on patients. Despite this, there is evidence to suggest that certain clinical standards of care such as frequency of clinician review, OHP input and monitoring investigations are not being met, possibly reflecting the limited availability of resources. The results of this study advocate the need for greater funding in inherited ataxia care in Ireland to ease the financial burden on patients and improve resource availability.


Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals’ viruses: specifically, lyssaviruses, bornaviruses and rotaviruses. A more comprehensive map of viral host ranges can help identify and mitigate zoonotic and animal-disease risks. A divide-and-conquer approach which separates viral, mammalian and network features predicts over 20,000 unknown associations between known viruses and susceptible mammalian species. Some of these viruses have a very narrow host range, whereas others such as rabies and West Nile viruses2 have very wide host ranges . Host range is an important predictor of whether a virus is zoonotic4 and therefore poses a risk to humans. For example, Severe acute respiratory syndrome-related (SARS-CoV) and Middle East respiratory syndrome-related (MERS-CoV) coronaviruses are both believed to have originated in bats, but through a host range that includes other mammals  they have successfully infected humans. Most recently, SARS-CoV-2 has been found to have a relatively broad host range, including: bats; cats; ferrets; and a proposed intermediate host, Malayan pangolins, which may have facilitated spill-over to humans7. Knowing the potential host range of viruses is essential for efforts to mitigate the global burden of viral diseases8.Thousands of viruses are known to affect mammals, with recent estimations indicating that less than 1% of mammalian viral diversity has been discovered to date9 and the information we have is hugely biased towards humans and domesticated mammals. For example, there is a significant gap between the number of known human viruses (274 species10), and those of wild primates  which is largely a result of differential research effort. Surveillance and research efforts often intensify during and after significant outbreaks, leading to further biases; for instance, recent efforts to identify potential reservoirs of SARS-CoV-2 have led to the identification of two new virus species in wild pangolins (Manis javanica and Manis pentadactyla)11, and a pangolin coronavirus7, thereby doubling the number of known viruses of pangolins.However, our knowledge of the host range of viruses remains limited13; bats vs rodents14) which could only be captured at the global level. Various network topological features have been exploited to provide significant insight into patterns of pathogen sharing14, disease emergence and spill-over events15, and as means to predict missing links in a variety of host-pathogen networks17 including helminths18, and viruses20.Despite these biases, the knowledge accumulated so far provides a valuable resource which can be exploited to estimate the extent to which we are under-observing associations between known viral agents and mammalian hosts. Networks, linking known viruses with their mammalian hosts, present a global view of sharing of these viruses amongst mammalian hosts. This sharing exhibits certain characteristics  associations between known viruses and their mammalian hosts. The novelty of our framework lies in its multi-perspective approach whereby each possible virus-mammal association is predicted three times: 1) from the perspective of each of our mammals ; 2) from the perspective of each of our viruses ; and 3) from the perspective of the network linking known viruses with their mammalian hosts.Here, we express the topology of our virus-mammal network in terms of counts of potential motifsOur framework utilises 6,331 associations between 1896 viruses and 1436 terrestrial mammals, representing 0.23% of all possible associations between these mammals and viruses. It assesses how much these associations are underestimated by predicting which unknown species-level associations are likely to exist in nature . We aggregate these predictions to enhance estimation of the host-range of known mammalian viruses, and to highlight variation in the degree of underestimation at the level of mammalian order (particularly in wild and semi-domesticated species), and viral group , family, and genus. In addition, we highlight knowledge gaps in mammalian species susceptible to known zoonoses and equivalent viruses in important domesticated mammals. By investigating this underestimation from three separate points of view, we enhance the overall predictive performance and capture local , as well as global (aggregated) variations in our knowledge gaps.Our framework to predict unknown associations between known viruses and potential mammalian hosts or susceptible species comprised three distinct perspectives: viral, mammalian and network. Each perspective produced predictions from a unique vantage point . Subsequently, their results were consolidated via majority voting. This approach suggested that 20,832  unknown associations potentially exist between our mammals and their known viruses, . Number of unknown associations predicted by each perspective individually were as follows: mammalian only = 41,537 , viral only = 21,352 , and network only = 76,081 . Our results indicated a ~4.29-fold increase  in virus-mammal associations .Additionally, we trained an independent pipeline including only the 3534 supported by evidence extracted from meta-data accompanying nucleotide sequences, as indexed in EID2 . We first consider each of our perspectives separately, and then showcase how these perspectives are consolidated to produce final predictions.Our multi-perspective framework generates predictions for each known or unknown virus-mammal association . We highlight this functionality using two examples  and our viruses – a ~4.37-fold increase  (Supplementary Results 1) The mammalian perspective: our mammalian perspective models, trained with features expressing viral traits (Table R. leschenaultia) suggested 18 , existing viruses could be found in this host  - Supplementary Results 5).2) The viral perspective: our viral models, trained with features expressing mammalian traits (Table  The viraR. leschenaultia), equivalent to a ~13.95  fold increase (Supplementary Results (3) The network perspective: Our network models indicated a median of 721   unknown associations between WNV and terrestrial mammals, and that 246  existing viruses could be found in our selected host . Figure R. leschenaultia . Similarly, our results indicated that .2). Simin = 699, response variable = 1 if the virus is known to associate with the focal mammalian species, 0 otherwise). This enabled us to assess the relative importance (influence) of viral traits . Overall, RNA viruses had the average host range of 21.65  hosts . DNA viruses, on the other hand, had 7.85  hosts on average . Table 3, ~9.86 n = 556, response variable = 1 if the mammal is known to associate with the focal virus species, 0 otherwise). This allowed us to calculate relative importance of mammalian traits . These results indicated an average of 13.45  unobserved virus species for each wild or semi-domesticated mammalian host . Our framework highlighted differences in the number of viruses predicted per order , we “force insert” this focal association into our network , 4 (n = 6), and 5-node (n = 20) potential motifs in which this association might feature if it actually existed21  against a held-out test set; 2) by systematically removing selected known viral-mammalian associations and attempting to predict them; and 3) against external data source, comprising viral-mammalian associations extracted using an exhaustive literature search targeting wild mammals and their virusesn = 407,265; 954 known virus-mammal associations, see methods below). We removed this set from our network, computed network features (motifs), and trained constituent models in each perspective with the remainder data. We then estimated our framework performance metrics against the held-out test set. Our framework achieved overall AUC = 0.938 [0.862–0.959], F1-Score = 0.284 [0.464–0.124], and TSS = 0.876 [0.724–0.918], when trained without including research effort in its mammalian and viral perspectives. When research effort was included in these perspectives, performance metrics were as follows: AUC = 0.920 , F1-Score = 0.272 , and TSS = 0.840 .Our held-out test set comprised 15% of all data  and attempted to predict these removed associations. Our framework succeeded in predicting 90% of removed associations  where both host and virus were included in our pipeline, and 77.82%  . When including research effort in our mammalian and viral perspectives, these results were: 84.47% , and 78.41% , respectively.31 and hantaviruses32, are linked with increased risk of zoonotic viruses33. Our results could potentially enable targeted surveillance of rodents and bats for known viruses not yet associated with species in these orders: we predict a 5.55-fold (2.69 per species) and 5.45-fold (3.77) increases respectively . Similarly, our results indicate a 5.20-fold increase between wild and semi-domesticated mammals and viruses of economically important domestic species (e.g. livestock and pets). Bats and rodents, which have been associated with recent outbreaks of emerging viruses such as coronavirusesThe increase in associations indicates a knowledge-gap across mammalian species that are potentially susceptible to these viruses. For bats the largest fold increase was in group III viruses with an 8.72-fold-increase , whereas in rodents the highest increase was in group V viruses - a 6.23-fold-increase (3.49 per species).35, with a 10.4-fold increase in the number of predicted associations  common in horses and other domesticated animals, had a 23 and a 12-fold increase in associations in bats and rodents, respectively. Finally, group III Rotaviruses had an 8.11-fold increase in bats – rotaviruses are the most common cause of diarrhoeal diseases in children and are of particular concern in developing countries37.The largest significant fold increases in included bats were with the group V Lyssaviruses (excluding rabies), a family of viruses causing an array of medically and veterinary important rabies-like diseases in a wide range of mammalsons Fig. . Group V38. Our framework highlights this knowledge-gap by predicting a 7.77-fold increase in number of associations between wild and semi-domesticated ruminants and known viruses  which were not associated with the lynx in our input data, including Nipah virus. Such information can be used to better understand the risk to people and livestock from these hosts. There are several reasons for which virus-mammal associations may have been disproportionately under-described, which can be categorised as follows:Felis catus, 57 known virus species) compared with the understudied microbiome of wild felines . Linked to this is wealthier countries producing a larger research volume, and hence interactions common within or of importance to such countries are more likely to be described.Public health, food security and economically driven research biases: Most of our current knowledge of infectious agents, including viruses, is centred upon humans. Second to humans , agricultural and companion animals tend to receive significantly more research effort . Examples include the well-studied microbiome of domestic cats (n = 1450) from 1,619 to 4,174 (~2.16 average increase per rare virus). Virus range of rare mammals  was also increased from 1150 to 4318 .Practical limitations: infectious agents of endangered and rare mammalian species, and mammalian species found predominantly in remote regions, we suspect, are less likely to be characterised due to difficulties in sampling these mammals in their natural habitats. The same likely applies to viruses that are less common in mammals (e.g. avian pathogens). Nevertheless, our approach can capture and expand associations of both rare viruses (found in one or two species), and understudied mammalian species, due to separation of perspectives. If a virus is rare, our approach would capture potentially susceptible mammals via the network and mammalian perspectives. Similarly, if a mammalian species is rarely studied, then we would still capture viruses potentially found in this mammalian species via the network and viral perspectives. Overall, our voting framework was able to expand the host range of rare viruses  and global (network) approaches. Our locally trained mammalian and viral models enabled the exploration of the effect, by means of variable importance, of a comprehensive set of mammalian and viral traits. We were able to measure the relative influence each of our mammalian features had on each multi-host virus; and conversely, the influence each of our viral features had on each mammal (with two or more known viruses). This facilitated the aggregation of variable importance by, for instance, viral or mammalian taxonomy, which in turn illustrated differences in how these features influenced our models. For example, when aggregated at genus level, we found that body mass and a larger proportion of plants in the diet had higher influence on our models for Orbiviruses, which are known to infect ruminants and horses ; whereas longevity, and weaning age were more influential to Ebolavirus models . Uniquely, we incorporated geospatial features extrapolated from an extensive collection of global data on climate, environmental, agricultural, and mammalian diversity variables. The importance of these varied across our viral models. For instance, in coronaviruses, mean human population was more important for Beta-coronaviruses (83.38) than Alpha-coronaviruses (65.65). From the mammalian perspective, phylogenetic and ecological distances to known hosts were the most influential across all models. The importance of maximum phylogenetic breadth varied across families within the same taxonomic order. For instance, in rodents, it ranged from 89.08 (median) in Sciuridae (14 species) to 48.83 in Muridae (37 species). Local, species-level variable importance further enhances the utilisation of our approach to targeted surveillance, by enabling flexible aggregation of results from individual species to entire groups and orders.Secondly, we consolidated these viral and mammalian traits with network topological features, expressed in terms of counts of potential motifs. We measured variable importance of our topological features and found that the likelihood of an association increased the more it featured in motifs linking its mammalian host with a virus with a wide host range (M4.1 and M5.1). Similarly, an association was more likely to be predicted by our network perspective models the more it featured in motifs linking a mammal with a wide range of viruses (M4.6 and M5.20), but the influence of these motif-features was not as high as the previous two. More complex motif-features  had a negative influence: the more an association featured in them, the less likely it was to be predicted by our models. This could be because these motifs indicate a separation between the known host range of the focal virus and the focal mammal, and vice versa. For instance, higher counts of M5.19 suggest that, in general, there are no indirect pathways between the focal virus and mammal, despite the mammal featuring in several such pathways. Thus, higher counts of M5.19 might indirectly indicate that the focal virus is known to affect different types of hosts (e.g. different taxa).44. For instance, we predict 44 novel associations between bats and Filoviruses , which is a more conservative estimate than recent studies43. For flaviviruses, we predict 85 species of primates to be hosts to both zika and yellow fever viruses  compared to 21 predicted in recent work44. Despite the large number of predicted, potentially novel, associations, the fact that our predictions can distilled to the level of individual virus or a mammalian species, makes our approach suitable for targeted surveillance per host or virus, or groups therein.Thirdly, our voting approach, despite being more conservative than its components , a higher proportion of their currently known viruses are likely to have been found. Hence, our approach was able to make predictions for wild and semi-domesticated (medium to under-studied) species in those orders. However, for mammalian orders with fewer species, and where those species are under-studied, there are more likely to be more unknown-unknowns, therefore a larger proportion of their viruses would not be predictable by our approach or other approaches.There remains, however, key areas for further improvement. We differentiate between two types of unknown virus-mammal associations: 1) associations between a known virus and a potentially susceptible mammalian host of this virus: known-unknowns; and 2) completely unknown viruses associated with a host but are not yet discovered: unknown-unknowns. Our approach aimed at the first type: we included as much information as available on known viruses and their susceptible mammalian species to predict associations between wild and semi-domesticated species and our viruses. In the case of species-rich mammalian orders containing sufficiently studied species , bridge vectors2, and reverse zoonoses48. However, for completely novel or never-studied viruses, our approach cannot predict potential associations due to lack of viral and network traits: an example is SARS-CoV-2; our pipeline could not have predicted its host association when it first emerged, but subsequent study of the virus, its traits and its observed hosts allows for prediction of its unobserved host associations49. Future work may be able to enhance the predictive power of our approach by incorporating more diverse viral traits, particularly in terms of detailed genetics9 and in terms of geographical distribution and associated features of the virus as highlighted in previous work51. Integration of predictors of host-virus interactions such as the existence of particular viral receptors in host cells would also greatly benefit our models and create a fourth perspective that could be added into our framework.Prediction of unknown and novel viruses and their potential threat to humans, livestock and wildlife is an increasingly important and active research area. Where an established virus is increasing its geographical range (e.g. due to climatic or demographic factors), then our framework provides powerful means to assess potential hosts it has yet to come into contact with. The identification of these hosts is exceedingly important, as viruses continue to move across the globe via complex transmission cycles featuring migratory animalsFinally, a further separation of perspectives could also be achieved by incorporating arthropod vectors or intermediate hosts, or different classes of pathogens and hosts, particularly birds. Future integration of avian species into our network could potentially increase predictive power and explainability of our approach, particularly in relation to the ecology of viruses for which birds are known to be important reservoirs or amplifying hosts . The incorporation of birds into our network component will enable quantification of yet-uncaptured important pathways in which birds play central roles. However, such integration will first require establishing a more complete picture of avian viruses and their hosts – the number of associations we were able to capture for avian species was 2,525 between 1,251 bird and 306 virus species . This could be achieved either by deeper mining of existing sources or by developing separate predictive pipelines focusing solely on birds.In this study we attempted to expand our knowledge of viral host ranges by predicting the unknown hosts of known viruses. We applied a divide-and-conquer approach which separated viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. We predicted over 20,000 unknown associations between known viruses and mammalian hosts, suggesting that current knowledge greatly underestimates the number of associations between wild and semi-domesticated mammals. Completing the picture of virus-host interactions can help identify and mitigate current and future zoonotic and animal-disease risks, including spill-over from animals into humans.10– EID2 (version from December 2019). EID2 automatically mines information on pathogens (of any taxa), their hosts and locations from two sources: meta-data accompanying nucleotide sequences (hereafter sequences) published in Genbank53; and 2) titles and abstracts (hereafter TIABs) of publications indexed in the PubMed54. At time of extractions, EID2 has collated information from >7 million sequences (and processed 100 M + sequences), and >8 million TIABs. EID2 imports names of organisms , and their taxonomy from the NCBI Taxonomy database55. It also extends these names with an exhaustive, expertly curated, collection of alternative and common names. These names are utilised to disambiguate hosts and pathogens in sequence meta-data and TIABs using inclusion and exclusion terms10. Evidence collated from TIABs is considered likely if it exceeds a given threshold . For the vast majority of stored organisms, EID2 follows the NCBI definitions of ‘species’ and ‘subspecies’, with unclassified and uncultured species being denoted as ‘no rank’. For the purposes of this study, we recursively aggregated virus-mammal associations – a mammal that was found to host a strain or subspecies of virus was considered a host of the corresponding virus species (and vice versa). We further checked each of these species level associations for accuracy and to eliminate laboratory-produced results. This resulted in 6331 associations between 1896 viruses and 1436 terrestrial mammals. The support of these associations in EID2’s evidence base was as follows: 22.79% had publication and sequence evidence; 33.03% were supported by nucleotide sequence only, and 44.18% were supported by evidence extracted from TIABs. The nature of this evidence was as follows: 70.48% of associations were strongly supported by sequence, isolation, or PCR evidence; 29.52% were supported by serology-only evidence. Of the total number of associations inferred from publication-only evidence, 66.82% were supported by serological evidence. We trained our pipelines with associations obtained from both sources; this is because serology is a standard means of determining previous viral infection in an individual. Isolation cannot detect an infection that has since been cleared by the host’s immune system. Hence isolation and serology have different applications, and both should be utilised to get a more complete picture. Both sequencing and serological methodologies vary in their sensitivity and specificity depending on the virus clade. Both sequencing and serological methodologies vary in their sensitivity and specificity depending on the virus clade, with neither being superior in all cases58. Consequently, we chose to present the results using both isolation and serology in the manuscript. However, to account for possible variations in the strength of our evidence base, we trained a separate predictive pipeline including only those associations supported by sequence evidence ; Supplementary Results Species-level virus-mammal associations were extracted from the ENHanCEd Infectious Diseases DatabaseWe transformed our species-level virus-mammal associations into a bipartite network in which nodes represent either virus or mammal species, and links indicate associations between mammalian and viral species. Our bipartite virus-mammal network is sparsely connected – roughly 0.23% of potential associations are documented in EID2, despite it being the most comprehensive resource of its kind. This sparsity is more evident in wild and semi-domesticated species where only 0.182% of potential associations are observed. We treated the problem of bridging this gap in our knowledge of virus-mammal associations as a supervised classification problem of links in the bipartite network. In other words, we aimed to predict unknown associations between known viruses and their mammalian hosts based on our knowledge to date of these species. Each possible virus-mammal association is predicted three times as follows.1 – From the mammalian perspective: For each mammal in our network, given a set of features (predictors) comprising viral traits  – Table 2 – From the viral perspective: For each virus species found in our network, given a set of features (predictors) encompassing mammalian phylogeny, ecology, and geographical distribution – Table n = 1,896 × 1,436 = 2,722,656 possible associations)?3 – Form the network perspective: Given a set of topological features representing the bipartite network expressing most of our knowledge to date of virus-mammal associations, what is the probability of an association forming between any virus and any mammal in our dataset  association was selected if it was predicted by at least two of the three perspectives. This is because each of our perspectives focuses on a particular aspect of virus-host associations. From the mammalian perspective, and for every included mammal, the probability of a virus affecting/associating with this focal mammal is quantified based on our knowledge of the viruses found in this mammal to date. Similarly, from the viral perspective, the probability of the virus infecting/associating with included mammalian species is quantified based on our knowledge to date of known hosts of this virus. The final perspective enables generation of predictions based on the topology of the network linking all included mammals with all included viruses. Thus, our three perspectives capture all aspects of viral-mammalian association without biasing toward one aspect.Our framework is flexible, in terms of machine-learning algorithms selected, classifiers trained, and features engineered for each perspective. It avoids overfitting as it approaches the problem from various perspectives, and effectively consolidates ensembles of classifiers trained on subsets of the underlying data. In addition, no constituent model of our framework has been trained with all available data at any time. Finally, our framework enables the incorporation of hosts where only one virus has been detected to date (via perspectives 2 and 3), and viruses where only one host has been discovered (via perspectives 1 and 3).Our mammalian and viral perspectives generate “local” predictions for hosts and viruses, respectively. These local predictions are derived by training a suite of models for each host (with two or more known viruses), and virus species , as described in subsequent sections. In other words, each mammalian species has its own “local” suite of models, trained using viral traits Table , to pred21  or fewer from each focal node . Cohen’s Kappa = 0.86 [0.85–0.89] (Kappa range: 0-1). Results of this pipeline are listed in Supplementary Results We incorporated research effort on mammal and virus species into our network perspective models. This is because it is through this perspective that predictions are made for all hosts and all viruses at the same time, and where the effect of research effort into both the hosts and viruses can be measured and corrected for adequately and simultaneously. We calculated research effort as the total number of sequences and publications of each species as indexed by EID2As highlighted above, our framework comprised three perspectives: mammalian, viral and network. Each of these perspectives trained a set models with different features Tables .n = 1281) which was found in only one mammal species from our virus models pipeline , and we excluded any mammal (n = 758) which is only affected by one virus from our mammal models pipeline . Second, we deployed SMOTE - Synthetic Minority Over-sampling Technique60 to rebalance the classes prior to training each of our viral (n = 8 × 556) and mammalian (n = 8 × 699) models. SMOTE synthesises new minority class instances from existing minority instances using a variation of k-nearest neighbour algorithm. The SMOTE algorithm then over-samples from the minority instances  and under-samples from the majority class to create a balanced training set. All class balancing was achieved using caret R package63 (R version 3.6.2).On average each virus in our dataset affected 3.45 mammals , and each mammalian host was affected by 4.41 viruses (~0.241% of the 1833 viruses in our models). This presented an imbalance in our data, whereby a small percentage of instances are actualised. We dealt with this issue in two ways: first we excluded any virus (61), and were selected due to their robustness, scalability61, and their potentially good performance with imbalance data classification62. All models were trained and optimised using caret R package63 (R version 3.6.2) as described below.For each mammal and each virus selected above we trained 8 classification algorithms . This validation method works by splitting training data into 10 random samples, each sample is held out in turn, and the model is trained on the remainder groups. The model’s prediction for the existence or absence of the mammal-virus associations in the held-out group are used to construct confusion matrices and calculate an optimisation metric . The optimisation metric is used to select best model in the validation process.64 to tune the hyper-parameters of our models. This approach resamples the tuning parameter grid in a way that concentrates on values that are the in the neighbourhood of the optimal settings (adaptive). Due to the large number of classifiers trained in our framework this adaptive approach allowed us to find optimal  values of the hyper-parameters of each included machine-learning algorithm without relying on the nominal resampling process whereby all the tuning parameter combinations are computed for all the resamples before a choice is made about which parameters are good and which are poor.We adopted an adaptive resample approachWe computed three performance metrics based on the median predicted probability across each set of replicate models: AUC, true skills statistics (TSS) and F1-score , hyper-parameters of best performing models were carried across to train 50 replicate models for each best performing mammalian or viral model. In other words, we generated a bragging (i.e. median) ensemble for each selected host or virus, and the resulting prediction was carried to our multi-perspective final model.Our bipartite virus-mammal network is sparsely connected with 6331 documented associations out of 2,722,656 possible associations (0.23%). Due to this we implemented strict under-sampling: whereby balanced samples drawn at random (without replacement) from the set of all potential virus-mammal associations. Each sample comprised 2000 instances (1000 positive (known) and 1000 unknown virus-mammal associations.We trained the same selection of algorithms as above with balanced sets (2000 instances each) using 10-fold cross validation with adaptive resampling to optimise AUC. We repeated this process 100 times to generate a bragging ensemble of predictions (derived as probabilities) of these replicate models. We calculated empirical confidence intervals (90%) of the ensemble probabilities across the 100 replicate models.We selected the bragging ensemble which obtained the best overall performance metrics  when applied to all available associations. The predictions of the best overall ensemble were incorporated into our final model . The processes described above were repeated with training set only, and performance was measured against the held-out test set . Performance metrics obtained through this assessment are reported above and in Supplementary Results We trained the constituent models of each perspectives with a stratified random training set comprising 85% of all data (63).we calculated relative importance (influence or contribution) of viral Table , mammaliFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6


Bacillus anthracis caused repeated outbreaks in humans. More than 300 human cases with Nipah virus infection were reported since the first outbreak in 2001. The highly pathogenic avian influenza virus (H5N1) caused more than 550 outbreaks in poultry, and eight human cases were reported so far since 2007. People of Bangladesh are frequently exposed to zoonotic pathogens due to close interaction with domestic and peri‐domestic animals. The rapidly changing intensified animal–human–ecosystem interfaces and risky practices increase the risk of zoonotic disease transmission. The narrative review's findings are useful to draw attention to the risk and emergence of zoonotic diseases to public health policymakers in Bangladesh and the application of one‐health approach to address this public health threat.Zoonotic diseases cause repeated outbreaks in humans globally. The majority of emerging infections in humans are zoonotic. COVID‐19 is an ideal example of a recently identified emerging zoonotic disease, causing a global pandemic. Anthropogenic factors such as modernisation of agriculture and livestock farming, wildlife hunting, the destruction of wild animal habitats, mixing wild and domestic animals, wildlife trading, changing food habits and urbanisation could drive the emergence of zoonotic diseases in humans. Since 2001, Bangladesh has been reporting many emerging zoonotic disease outbreaks such as nipah, highly pathogenic avian influenza, pandemic H1N1, and COVID‐19. There are many other potential zoonotic pathogens such as Ebola, Middle East respiratory syndrome coronavirus, Kyasanur forest disease virus and Crimean–Congo haemorrhagic fever that may emerge in the future. However, we have a limited understanding of zoonotic diseases’ overall risk in humans and associated factors that drive the emergence of zoonotic pathogens. This narrative review summarised the major emerging, re‐emerging, neglected and other potential zoonotic diseases in Bangladesh and their associated risk factors. Nipah virus and  Live poultry market ‐ an important interface for zoonotic pathogens spillover. H5 and 3.2Paramyxoviridae  infection is a highly fatal emerging zoonotic disease caused by an emergent Henipavirus under the family 3.3Bacillus anthracis. Anthrax is endemic in Bangladesh. Cattle, sheep and goats are mostly affected. In 1980, anthrax was first reported in man and animals in Bangladesh  is a zoonotic disease caused by the mosquito‐borne Flavivirus. JE is endemic in humans in South Asian countries, including Bangladesh, India, Pakistan, and Myanmar  was first detected in humans in March 2009. Mexico reported the first outbreak of this virus WHO, . More th3.8Brucella melitensis, Brucella abortus, Brucella suis and Brucella canis cause illnesses in humans  and animals (8%–70%) in Bangladesh  is a highly transmissible disease infecting more than four million people worldwide as of 12 May 2020 WHO, . COVID‐13.13Coxiella burnetii, Rickettsia rickettsii, Borrelia burgdorferi, Francisella tularensis, tick‐borne encephalitis virus, Kyasanur forest disease (KFD) virus and Crimean–Congo haemorrhagic fever (CCHF) virus , Salmonella spp, Campylobacter spp and Listeria monocytogenes are considered as the major causes of food‐borne illness. The majority of the pathogens causing food‐borne illness are zoonotic , rotavirus (19%), Campylobacter jejuni (14%), Vibrio cholera (6%), Shigella (12%) and Salmonella (1%) were the main causes of diarrhoea in Bangladesh , Ebola, West Nile virus (WNV), zoonotic hepatitis E virus, and zoonotic pox virus, which can be emerged to human in Bangladesh. People of Bangladesh live very close to domestic and peri‐domestic animals such as rodents, crows and bats. A study detected antibodies for MERS‐CoV in camel raised in Bangladesh (Islam et al., 4Bangladesh has generated adequate information on the epidemiology of certain zoonotic diseases such as the nipah and highly pathogenic avian influenza. Information about other potential zoonotic diseases is limited. Only a few studies were conducted to develop and design locally accepted and effective interventions to control zoonotic disease transmission. Very few molecular and immunologic studies were performed so far. More research studies are needed to understand the burden of zoonotic diseases and to develop interventions. People of Bangladesh are not well conscious about zoonotic diseases and associated risk factors. Awareness of zoonotic diseases, proper biosafety measures, improved biosecurity practices, the impact of deforestation and wildlife hunting, wildlife conservation, safe animal handling and carcass disposal must be improved in general people through mass communication. Strong multi‐sectoral collaboration among medical professionals, veterinarians, eco‐health and agriculture personals is required to detect, respond, and prevent zoonotic diseases in Bangladesh using one‐health approach.The authors have no conflict of interest to declare.Sukanta Chowdhury: Conceptualization; Data curation; Formal analysis; Methodology; Project administration; Resources; Software; Validation; Writing‐original draft. Mohammad Abdul Aleem: Conceptualization; Data curation; Formal analysis; Methodology; Writing‐review & editing. Md Shafiqul Islam Khan: Conceptualization; Formal analysis; Methodology; Writing‐review & editing. Mohammad Enayet Hossain: Conceptualization; Methodology; Writing‐review & editing. Sumon Ghosh: Conceptualization; Formal analysis; Writing‐review & editing. Mohammed Ziaur Rahman: Conceptualization; Formal analysis; Methodology; Supervision; Writing‐review & editing.https://publons.com/publon/10.1002/vms3.465.The peer review history for this article is available at