Malaria is among the top causes of death in adolescent girls (10 to 19 years) globally. Adolescent motherhood is associated with increased risk of adverse maternal and neonatal outcomes. The interaction of malaria, adolescence, and pregnancy is especially relevant in malaria endemic areas, where rates of adolescent pregnancy are high. However, data on burden of malaria among adolescent girls are limited. This study aimed at investigating whether adolescent girls were at a greater risk of experiencing malaria-related outcomes in pregnancy—parasitaemia and clinical disease—than adult women.p-value = 0.003, I2 = 0.0%, N = 4,092), peripheral parasitaemia at delivery (odds ratio (OR) 2.28, 95% CI 1.46; 3.55, p-value < 0.001, I2 = 0.0%, N = 3,977), and placental infection . Similar associations were observed among the subgroup of HIV-uninfected participants: IRR 1.72  for clinical malaria episodes, OR 2.39  for peripheral parasitaemia, and OR 1.88  for placental malaria. Among HIV-infected subgroups statistically significant associations were not observed. Similar associations were found in the subgroup analysis by gravidity. The small sample size and outcome prevalence in specific countries limited the inclusion of some countries in the meta-analysis. Furthermore, peripheral parasitaemia and placental malaria presented a considerable level of missing data—12.6% and 18.2% of participants had missing data on those outcomes, respectively. Given the original scope of the clinical trials, asymptomatic malaria infection was only assessed at the end of pregnancy through peripheral and placental parasitaemia.An individual secondary participant-level meta-analysis was conducted using data from 5,804 pregnant women participating in 2 malaria prevention clinical trials in Benin, Gabon, Kenya, Mozambique, and Tanzania between 2009 and 2014. Of the sample, 1,201 participants were adolescent girls with a mean age of 17.5 years (standard deviation (SD) 1.3) and 886 (73.8%) of them primigravidae. Among the 4,603 adult women with mean age of 27.0 years (SD 5.4), 595 (12.9%) were primigravidae. Mean gestational age at enrolment was 20.2 weeks (SD 5.2) and 1,069 (18.4%) participants were HIV-infected. Women were followed monthly until the postpartum visit (1 month to 6 weeks after delivery). This study considered outcomes including clinical episodes during pregnancy, peripheral parasitaemia at delivery, and placental malaria. A 2-stage meta-analysis approach was followed by pooling single multivariable regression results into standard DerSimonian–Laird random-effects models. Adolescent girls were more likely than adult women to present with clinical malaria during pregnancy (incidence risk ratio (IRR) 1.70, 95% confidence interval (CI) 1.20; 2.39, In this study, we observed that adolescent girls in sub-Saharan Africa (SSA) are more prone to experience clinical malaria episodes during pregnancy and have peripheral malaria and placental infection at delivery than adult women. Moreover, to the best of our knowledge, for the first time this study disaggregates figures and stratifies analyses by HIV infection. Similar associations were found for both HIV-infected and uninfected women, although those for HIV-infected participants were not statistically significant. Our finding suggests that adolescent girls may benefit from targeted malaria prevention strategies even before they become pregnant. In a secondary analysis of clinical trial data, Clara Pons-Duran and colleagues study clinical malaria during pregnancy among adolescent and adult women in Benin, Gabon, Kenya, Mozambique, and Tanzania between 2009 and 2014. Both malaria infection and adolescence are associated with increased risk of adverse pregnancy and birth outcomes.Few studies conducted in sub-Saharan Africa (SSA) have assessed the burden of malaria in pregnancy among adolescent girls compared to adult women.However, there is still a need to determine the magnitude of the association between a wide range of malaria in pregnancy indicators and adolescence accounting for the number of previous pregnancies and considering special population subgroups such as HIV-infected women.We designed a secondary analysis of data of 2 clinical trials conducted between 2009 and 2014 in 5 SSA countries: Benin, Gabon, Mozambique, Tanzania, and Kenya.We followed an individual participant data meta-analysis approach using data from 5,804 trial participants, of whom 1,202 were adolescent girls, and including HIV-infected individuals .We observed that adolescent girls presented with clinical malaria during pregnancy more frequently than adult women (1.7 times), and that they were more likely to have experienced peripheral and placental malaria infection at delivery compared to adults (odds ratio (OR) 2.28 and 1.97, respectively).These findings indicate that there is an urgent need for more malaria in pregnancy research focused on adolescent girls.Adolescent girls may benefit from targeted malaria control strategies, even before they become pregnant, that consider their specific needs.To interpret our results, some limitations should be considered, namely the small sample size in specific study countries that limited their inclusion in some meta-analyses and the considerable level of missing data. Malaria infection during pregnancy constitutes a major public health problem, especially in sub-Saharan Africa (SSA) where an estimated 11.6 million pregnancies were exposed to the infection in 2020 . MalariaAdolescent motherhood is associated with increased risk of preterm birth, low birth weight, and childbirth complications such as asphyxia or neonatal mortality , both asThe interaction of malaria, adolescence, and pregnancy is especially relevant in SSA, where the highest malaria burden overlaps with a high rate of adolescent pregnancy. Among older adolescents (aged 15 to 19 years), it is estimated that approximately 9 million become pregnant in Africa every year . HoweverRecent studies have evaluated and described the burden of malaria among adolescent girls and nulliparas ; howeverTo our knowledge, no multicountry studies to date have been done to investigate the burden of malaria in pregnancy among adolescent girls compared to adult women. Furthermore, the authors could not identify any studies that stratified results by human immunodeficiency virus (HIV) status of women. Although malaria infections among adolescent girls are known to be generally asymptomatic and incidence of clinical disease is low among them ,25, pregIn this context, we designed a secondary analysis of 2 multicountry clinical trials to address the abovementioned gaps. It was hypothesised that adolescence compared to adulthood is significantly associated with increased burden of malaria in pregnancy.This is an individual participant-level data meta-analysis of 2 clinical trials. The selection of the included studies was not systematic; only the data collected as part of the Malaria in Pregnancy Preventive Alternative Drugs (MiPPAD) consortium clinical trials was used ,28. ThisWe collected individual participant-level data from 2 multicentre clinical trials conducted in parallel in Benin , Gabon (Lambaréné and Fougamou), Kenya (Siaya), Mozambique (Manhiça and Maragra), and Tanzania (Makole and Chamwino) between 2009 and 2014 . Both trials followed the same clinical protocols and were managed by the same team. Characteristics of study sites are described in The first study  was a randomised controlled open-label trial, performed in Benin, Gabon, Mozambique, and Tanzania, that evaluated the efficacy and safety of mefloquine (MQ) compared to sulfadoxine-pyrimethamine (SP) as intermittent preventive treatment of malaria during pregnancy (IPTp) among 4,749 HIV-uninfected pregnant women . The secThe study population were pregnant women of all gravidities attending their first antenatal care visit in the current pregnancy. The inclusion criteria in the 2 trials were the same except for the HIV status of the participants: (a) ≤28 gestational weeks; (b) being residents in the study area; and (c) agreeing to give birth in one of the maternity wards of the study area. No age restrictions were applied. Women were followed monthly during pregnancy at the antenatal care clinics. At the end of pregnancy, peripheral blood and placental samples were collected from the mothers and their newborns. Malaria was assessed by passive case detection during pregnancy  and actively at delivery. More information on the specific malaria diagnostic technique can be found in the original trials’ publications ,28. DataPlasmodium falciparum parasites of any density in a blood smear, plus any of the following signs and symptoms suggestive of malaria: reported history of fever in the last 24 hours, axillary temperature ≥37.5°C, pallor, arthromyalgias, headache, and history of convulsions. Secondary outcomes were peripheral PCR-confirmed P. falciparum infection at delivery, anaemia at delivery (defined by haemoglobin concentration lower than 11 g/dL), and PCR-confirmed P. falciparum placental infection. The main independent variable of the analyses was the age group, categorised into adolescents (10 to 19 years) and adults (>19 years) [The primary study outcomes were peripheral malaria parasitaemia at delivery (measured by thick blood smear), placental infection (measured by thick blood smear and histology), and number of clinical malaria episodes during pregnancy. A clinical malaria episode was defined as the presence of asexual 9 years) .Additional health and demographic potential confounders were used as covariates to adjust the analyses: trial arm, gravidity, literacy (able to read and write), gestational age at recruitment , mid-upper arm circumference (MUAC) (measured on left arm with MUAC tape), baseline anaemia (determined using mobile HemoCue and Hemocrontrol devices in capillary blood sample), and adherence to study treatment (IPTp) . Season at enrolment was also used to adjust analysis using a proxy variable where we divided the duration of recruitment into 8 periods in each study site ,28. ThesObservations with missing information on age and covariates were dropped (5 cases); they were less than 0.001% of the sample. Parasitaemia at delivery, placental infection, and anaemia had missing values in 12.6%, 18.3%, and 12.5% of the participants, respectively. Overall and within each country and trial, mean age of participants remained similar after dropping missing observations. PCR infection confirmation, both for peripheral parasitaemia at delivery and placental infection, was only performed in a randomly selected subsample of participants from all countries except for Tanzania . This waIn order to control for the effect of country and HIV status in this analysis, each country included in each trial was considered as a separate sub-study. Therefore, 4 sub-studies including HIV-uninfected women , and Tanzania (I)) and 3 sub-studies including HIV-infected women , and Tanzania (II)) were considered in the analyses. The 2 sub-studies carried out in Tanzania were excluded from the analysis of clinical malaria incidence and parasitaemia at delivery since malaria was not detected in any of the participants. The study performed in Tanzania among HIV-infected women was also excluded from the analysis of placental malaria infection for the same reason. Kenya was dropped from the analysis of clinical malaria since no cases among adolescents were found and no statistical adjustment could be applied to allow its inclusion. The potential bias that these exclusions may cause is discussed in last section of this article.Prevalence was calculated using frequencies for discrete variables; means and standard deviations were used for continuous variables. Incidence outcomes were estimated as cases per person-year at risk.2 statistic [Data were analysed following a 2-stage individual participant data meta-analysis (IPD-MA) approach. The availability of individual participant data allowed for adjusting the analysis by potential confounders. The first stage consisted of performing multivariable regression analyses for each outcome, per study, with age group as the principal independent variable and all the covariates as adjusting factors fixed in all models. Logistic regression models were used in the case of dichotomous outcomes, and Firth correction was applied due to low prevalence of some study outcomes . Negativtatistic .Supplementary sensitivity analyses were carried out for the primary outcomes with missing information  applying inverse probability weighting in order to adjust the results for potential selection bias . The proA supplementary subgroup analysis by gravidity  was performed as a robustness check for those outcomes found to be significantly associated with age group, after removing gravidity from the list of covariates. Finally, an exploratory sub-analysis to compare malaria infection between girls ≤16 years with those aged >16 to 19 years was conducted for outcomes found to be significantly associated with adolescence. It aimed at exploring whether differences between younger and older adolescents exist in the relationship with malaria in pregnancy.ipdmetan [The significance level was set at 0.05. All analyses were performed using the Stata statistical program version 16  and the command ipdmetan .The protocols and informed consent/assent forms of the original trials were approved by the Ethics Committee of the Hospital Clínic of Barcelona (Spain) (IP.07.31080.002), the US Centers for Disease Control and Prevention (#5609), and the national ethics review committees of the participating countries. Minors as young as 13 years were included in the clinical trials. Their inclusion followed national guidelines. In Kenya, a separate assent form was signed by minors under 16 years and the signature of the legal guardian was required. In the other countries, they were considered mature minors since they were pregnant. The trials were conducted in accordance with the Declaration of Helsinki and the Good Clinical Practice guidelines.This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline  of whom were adolescents. A detailed description of the adolescent population in terms of specific age bands by country can be found in p-value = 0.003, N = 4,092, 4 sub-studies) (2 = 0.0%). The analysis including only sub-studies performed among HIV-uninfected women also demonstrated a significantly higher prevalence among adolescents, with a similar magnitude , whereas the analysis among HIV-infected women did not yield statistically significant differences between adults and adolescents .The incidence of clinical malaria during pregnancy was significantly higher among adolescents than among adults (incidence risk ratio (IRR) 1.70, 95% confidence interval (CI) 1.20; 2.39, studies) . There wp-value < 0.001, I2 = 0.0%, N = 3,977, 5 sub-studies), indicating that adolescent girls were at a greater risk for parasitaemia at delivery than adult women , HIV-infected subgroup OR 1.61 .The overall odds ratio (OR) for the association between age group and peripheral parasitaemia at delivery was 2.28  , and there was moderate heterogeneity (I2 = 34.9%). In studies among HIV-infected women, the association observed between age and placental infection was not significant .Adolescence was associated with an increased likelihood of placental infection  . In the p-value = 0.03, I2 = 0.0%, N = 1,414, 5 sub-studies)  .p-value = 0.70, I2 = 9.8%, N = 5,081, 7 sub-studies) or PCR-confirmed placental infection . Additional details on these analyses can be found in No associations were observed between age group and prevalence of anaemia at delivery   , Table Pp-value < 0.001, I2 = 0.0%, N = 3,962, 5 sub-studies) and placental malaria  after applying weights. Forest plots and additional details of the sensitivity analysis can be found in Applying inverse probability weighting as a sensitivity analysis for selection bias from missing data yielded similar results than primary analysis. Adolescence was found to be associated with both peripheral parasitaemia at delivery     and PCR-confirmed parasitaemia at delivery among both primigravidae and multigravidae  did not yield statistically significant associations.The results of the analyses stratified by gravidity are included in the Fig E in , parasitFig F in  and onlyFig G in . Analysep-value = 0.10, I2 = 23.6%, N = 938, 3 sub-studies), parasitaemia at delivery , and placental infection   and older adolescents (>16 years) for primary study outcomes: incidence of clinical malaria , very young maternal age (≤16 years) was associated with higher risk of preterm birth and low birth weight compared to older women, even after adjusting for parity . This fiRegarding anaemia at delivery and contrary to our results, a previous cross-sectional study carried out in Ghana observed that adolescent girls were more likely to suffer from anaemia during pregnancy . The mosOur findings also suggest that adolescent girls might experience higher risk of malaria in pregnancy regardless of their HIV status, although associations between adolescence and malaria-related outcomes for HIV-infected participants only did not reach statistical significance. To our knowledge, this is the first time this is evaluated. In this same line, our team has recently shown that the associations between age, maternal morbidity, and adverse pregnancy outcomes were not modified by HIV status among pregnant mothers from Mozambique . Both thSome study limitations need to be acknowledged. First, the extrapolation of our results to other contexts should be done with caution. Our results regarding HIV-stratified analyses may not be applicable to HIV-infected populations with lower rates of treatment adherence, and the main study findings need to be interpreted in the context of antimalarials. Then, participants in clinical trials regularly attend health facilities, and consequently their health is closely monitored. Also, their background characteristics might differ from those of the general population in their countries, and their attitude towards the health system may also be different . MoreoveAnother study limitation was the exclusion of certain countries from specific analyses. The sub-studies from Tanzania were excluded from some of the meta-analyses since they did not present cases of parasitaemia at delivery or clinical malaria episodes during pregnancy. It is worth noting that these were not true stand-alone studies, but subsamples of clinical trials. Thus, the sample size of each might have been insufficient to observe some of the outcomes by age group. This is especially applicable to HIV-infected participants, who represented only a small proportion of the total study sample. For instance, the low proportion of adolescent girls in Kenya did not allow its inclusion in the analysis of clinical malaria cases and this could have biased the results for that outcome. However, the sample size of Kenya sub-study was lower than that of other countries; therefore, its weight in the analysis would have been low. Besides, the sensitivity and stratified analyses showed consistent associations of similar magnitude and direction regardless of the studies included in the specific meta-analysis.Then, it is necessary to acknowledge the considerable amount of missing data with regard to the study outcomes. This was mainly due lost to follow-up trial participants and an incomplete assessment of malaria outcomes among the final trial sample. Nevertheless, sensitivity analysis where inverse probability weighting was applied indicates that the associations observed still hold after accounting for potential selection bias. It is important to note that this approach relies on the correct specification of the model that includes all possible covariates associated with missingness of outcome data.Finally, some factors and covariates known to be strongly associated with malaria risk, namely nutritional status, supplementation, and habits were not considered in this study due to the secondary nature of the data. Also, incidence of asymptomatic malaria infection during pregnancy was not analysed as a study outcome due to the study design of the original clinical trials, although it would have provided an added value to the study. Importantly, the presented findings do not allow ascertaining whether adolescent girls are at a higher risk of being infected with malaria during the course of pregnancy or if asymptomatic infections are more frequent among adolescents or adult women.Despite these limitations, the present study includes individual participant data of more than 5,800 pregnant women from 5 different SSA countries, recruited and followed-up using the same procedures and with the same definitions of study outcomes.Our results highlight the need for more malaria in pregnancy research focused on adolescent girls. First, malaria studies should present results by age group when possible. It is of a high priority to determine which are the mediators of the increased risk of malaria in pregnancy among adolescents and whether there are confounders that have not yet been identified. In addition, it is also fundamental to understand the health-seeking behaviour of adolescents in order to design strategies that could best target them.From the public health perspective, the present findings indicate that there is a need to promote interventions aiming to protect adolescents from malaria. Adolescent girls should be particularly targeted in malaria control strategies including ITN distribution campaigns, even before they become pregnant. This is especially relevant because they sometimes carry asymptomatic and chronic infections before conception, and drug-based preventive strategies cannot be initiated before the second trimester of pregnancy. In addition, interventions to prevent malaria among adolescents once they become pregnant should be developed considering their specific needs.This study is not the first calling for action to overcome the gaps in research and implementation with regard to malaria control among adolescents . In 2017In conclusion, the findings of this study support an increased likelihood of adolescent girls to experience malaria in pregnancy compared to their adult peers. Moreover, it showed that this association remains after stratifying populations by gravidity. There is a need to stress the importance of putting adolescents in the malaria research agenda and to consider them a vulnerable group to be targeted in future malaria control strategies.S1 File(PDF)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 FileTable A. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Mozambique . Table B. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Mozambique . Table C. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Gabon . Table D. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Tanzania . Table E. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Tanzania . Table F. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Benin . Table G. Characteristics of study participants with and without data on peripheral parasitaemia at delivery and placental infection in Kenya . Table H. Number and proportion of adolescent girls by single age bands, by country. Table I. Number of clinical malaria cases and incidence of clinical malaria during pregnancy, by sub-study. Table J. Sensitivity analysis: clinical malaria incidence during pregnancy, effect of removing countries from the analysis. Table K. Number of participants with peripheral parasitaemia at delivery by sub-study. Table L. Sensitivity analysis: peripheral parasitaemia at delivery, effect of removing countries from the analysis. Table M. Number of participants with placental malaria by sub-study. Table N. Sensitivity analysis: placental malaria, effect of removing countries from the analysis. Table O. Number of participants with peripheral PCR-confirmed infection at delivery by sub-study. Table P. Sensitivity analysis: peripheral PCR-confirmed infection at delivery, effect of removing countries from the analysis. Table Q. Number of participants with placental PCR-confirmed malaria infection by sub-study. Table R. Sensitivity analysis: placental PCR-confirmed malaria infection, effect of removing countries from the analysis. Table S. Number of participants with anaemia at delivery by sub-study. Table T. Sensitivity analysis: anaemia at delivery, effect of removing countries from the analysis. Table U. Number of clinical malaria cases and incidence of clinical malaria during pregnancy, by sub-study and gravidity. Table V. Number of participants with peripheral parasitaemia at delivery by sub-study and gravidity. Table W. Number of participants with placental malaria by sub-study and gravidity. Table X. Number of participants with peripheral PCR-confirmed infection at delivery by sub-study and gravidity. Table Y. Number of clinical malaria cases and incidence of clinical malaria during pregnancy among adolescent girls belonging to different age groups, by sub-study. Table Z. Number of peripheral parasitaemia cases during pregnancy among adolescent girls belonging to different age groups, by sub-study. Table AA. Number of placental infection cases among adolescent girls belonging to different age groups, by sub-study. Table AB. Number of peripheral PCR-confirmed infections at delivery among adolescent girls belonging to different age groups, by sub-study. Fig A. Sensitivity analysis: peripheral parasitaemia at delivery, inverse probability weighting. Fig B. Sensitivity analysis: placental malaria, inverse probability weighting. Fig C. Analysis for placental PCR-confirmed malaria infection. Fig D. Analysis for anaemia at delivery. Fig E. Subgroup analysis by gravidity: incidence of clinical malaria during pregnancy. Fig F. Subgroup analysis by gravidity: peripheral parasitaemia at delivery. Fig G. Subgroup analysis by gravidity: placental malaria. Fig H. Subgroup analysis by gravidity: peripheral PCR-confirmed infection at delivery. Fig I. Sub-analysis among adolescent girls: incidence of clinical malaria during pregnancy. Fig J. Sub-analysis among adolescent girls: peripheral parasitaemia at delivery. Fig K. Sub-analysis among adolescent girls: placental malaria. Fig L. Sub-analysis among adolescent girls: peripheral PCR-confirmed infection at delivery.(DOCX)Click here for additional data file.


Malaria represents one of the most important imported tropical infectious diseases in European travellers. The objective of the study was to identify changes in the epidemiological features of imported malaria and to analyse the clinical findings and outcomes of imported malaria.This single-centre descriptive study retrospectively analysed the medical records of all imported malaria cases in travellers treated at the Department of Infectious Diseases of University Hospital Bulovka in Prague from 2006 to 2019.Plasmodium falciparum was the predominant species (149/203), and its proportion significantly increased from 35/60 cases (58.3%) in 2006–2011 to 69/80 (86.3%) in 2016–2019 (p < 0.001). In contrast, the incidence of Plasmodium vivax malaria decreased from 19/60 cases (31.7%) in 2006–2011 to 5/80 (6.3%) in 2016–2019 (p < 0.001). Malaria was imported from sub-Saharan Africa in 161/203 cases (79.3%). The proportion of travellers from Southeast and South Asia decreased from 16/60 (26.7%) and 6/60 (10.0%) in 2006–2011 to 2/80 (2.5%) and no cases (0.0%) in 2016–2019, respectively (p < 0.001 and p = 0.006). Tourism was the most common reason for travel (82/203), however, the proportion of non-tourists significantly increased over time from 29/60 (48.3%) in 2006–2011 to 55/80 (68.8%) in 2016–2019, p = 0.015. Severe malaria developed in 32/203 (15.8%) patients who were significantly older (p = 0.013) and whose treatment was delayed (p < 0.001). Two lethal outcomes were observed during the study period.The study included 203 patients with a median age of 37 years (IQR 30–48) and a male to female ratio of 3.72:1. P. falciparum malaria, which frequently resulted in severe disease, especially in older patients and those with delayed treatment initiation. The rising proportion of imported malaria in non-tourists, including business travellers and those visiting friends and relatives, is another characteristic finding analogous to the trends observed in Western European and North American centres. The described changes in the aetiology and epidemiology of imported malaria may serve to optimize pre-travel consultation practices and improve post-travel diagnostics and medical care.This study demonstrated a significant increase in The online version contains supplementary material available at 10.1186/s12936-022-04282-8. Plasmodium falciparum is the predominant species. However, there was a significant decrease in cases reported in all other tropical regions, including Southeast Asia, the Americas, and the Western Pacific region since the beginning of the century. This trend is paralleled by a steady decrease in cases caused by Plasmodium vivax, the species that causes a substantial mortality burden outside of Africa and remains the most common species in Latin America  and those visiting friends and relatives [30 days (IQR 21–55)] than in tourists [21 days (IQR 16–28)], p < 0.001.P. falciparum malaria , 122/203 (60.0%) patients with non-complicated P. falciparum malaria , and 49/203 (24.1%) patients with non-falciparum malaria . Severe P. falciparum malaria represented 7/60 (11.7%) of all malaria cases in 2006–2011, 10/63 (15.9%) in 2012–2015, and 15/80 (18.8%) in 2016–2019, respectively (p = 0.258). No cases of severe non-falciparum malaria were observed.There were 32 patients (15.8%) with severe The most frequently reported chronic co-morbidities were arterial hypertension , asthma/chronic obstructive pulmonary disease , hyperlipidaemia , history of thromboembolic disease , hyperuricaemia , depressive disorder , diabetes mellitus , and HIV infection . However, most patients  reported no co-morbidities.P. falciparum malaria. However, two of the patients reported using a non-recommended drug (chloroquine and artemisinin), and one patient was non-compliant with atovaquone-proguanil prophylaxis. Fourteen patients (11.5%) with non-complicated P. falciparum malaria used anti-malarial chemoprophylaxis: 2 patients reported proper use of mefloquine, 9 patients were non-compliant with chemoprophylaxis, and 3 patients used an inadequate drug. In non-falciparum malaria, chemoprophylaxis was used by 18 patients: 12 patients were compliant with the recommended regimen, and 6 patients were non-compliant. However, most patients with severe malaria [29/32 (90.6%)], non-complicated P. falciparum malaria [108/122 (88.5%)], and non-falciparum malaria [31/49 (63.3%)] did not use any chemoprophylaxis (p < 0.001).Anti-malarial chemoprophylaxis was used by three patients (9.4%) with severe P. falciparum malaria, 0.39% (IQR 0.06–1.80) and 0.52% (IQR 0.10–2.07) in non-complicated P. falciparum malaria, 0.03% (IQR 0.01–0.12) and 0.03% (IQR 0.01–0.16) in non-falciparum malaria . The median age of patients with severe P. falciparum, non-complicated P. falciparum and non-falciparum malaria was 44 (IQR 36–56), 35 (IQR 29–48) and 35 years (IQR 30–45), respectively (p = 0.013). The proportions of comorbidities in patients with severe P. falciparum, non-complicated P. falciparum and non-falciparum malaria were 10/32 (31.3%), 29/122 (23.8%) and 11/49 (22.4%), respectively (p = 0.628). The median duration of fever from symptom onset to clinical examination was 4 days (IQR 3–5) in severe P. falciparum malaria, 2 days (IQR 1–3) in non-complicated P. falciparum malaria, and 4 days (IQR 2–6) in other cases (p < 0.001). The reported symptoms and clinical signs are presented in Table P. vivax malaria despite adequate chemoprophylaxis with mefloquine. Patients with severe malaria more often presented with hypotension, tachycardia, tachypnea, dehydration, jaundice, hepatomegaly, prostration, vomiting, and diarrhea than patients with non-complicated P. falciparum or non-falciparum malaria. Detailed laboratory findings are presented in Table Initial and maximal parasitaemias were 12.30% (IQR 6.20–22.00) and 17.28% IQR 10.44–28.00) in severe 4–28.00 iP. falciparum malaria. The median length of hospital stay was 11 days (IQR 7–20) in severe P. falciparum malaria, 5 days (IQR 4–6) in non-complicated P. falciparum malaria, and 4 days (IQR 3–5) in other cases of malaria (p < 0.001). The median length of intensive care in severe P. falciparum malaria was 7 days (IQR 1–11).A total of 190 patients (93.6%) were hospitalized. Among the 13 cases (6.4%) treated as outpatients, 9 had non-falciparum malaria, and 4 had non-complicated P. falciparum malaria was initiated in 18/32 (56.3%) patients with parenteral quinine and clindamycin, in 7/32 cases (21.9%) with artemether/lumefantrine or dihydroartemisinin/piperaquine, in 6/32 (18.8%) with mefloquine, and in 1 patient (3.1%) with atovaquone/proguanil. The initial oral therapy was eventually changed to parenteral quinine with clindamycin in 8/14 patients (57.1%). Non-complicated P. falciparum malaria was treated in 65/122 (53.3%) patients with artemether/lumefantrine, in 39/122 (32.0%) with mefloquine, in 17/122 cases (13.9%) with atovaquone/proguanil, and in one case with quinine (0.8%). P. vivax malaria was treated in 13/33 (39.4%) cases with chloroquine, in 8/33 (24.2%) with atovaquone/proguanil, in 7/33 (21.2%) with mefloquine, and in 5/33 (15.2%) with artemether/lumefantrine; data on treatment were missing in two of the patients. Plasmodium ovale malaria  was treated with chloroquine in 4/9 cases (44.4%), artemether/lumefantrine in 3/9 (33.3%), and mefloquine or atovaquone/proguanil in one patient (11.1%). A total of 33/55 patients with P. vivax or P. ovale malaria  were subsequently treated with primaquine. P. malariae infections were treated with artemether/lumefantrine in 2/5 patients (40.0%), atovaquone/proguanil in 2/5 patients (40.0%), and mefloquine in one patient (20.0%).The treatment of severe P. falciparum malaria developed acute respiratory distress syndrome requiring mechanical ventilation in 8/32 patients (25.0%). Other complications of severe P. falciparum malaria included septic shock in 12/32 cases (37.5%), altered mental status in 11/32 (34.4%), severe coagulopathy in 10/32 (31.3%), and acute kidney injury requiring urgent renal replacement therapy in 8/32 patients (25.0%). Bacterial superinfection was diagnosed in 14/32 (43.8%) patients with severe P. falciparum malaria, in 13/122 (10.7%) with non-severe P. falciparum malaria and no patient with non-falciparum malaria (p < 0.001). Relapse or reinfection was diagnosed in 5/35 (14.3%) patients treated with P. vivax malaria (2 previously treated with primaquine). Recrudescent malaria occurred in 7/149 (4.7%) cases of P. falciparum treated with artemether/lumefantrine and one case of P. malariae treated with atovaquone-proguanil. Six cases of P. falciparum recrudescence occurred in adult Czech patients, and one was a Beninese child of 5 undergoing international adoption by a Czech family. The median time from initial parasitaemia clearance to P. falciparum recrudescence was 17 days (IQR 15–21). All recrudescent cases were uncomplicated. There were two lethal outcomes in patients with severe P. falciparum malaria .A total of 15/32 patients (46.9%) with severe This study reports the epidemiological characteristics of imported malaria diagnosed at a tertiary care centre for infectious diseases in Prague, Czech Republic. Among the main strengths of this study are the relatively high number of included patients and the long study period, which enables the identification of potential changes in the epidemiology of imported malaria in Eastern European settings. However, the observations of this single-centre retrospective analysis require confirmation from more powerful multi-centre studies that should include Central and Eastern European countries. Therefore, this study is also a bid for broader cooperation and partnership among all European centres for imported tropical infectious diseases.P. falciparum malaria, which has the most potential to cause severe disease. These observations can be explained mainly by an increasing proportion of travellers returning from sub-Saharan Africa, which accounted for nearly all malaria cases from 2016 to 2019. Tourism is no longer the most prevalent reason for travel in recent years, and there is an increasing trend in the proportions of the other types of travellers. Travellers for business and visiting friends and relatives (VFR) tended to travel for longer periods, significantly increasing their exposure to malaria. Other studies have shown that non-tourism reasons for travel are associated with lower adherence to anti-malarial chemoprophylaxis [Among the main findings of this study is the steadily increasing incidence of malaria cases imported to the Czech Republic in recent years. Moreover, this rise is primarily attributed to The low adherence rates to anti-malarials in this study are troubling but not surprising. At least in part, this may be explained by selection bias, as the study only evaluated patients who acquired malaria during their travel, thus lacking the denominator. However, insufficient adherence to anti-malarial chemoprophylaxis is a global problem described in several studies , 16. In P. falciparum infections. Vygen-Bonnet et al. reported a sharp increase in imported malaria cases had been documented in Germany in recent years, reflecting both the increasing number of newly arriving refugees and a rising trend in malaria acquisition by native Germans traveling to malaria-endemic countries [The number of studies on the epidemiology of travel-acquired malaria conducted in the Central European region remains low. According to the ECDC, the notification rate of malaria in the Czech Republic has ranged between 3 and 4 cases per 1 million population in recent years. Stępień published a report on the epidemiology of imported malaria in Poland in 2014–2018 and a comparison with previous years . Poland ountries –23.P. falciparum malaria tended to present more commonly with gastrointestinal symptoms, including vomiting, diarrhea, and abdominal pain, than patients with non-falciparum malaria. The exact prevalence of diarrhoea and other GI symptoms in malaria remains unclear. However, an association with higher parasitaemia has been suggested by other studies [The initial signs and symptoms of patients with malaria are notoriously non-specific. Notably, both patients with non-complicated and severe  studies , 25. The studies , 17, 18. studies . In this studies , which s studies –29.The most frequent laboratory findings included low platelet counts, mildly elevated liver enzymes and markedly elevated CRP. Anaemia is often mentioned among the classical laboratory findings in malaria, but this was not frequently observed in this study. This may be associated with the earlier presentation and lower parasite burdens in this study, as anaemia tends to be a relatively late manifestation, particularly common in patients with severe disease or individuals living in endemic areas suffering frequent reinfections –32.P. vivax are rarely severe, two crucial clinical considerations should be accounted for. First, while chloroquine is still the drug of choice in most cases of non-falciparum malaria, the resistance of P. vivax has been documented and may be on the rise [P. vivax infection always requires the eradication of liver hypnozoites with primaquine to prevent relapses of latent infection. There were five cases of recurrent disease caused by P. vivax in the present study. However, reinfection could not be reliably excluded in these cases.In this study, vivax malaria was the most common non-falciparum malaria and the most important species acquired outside Sub-Saharan Africa (particularly in Asia and Latin America). While infections with the rise . TherefoP. falciparum malaria and chloroquine-resistant non-falciparum malaria and a treatment option for chloroquine-susceptible non-malaria [P. falciparum infection, regardless of species diagnosis. Combination therapy is now recommended to reduce the risk of selecting for resistant parasite species, as artemisinin resistance was observed in the regions where monotherapy had been previously used  [P. falciparum malaria were treated with artemether/lumefantrine, the most widely used artemisinin-based combination globally [P. falciparum malaria with a different class drug . Quinine (with clindamycin) is still widely used in Europe to treat severe P. falciparum malaria, although randomized trials have shown a clear mortality benefit of intravenous artesunate over quinine [Artemisinin-based combinations represent the treatment of choice for -malaria . Infecti Guyana) –37. In tglobally . Artemetglobally . Howeverglobally , 41. Thiglobally . This prglobally . WHO curglobally . The QUIglobally . However quinine , 46. Int quinine , 48.P. falciparum. A prospective study with data from multiple centres across the country is required to fully describe the epidemiology and clinical spectrum of imported malaria.The main limitations of this single-centre retrospective descriptive study are related to the patient population and data availability. This study included only symptomatic cases diagnosed in acute care settings. Therefore, patients with a clinically inapparent disease or those diagnosed and treated abroad are underrepresented in the study population. In addition, the PCR confirmation and species differentiation were not performed in most cases. As a result, some hidden mixed infections or submicroscopic parasitaemias may have been missed. Finally, the study site lacked the necessary resources for molecular surveillance and investigation of the epidemiologically relevant genetic polymorphisms in Malaria remains the most important tropical infectious disease in European travellers. Its incidence in some countries may be on the rise, especially with the gradual restoration of international travel during the ebbing phase of the COVID-19 pandemic. Primary care physicians and hospitalists alike are again becoming increasingly likely to encounter a case of malaria. As timely diagnosis and treatment are among the defining factors for favourable patient outcomes, improving general awareness of malaria is crucial for tropical medicine practitioners and researchers. In cooperation with European health authorities, post-travel clinics should strive for equitable accessibility of both oral and intravenous ACTs to ensure the best evidence-based patient management in all regions.Additional file 1. The study's underlying dataset, on which the reported findings are based.


Plasmodium infections in Homa Bay County.Long-lasting insecticidal nets (LLINs) have been the primary vector control strategy until indoor residual spraying (IRS) was added in Homa Bay and Migori Counties in western Kenya. The objective of this study was to evaluate the impact of LLINs integrated with IRS on the prevalence of asymptomatic and submicroscopic Plasmodium infection were conducted in residents of Homa Bay county, Kenya. Prior to the start of the study, all study households received LLINs, which were distributed between June 2017 and March 2018. The first (February 2018) and second (June 2018) surveys were conducted before and after the first round of IRS (Feb–Mar 2018), while the third (February 2019) and fourth (June 2019) surveys were conducted before and after the second application of IRS (February–March 2019). Finger-prick blood samples were obtained to prepare thick and thin smears for microscopic determination and qPCR diagnosis of Plasmodium genus.A two-stage cluster sampling procedure was employed to enroll study participants aged ≥ 6 months old. Four consecutive community cross-sectional surveys for Plasmodium spp. infection prevalence by microscopy was 18.5% (113/610) before IRS, 14.2% (105/737) and 3.3% (24/720) after the first round of IRS and 1.3% (11/849) after the second round of IRS (p < 0.0001). Submicroscopic  parasitaemia reduced from 18.9% (115/610) before IRS to 5.4% (46/849) after IRS (p < 0.0001). However, the proportion of PCR positive infections that were submicroscopic increased from 50.4% (115/228) to 80.7% (46/57) over the study period (p < 0.0001). Similarly, while the absolute number and proportions of microscopy positives which were asymptomatic decreased from 12% (73/610) to 1.2% (9/849) (p < 0.0001), the relative proportion increased. Geometric mean density of P. falciparum parasitaemia decreased over the 2-year study period (p < 0.0001).Plasmodium parasitaemia, while the proportion of asymptomatic and submicroscopic infections increased. To reduce cryptic P. falciparum transmission and improve malaria control, strategies aimed at reducing the number of asymptomatic and submicroscopic infections should be considered.These data suggest that two annual rounds of IRS integrated with LLINs significantly reduced the prevalence of  Plasmodium falciparum is the most abundant species (92%) accompanied by Plasmodium malariae (6%) and Plasmodium ovale at 2% [Despite increased malaria control interventions, malaria morbidity and mortality remain high, making the disease a major public health concern in sub-Saharan Africa –3. Out ole at 2% .Plasmodium infections in low transmission areas [The fight against malaria has been scaled up along the Lake Victoria Basin region, where the infection rates remain high. For instance, mass distribution of LLINs occurs every 3 years . Besideson areas , 13. TheWhile the impact of long-lasting insecticidal nets on malaria has been well documented in western Kenya, little is known on the impacts of the addition of IRS on parasite profiles at the population level. Therefore, a study targeting the entire community to identify the prevalence of malaria parasites, asymptomatic and submicroscopic infections was undertaken before the application of indoor residual spraying  and during IRS intervention period. Highly sensitive molecular tools were used to detect submicroscopic parasites and improve species identification. Results of this study will guide policy makers in the health, and other concerned stakeholders with regard to enhanced malaria control and management.o15′–0o52′ South latitude and 34°–35° east longitude. The area has two rainy seasons: “long rains” (April–July) and “short rains” (September–November). Annual rainfall is 250 to 1000 mm. The study area is comprised of irrigated and non-irrigated settings. In irrigated area, a concrete channel based multi-crop irrigation scheme was constructed in 2012 to boost subsistence farming. Non-irrigated areas are located 5–10 km away from irrigated areas as indicated in Fig. P. falciparum infection prevalence was 27% [The study was undertaken in Rachuonyo South and Rangwe sub-counties of Homa Bay County, Kenya located at 0 was 27% .Fig. 1MaThe formula proposed by Naing et al.  was used Cochran . The des Cochran  Hence, tPlasmodium infection was referred to the nearest health center for further examination and treatment.Four community cross-sectional surveys were conducted from February 2018 to June 2019. The first survey was in February 2018 before the first IRS, and the second in June 2018. A similar schedule was executed in 2019 before and after the second IRS. Selection for study participation was based on a two-stage procedure similar to that described by Galway et al. [Plasmodium infections by microscopy. Briefly, thick and thin blood smears were stained with 10% Giemsa for 15 min and examined using a microscope. A total of 200 microscopic fields containing leukocytes in the thick smear were examined before declaring a slide negative. For quality control, each slide was examined by two independent microscopists, In the case of discrepancy, a third reader was involved. To estimate the P. falciparum density, the number of parasites observed in microscopic fields containing based on the 200 leucocytes on a thick smear. This number was then multiplied by 40 assuming 8000 leukocytes/µL blood [Finger-prick blood samples were obtained from study participants to screen for µL blood . The parµL blood . This wa® 3MM filter paper (3 spots). Filter paper was air dried and kept at 4 °C in sealed plastic bags with a desiccant. DNA was extracted from each filter paper using Chelex resin (chelex -100) as described previously [Plasmodium species  since they had been previously reported as the most possible species to exist in study area [P. falciparum, P. malariae, and P. ovale) were used for multiplex qPCR. The qPCR was run on the Applied Biosystems QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific Inc. USA) in a final volume containing 6 µl of PerfeCTa® qPCR ToughMix™, Low ROX™ Master mix (2X), 0.4 µl of the species specific forward and reverse primers (10 µM), 0.5 µl of the species-specific probe, 0.1 µl of double-distilled water and 2 µl of sample DNA. The thermo profile was set as follows; 50 °C for 2 min,  for 45 cycles.Dry blood spots were collected from study participants as described . Briefleviously  with mineviously . The stuudy area . Malariaudy area , 26 withPlasmodium positive test result with the body temperature below 37.5 °C during the time of blood sample collection or in the last 48 h. Blood samples testing negative by microscopy but positive by qPCR were considered to be submicroscopic. All data collected was entered in Excel spreadsheet, cleaned and malaria prevalence from the study area analysed using GraphPad Prism version 8. Differences in prevalence among 4 cross-sectional surveys and age sets were determined using inferential statistics (Pearson χ2). Parasite densities were logarithm-transformed (log (x + 1) and mean difference among age groups or survey periods determined using analysis of variance (ANOVA).An asymptomatic infection was defined as a Plasmodium species infections during the first, second, third, and fourth survey, were 610, 737, 720, and 849 study participants respectively. Age group distribution during the survey period are shown in Table The study team approached 600 households in the study area during the recruitment stage. However, 42 household heads refused to participate, and 70 household stated that they did not have LLINs and were, therefore, excluded. A total of 240 households were chosen at random from the remaining 498 households to participate in the study. These 240 households had a total population of 1060 people. However, 24 of them refused to participate, 40 children under the age of 6 months were excluded, and 36 people from households with more than 4 eligible participants were also excluded. Finally, the total number of participants who met the inclusion criteria was 960 as indicated in Fig. χ2 = 5.117, df = 1, p = 0.02) by microscopy and 25.1% (185/737)  by qPCR. Further decline in parasite prevalence was reported in the third survey with microscopy and qPCR recording 3.3% (24/720) and 10.4% (75/720) respectively. During the last survey, malaria parasite prevalence was finally reduced to 1.3% (11/849) and 6.7% (57/849) by microscopy and qPCR respectively. Overall, the reduction in parasite prevalence during the 4 cross-sectional surveys was significant by microscopy  and by qPCR  as indicated in Table During the first survey (dry season), conducted in February 2018, the parasite prevalence by microscopy and qPCR was 18.5% (113/610) and 37.4% (228/610), respectively. A significant reduction in prevalence was recorded during the second cross-sectional survey (wet season) by both microscopy and qPCR. For instance, parasite prevalence reduced to 14.2% (105/737) , 28.1% (59/210), and 10.2% (31/304), respectively . During the second survey, parasite prevalence among children aged below 5, 5–14 and those aged ≥ 15 years was 15.8% (22/139), 20.7% (59/285), and 7.7% (24/313), respectively . Similarly, during the third survey, Plasmodium infection rate among < 5, 5–14, and ≥ 15 years old was 2.4% (4/163), 6% (15/250) and 1.6% (5/307), respectively . However, during the fourth survey, the parasite prevalence among < 5, 5–14, and ≥ 15 years old was 1.4% (2/144), 2.3% (6/260), and 0.7% (3/445), respectively   during first, second, third and fourth survey were 2229 infected red blood cells (irb)/µl, 353 irb/µl, 578 irb/µl and 184 irb/µl, respectively  , 81.6% (151/185), 68% (51/75) and 87.7% (50/57) of all infections during the first, second, third and fourth survey respectively. Similarly, the proportion of P. malariae during survey 1, 2, 3 and 4 was 0.9% (2/228), 8.6% (16/185), 10.7% (8/75) and 1.8% (1/57) respectively. The infections due to P. ovale only occurred during the first (0.4% (1/228), second (1.6% (3/185), and fourth survey (5.3% (3/57). Mixed species infections due to P. falciparum and P. malariae mostly occurred throughout the study period. Mixed infections involving P. ovale were rare and were reported during the first and second surveys, as illustrated in Table χ2 = 12.97, df = 3, p = 0.005). The geometric mean gametocyte density was: 66.6, 35.9, 48 and 16 gametocytes/µl of blood during survey 1, 2, 3 and 4 respectively .Three malaria species were found in the study population with or 94.3% 15/228, 8χ2 = 80.2, df = 3, p < 0.0001) for the first, second, third and fourth surveys, respectively .The absolute numbers of submicroscopic  infections decreased during the four cross-sectional studies. For instance, the proportion of submicroscopic infections was 18.9% (115/610), 10.9% (80/737), 7.1% (51/720), and 5.4% (46/849)  .. However, the relative proportions of microscopy positive that were asymptomatic increased from 64.6% (73/113) to 83.3% (9/11) . The detection of Plasmodium species infection rate by qPCR was significantly higher than that of microscopy as illustrated in Fig. The rate of symptomatic infections as determined by microscopy decreased during the survey period. For instance, the rate of microscopy positive that were symptomatic during the first, second, third, and fourth surveys, was 6.6% 40/610), 4.6% (44/737), 0.8% (6/720), and 0.2% (2/849), respectively (0/610, 4.Plasmodium infection rates is key towards monitoring the progress of malaria intervention programmes initiated within endemic regions [Plasmodium infections before the introduction of IRS and during two rounds of IRS in an area where LLINs were widely used. Four cross-sectional surveys were conducted and findings indicated a sustained reduction in Plasmodium infection rates especially after the rollout of IRS programme. Just before the onset of IRS, the Plasmodium prevalence rate by both microscopy and qPCR was at 18.5% and 37.4%, respectively. However, during the last survey, the prevalence of malaria parasites had declined to 1.3% by microscopy and 6.7% by qPCR.This study was undertaken to document the impact of IRS and LLINs on parasitological profiles of malaria parasites in a human population in an endemic site in western Kenya. Accurate determination of  regions . To accu regions . The prePlasmodium infection rate and parasite density decreased significantly.The geometric mean parasite density significantly declined from 2229 parasites/µl of blood before intervention to 184 parasites/µl of blood after 2 rounds of IRS. The most plausible explanation to such a drastic reduction in parasite prevalence and parasite density could be due to very low parasite transmission after the application of IRS. For instance, in a neighboring Migori county, where a similar intervention was initiated, malaria transmission was reported to have declined significantly , Mali 3 and TanzPlasmodium infections, older children, for example, develop an improved immune response that significantly suppresses the multiplication of asexual parasites.Parasite prevalence determined by both microscopy and qPCR were reported to be highest among the age group 5–14 years old. This is in agreement with other previous studies –36. A liPlasmodium infections with low parasite densities may be easily tolerated, increasing the number of asymptomatic cases. Similarly, the current study area has historically been a malaria endemic region. Areas undergoing perennial Plasmodium infections endure elevated rates of asymptomatic infections [Plasmodium infections may trigger strong immune modulation resulting in reduced clinical symptoms [The prevalence of asymptomatic and symptomatic cases decreased during the study period. This could be due to sustained malaria vector control in the study area through the use of IRS and LLINs. It was important to note, however, that the relative proportions of microscopy positives that were asymptomatic increased with each subsequent survey. This observed trend could be explained by a significant decrease in parasite prevalence and density. For example, the study revealed a complete elimination of hyperparasitaemia and high parasite density infections during the third and fourth survey. Likewise, there was a significant decrease in infections with moderate parasitaemia. As a result, the few existing fections , 44. Thifections . Frequensymptoms –49. HowePlasmodium infection rate. However, the relative proportion of submicroscopic infections increased during study period. As the parasite densities decrease, submicroscopic infections may increase due to microscopy’s low detection limit [The absolute number of submicroscopic infections decreased with decrease in on limit , 27. Thion limit . Submicron limit  as well on limit . The preon limit , the curPlasmodium species detected in the current study varied with P. falciparum accounting for the highest proportions. This is consistent with other observations along the lake region, western Kenya [P. malariae and P. ovale. While the number of P. falciparum infection cases reduced, the proportion of P. malariae cases remained relatively stable over the course of four surveys. Most importantly, these P. malariae infections occurred at very minimal parasite densities, hence the majority of cases were diagnosed using qPCR. This is similar to a previous study which reported subpatent P. malariae with low parasite densities [P. malariae infections may be a challenge to microscopy based diagnosis leading to misdiagnosis or underreporting of malaria cases [P. malariae infections is paramount to minimize cases of nephrotic syndrome, which are associated with excessive mortality [rn Kenya , 54. Theensities . Low paria cases . Accuratortality .Plasmodium species by qPCR was significantly higher compared to that of microscopy. This is consistent with previous studies which compared the two diagnostic tools [P. malariae are common.Detection rate of ic tools , 58. Molic tools , 60. HigPlasmodium infection rate prior to net interventions. Finally, due to the study's cross-sectional design, it was difficult to directly link the lower Plasmodium infections to LLINs and IRS interventions.The study was limited to two sub-counties, hence the findings cannot not be generalized to the entire county. Besides, the study did not include the Plasmodium infection rates and parasite densities following the introduction of the indoor residual spray programme. The reports also show that, while the absolute number of asymptomatic and submicroscopic infections decreased in the study area, the relative proportion of both asymptomatic and submicroscopic infections increased. This is likely to negate the progress achieved towards vector control. The study also demonstrated a large reduction in clinical malaria, which is an important public health benefit.The study findings report significant reduction in


Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work. Malaria has turned into a major risk to individuals worldwide as one of the main reasons for causalities across the world. It is a curable infectious disease caused by a protozoan parasite that can be life-threatening. As per the latest report of the World Health Organization (WHO), in 2019, 229 million malaria cases were detected worldwide, and causalities were reached to 409000. In 2018, 228 million malaria cases were detected, and causalities were reached 411000 [P. falciparum species affected [In 2016 and 2017, about 1.09 million and 0.84 million malaria cases were registered in India, in which most of the malaria cases were Anopheles mosquitoes and human beings are the two main hosts infected by the parasite. When female Anopheles mosquitoes desire to foster their eggs, they bite and draw blood from the human body. If a parasite infects that person, then that same infected parasite blood is found in the mosquito and that parasite reproduces and develops in the mosquito body. When that infected mosquito bites another person, parasites containing the salivary gland are transferred into that person's blood [Dr. Ronald Ross first discovered malaria transmission in the human body by mosquitoes in 1897 . The mai's blood . After t's blood . MalariaPlasmodium falciparum , Plasmodium vivax (P. vivax), Plasmodium ovale , Plasmodium malaria , and Plasmodium knowlesi (P. knowlesi). Among all five species, the first four are the most common species, which occur in the human body. The fifth species is P. knowlesi mostly occurs in monkeys that live in South-East Asia forests. But, in past years, some cases of P. knowlesi malaria occurred in the human body. The most common species found in the human body is P. vivax, but the most dangerous species is P. falciparum [There are five different protozoan parasite species, which are the main cause of malaria in the human body. These are lciparum .  FigureAll species of protozoan parasites are morphologically different. At every stage of its lifecycle, each species changes in its size, color, shape, and morphology. These various stages of every species are ring, trophozoite, schizont, and gametocyte, as shown in The main reason for the high mortality rate is the late detection of malaria. In medical science, for the detection of malaria, microscopic examination is the gold standard. A microbiologist manually counts affected RBC under the microscope to examine the patient's blood sample, which is a very time-consuming and highly tedious process. The accuracy of this process is entirely dependent on microbiologist expertise . Hence, The pattern of total worldwide malaria patients is illustrated in The paper's contributions are as follows: (i) a comprehensive review has been conducted on the state-of-the-art techniques for malaria diagnosis that have been published in the last decade; (ii) various types of automated computational methods such as preprocessing, segmentation, feature extraction, and classification for diagnosing malaria have been discussed in detail; (iii) additionally, different types of machine learning and deep learning models, as well as their accuracies for malaria parasite detection and diagnosis, have been discussed; (iv) moreover, several types of blood smear image datasets for malaria diagnosis have been identified; and (v) various challenges and issues with the already implemented techniques and scope of future work have also been discussed.The paper is organized as follows: (i) Malaria is a disease in which symptoms appear after 7 to 15 days. Primary symptoms are headache, vomiting, fever, pain, chills, etc. These symptoms could be an indication of malaria, although many diseases have the same symptoms. Hence, some techniques are needed that can diagnose malaria correctly. For malaria diagnosis, different techniques have been developed such as microscopy blood smear examination, cytometry, rapid diagnostic test (RDT), polymerase chain reaction, and fluorescent microscopy. Still, for diagnosing malaria, the primarily used techniques are (a) microscopic thick and thin blood smears examination and (b) rapid diagnosis test in medical science .In this, a laboratory examination is performed in which a clinician divides the blood sample into two parts on the slide. One is called a thick blood smear, and another is a thin blood smear. After that, a clinician manually counts the affected RBC under the microscope. A thick blood smear helps clinicians detect the presence of malaria parasites, and a thin blood smear helps identify the species of the parasites causing malaria. All the steps for malaria detection using microscopic blood smears examination are shown in Advantages of the microscopic technique are as follows: (i) a clinician can distinguish the different stages of malaria species at a very low cost using microscopic method and (ii) microscopy technique for malaria detection is more effective as compared to rapid diagnostic tests as it can count affected RBC very efficiently. Apart from the advantages of microscopic techniques for malaria detection, some challenges are also there. Microscopic thick and thin blood smears examinations technique accuracy depends on microbiologist experience. To detect and diagnose malaria through a microscope, a microbiologist may have to count malaria-affected RBC manually, which is a highly tedious and time-consuming task . It is fRapid diagnosis test or antigen test is a small kit used to detect antigens derived from malaria parasites. To identify malaria, a drop of blood is inserted into the kit from the given hole, and internally, this device performs the tests and provides the result in minimum time. RDT kit functioning is shown in Advantages of the RDT kit are as follows: (i) it is significantly faster than manual cell counting techniques, and it gives instant results; (ii) for the use of the RDT kit, no expertise is required; and (iii) it is beneficial in endemic regions. Instead of the advantages of the RDT kit for malaria detection, some challenges are also there. As per the analysis of different studies, the results of this technique are less accurate, and any wrong result can affect the patient's treatment . AnotherHence, after studying different techniques of malaria diagnoses and their advantages and challenges, researchers observed that a computer-assisted malaria detection technique would be required. A computer-assisted malaria detection technique increases the performance of existing techniques by avoiding its limitations in terms of accuracy, instant results, dependency, and requirement of the expert microbiologist.In medical science, the computer plays a very crucial role. Different automated computational methods are used for the diagnosis of multiple diseases. Ultrasound images, magnetic resonance imaging, X-ray images, and computed tomography images are used to diagnose different diseases of human anatomy using computerized imaging techniques. The computer-assisted diagnosis technique for malaria is based on the microscopic technique, which is performed by computer with the help of machine learning algorithms and computer vision techniques. This is the technique in which digital thin and thick blood smear images are used for the detection of malaria parasites automatically. Different steps of automated diagnosis of malaria are image acquisition, preprocessing, red blood cell detection and segmentation, feature extraction, and selection and classification (parasite identification and labeling). The stepwise process of automated computational methods for malaria parasite diagnosis is shown in Digital images of blood smear samples are required to detect malaria in a patient using computer vision image processing and machine learning techniques. Each patient's blood smear sample is distributed into two parts: thick and thin blood smear images. Most computer-assisted detection studies use thin blood smear digital images, and very few researchers have worked on thick digital blood smear images .After analyzing the different image acquisition techniques in Furthermore, Preprocessing is a technique used to remove the unwanted noise and produce high contrast digital blood smear images for the next step. When different resources take blood smear images, the images are corrupted by noise, and thus, visualization of the images is not good. Due to this problem, further steps of segmentation and classification are challenging to implement, and it produces poor results. Hence, certain preprocessing techniques have been used to remove that unwanted noise from images. Preprocessing techniques remove the noise from the image for better visualization, which is very useful for further analysis . As showSegmentation is the process in which digital images are disjoint into nonoverlapping regions. Each disjoint image typically corresponds to other parts of an image object. Once each digital image object is isolated, each object can be easily measured and classified. In the literature, different segmentation techniques have been applied on digital blood smear images to detect ROI (region of interest).Das et al. have developed an automated system for classifying malaria at different stages. The researcher used the watershed segmentation technique in their research work for the segmentation of thin blood smear digital images. This technique provided better results for detecting erythrocytes from the whole blood smear image . FurtherThe segmentation algorithm has been used on different color spaces to find the optimal performance of digital blood smear images . The resTo detect the exact radius of RBC, the circle hough transformation method was introduced by Ma et al. . Otsu thDeep learning techniques are also beneficial in image segmentation. Researchers for image segmentation have proposed many deep learning techniques. For image segmentation, a fully convolutional neural network-based deep learning technique has been proposed by Long et al.  and WangSegmentation techniques on blood smear images used in different studies are summarized in Feature extraction after segmentation is a prerequirement for feature selection and classification. The objective of feature extraction is to recognize and characterize an object whose dimensions are very nearest or similar for objects in the same class and different for objects from a different class. It reduces the computational complexity of the other processes and provides accurate and reliable recognition to unknown, unrecognized data.To develop a good classification model, a good feature selection method plays a very important role. Classification model processing time and results of classification model depend on selection and type of the number of selected features or attributes. In the literature, several researchers have developed and used different feature selection methods.To extract the features of haralick textures, mean, entropy, roughness, homogeneity, and standard deviation, Das et al. suggested gray-level co-occurrence matrix . To extrVarious features of extraction and selection techniques implemented by various researchers for malaria blood smear images are shown in Classification is a technique to identify a pattern that belongs to which class. In this literature, different authors developed different classification techniques to identify a patient, whether he or she is malaria-affected or not. So, there are two classes to detect whether the patient is affected by malaria or not.Vijayalakshmi and Kanna have introduced a deep learning approach to classify infected and noninfected falciparum malaria. The presented technique was achieved by the visual geometry group (VGG) network and SVM. In this, 1530 malaria digital corpus images have been used for training and testing the model. In this, the transfer learning approach to train the model is used in which we trained the top layer of the model and freeze the rest out of the layers approach applied. For the classification of infected or noninfected falciparum malaria, the given model obtained 93.13% accuracy .For the classification of malaria-infected stages from thin blood smear images, Das et al. used five different classifiers to classify the malaria-infected stages. These five classifiers are Naive Bayes, Logistic regression, Radial Basis Functions (RBF) neural network, Multilayer perceptron neural network, and classification and regression tree. In this, 888 erythrocytes infected and noninfected image dataset is used. Out of this, 592 labeled images are used to train the classifiers and the remaining are used for testing the classifiers. The experimental results show that among all five classifiers, the multilayer perceptron network has provided better results than the other four classifiers on the 750 images dataset .P. falciparum, P. malariae, and P. vivax [Seman et al. developed a multilayer perceptron network (MLP) to classify different malaria parasite species from thin blood smear images. This work classified three different species from malaria parasites: P. vivax . The autOtsu thresholding method is used by Malihi et al. for the classification of four species of malaria parasites in blood smear images. This technique has provided better results in comparison with other techniques. In this, 363 blood smear images are used and obtained 91% accuracy .Further, Anggraini et al. have classified the different stages of malaria parasites using a Bayesian classifier on 110 thin blood smear images and obtained 93.3% accuracy . MinimumKareem et al. have developed an automated technique for detecting malaria parasites in thin blood smear images. In this, a dataset of more than 200 images is used. Two methods of classification for parasites are used. The first one is based on relative size and morphology, and the second is based on intensity variation. The final results of the developed model have shown an accuracy rate of 87% .Prasad et al. have presented a decision support system approach to classify the infected and noninfected malaria parasites in thin blood smear images. In this, 200 thin blood smear images have been used, and 96% accuracy has been obtained . Rosado Mohammed and Abdelrahman have given a technique for detecting and classifying malaria from 160 thin blood smear images taken from the Centre for Disease Control and Prevention (CDC). To extract the RBC from blood smear images, researchers used morphological processing. This technique found the parasites and overlapped cells in the image. Based on the number of RBCs in each image, RBC is classified into two classes: infected and noninfected cells. After that, a normalized cross-correlation algorithm was employed to classify the affected blood smear parasite into four different malaria species. The given method has produced 95% accuracy for detection . SaiprasBibin et al. have given an automated technique to detect malaria parasites in peripheral blood smear images. The given binary classifier is based on deep learning, which used 1978 images to train and test the technique and achieved 96.21% accuracy . Simon eDave et al. used adaptive thresholding, erosion, and dilation operations to diagnose malaria from 117 blood smear microscopic digital images and got 89.88% accuracy . OliveirMohanty et al. have presented the autoencoder (AE) neural network unsupervised technique to identify malaria in blood smear images. In this, the AE technique has been compared with the SOM technique. The AE technique obtained 87.5% accuracy compared to the 79% accuracy of the SOM technique. 1182 blood smear images have been used to perform experiments . MoralesAfter the analysis of In the last decade, a lot of experiments have been done in the area of automated detection of malaria to reach the current state-of-the-art. In this study, different computational methods implemented on various stages of computer-assisted techniques for detecting malaria parasites using blood smear images have been examined. Image acquisition is the first and very important step for automatic detection of the malaria parasite. The present study shows various techniques for acquiring digital blood smear images, but the light microscopy technique is the most widely used and liked technique by researchers. There is a number of computational methods out of which preprocessing is the first step in image analysis.Preprocessing is one of the crucial stages implemented on acquired digital blood smear images. It plays a crucial role in detecting infected RBC by removing the unwanted noise and producing high contrast digital blood smear images without demolishing the image features. As per the current study, median/mean filter, morphological filter, Laplacian filter, Susan filter, and Gaussian low-pass filter are mostly used techniques by researchers to remove unwanted noise and increase the contrast of the images. Segmentation is the next stage after the preprocessing, which is used to segment the RBC to detect malaria parasites using blood smear images to facilitate the classification process. As per the study of literature, mostly used segmentation techniques by researchers are as follows: (i) Otsu thresholding for segmentation of parasite RBC; (ii) Marker-controlled watershed and Edge detection algorithm is used at the segmentation phase; and (iii) for the segmentation of overlapping cells, the watershed algorithm has been widely used.After segmentation, blood smear images have been classified to diagnose malaria-infected or not infected by feature extraction and selection techniques. As per the study, Color features, Texture features, and Morphological features have been mostly used feature extraction techniques for early diagnosis of malaria from blood smear images. From the literature, it has been observed that the maximum accuracy of 95.03% has been achieved by CNN based deep learning model in comparison with the VGG-SVM model , 126.A thorough review of the literature on automated analysis of malaria parasite using blood smear images yielded the following challenges and future directions:The accuracy of an automatic image classification model depends upon multiple aspects such as analysis of the digital blood smear image depend on the staining method, magnification factor of an image, condition of nearest environment where the digital image has been collected like the background of the image, light in the room, and most important quality and position of the camera. Therefore, a standard digital blood smear dataset is necessary to test and validate the model to obtain efficient and reliable results.Many researchers have performed their experiments and published their articles in the same area. Moreover, an automated computational-based computer vision method, which should be efficient and effective for automated detection of the malaria parasite from blood smear images, needs to be improvised according to the requirement of the community.The community requires (i) standard image dataset because researchers' datasets are mostly unstandardized. The digital blood smear dataset depends on the characteristics and quality of the microscope as all digital images of blood smears are taken by a digital camera attached to a microscope. So, a well-standardized dataset is most important for a machine learning algorithm for automated detection of malaria. (ii) In the literature, developed methods can recognize only one type of malaria parasite . But, thThis study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. This study reviews and discusses computer vision and image analysis works that target the automated detection of malaria on blood smear images. In this paper, we have discussed the present facts of necessary components of computer-assisted technique: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, (iv) feature extraction and selection, and (v) classification, which have been used to diagnose malaria parasite from blood smear images suggested by various researchers. Digital blood smear images taken from a microscope may affect how and which malaria parasites are detected. After analyzing segmentation and classification state of the art techniques, it has been observed that future computer-assisted techniques should be based on standard datasets and magnification factors to detect malaria parasites. The complexity of different classifiers of machine learning that are based on deep learning increases as the number of layers increases. To achieve efficient and reliable results, a large dataset is required for training and testing. With the help of computational methods such as data augmentation and deep learning methods, the computer-assisted method can obtain better results.However, some state-of-the-art techniques are presented in the literature, but still, there is a huge scope of future work, which may help the microbiologists in the detection and diagnosis of the malaria parasite at an earlier stage to reduce the mortality rate such as (i) different computational methods that are used to collect blood smear images physically can be studied more to enhance the segmentation results to detect infected RBC very effectively. Hence, an efficient computational method of infected RBC segmentation can be developed. (ii) Various feature extraction methods such as color features, texture features, and morphological features , 108 canIn the field of malaria detection from blood smear images, the contribution of many research publications is noteworthy. However, this study has tried to present opinions to the microbiologists and technical community. It will be very helpful for them to generate an effective and efficient computer-assisted technique for malaria detection at an early stage. , 130.


Plasmodium parasites, the causative agents of malaria, are single-celled organisms that cannot survive outside their hosts; thus, thost-pathogen interactions are fundamental for parasite’s biological success and to the host response to infection. These interactions are often combinations of biochemical and mechanical factors, but most research focuses on the molecular side. However, Plasmodium infection of human red blood cells leads to changes in their mechanical properties, which has a crucial impact on disease pathogenesis because of the interaction of infected red blood cells with other human tissues through various adhesion mechanisms, which can be probed and modelled with biophysical techniques. Recently, natural polymorphisms affecting red blood cell biomechanics have also been shown to protect human populations, highlighting the potential of understanding biomechanical factors to inform future vaccines and drug development. Here we review biophysical techniques that have revealed new aspects of Plasmodium falciparum invasion of red blood cells and cytoadhesion of infected cells to the host vasculature. These mechanisms occur differently across Plasmodium species and are linked to malaria pathogenesis. We highlight promising techniques from the fields of bioengineering, immunomechanics, and soft matter physics that could be beneficial for studying malaria. Some approaches might also be applied to other phases of the malaria lifecycle and to apicomplexan infections with complex host-pathogen interactions.Forces and mechanical properties of cells and tissues set constraints on biological functions, and are key determinants of human physiology. Changes in cell mechanics may arise from disease, or directly contribute to pathogenesis. Malaria gives many striking examples.  Plasmodium protozoan parasites of the phylum Apicomplexa. Malaria killed more than half a million people in 2020  and cytoadhesion (Section 2), where such technologies have generated paradigm shifts in our knowledge of malaria and important insights into potential new intervention strategies. Section 3 will describe the biomechanical determinants of invasion and cytoadhesion in other species of Plasmodium. This review takes a problem-oriented approach, to show how specific research questions in malaria have been addressed using biophysical techniques, hoping to inform the reader in the importance of biophysics in infection, and inspire to adopt similar approaches to tackle their own research questions.In this review, we focus on disruptive technologies that have been used to investigate the physical traits of Plasmodium blood stage, which starts when merozoites recognise and invade human RBCs. The invasion process is essential for parasite survival and replication, and consists of a sequence of mechanical and biochemical events that are precisely timed and tightly regulated. During invasion parasites are extracellular and hence exposed to the antibody mediated immune system, representing a potential target for vaccine and drug development. Advanced microscopy techniques such as cryo-electron tomography  pre-invasion, ii) recoil phase, and iii) internalisation  are descPlasmodium invasion is given by the 4D lattice light-sheet microscopy (LLSM), described by the pioneering work of Chen and colleagues  and receptors on the red cell membrane - with coat filaments of 20-150 nm length joining the two surfaces . Subsequ2+ signal in the RBC triggering phosphorylation of specific proteins and the subsequent rearrangements of the RBC cytoskeleton that facilitate the insertion and anchoring of rhoptry proteins and junction formation  . Only treatments like heparin that block invasion at the early stage show a reduction in the detachment force attesting that the force measured comes from ligands and receptors participating in the pre-invasion phase (But how strong is the attachment between the RBC and the invading merozoite? Crick  ± 8 pN) . Within  ± 8 pN) . The maion phase . The eleon phase  adds to on phase  .Merozoites were often observed laterally attached to Dantu RBCs but seldom poised for invasion, a recurrent feature observed also in invasion attempts of denser RBCs . Invasio calcium . An anal calcium : some din vitro , others n vitro . Since tSection 1.2), and active parasite-induced processes such as RBC cytoskeleton remodelling by merozoite ligand injection (Section 1.2) and merozoite motor contribution   motion  . Merozoirozoites  is the soridium  and BabeBabesia . As a re) motion  and thisrozoites .P. falciparum merozoites were for a long time thought to not display the gliding motility of other stages, but Yahata et al.  . This ison genes . Moving ytometry , a technSections 2.1, 2.2 are summarised in P. falciparum-infection prevented iRBC passage through small diameter constrictions. Later, wedged PDMS capillary constrictions were used to determine the smallest diameter a cell can pass through without increasing its surface area or lysing. Overall, this study found that schizonts lose the property of flowing through the narrowest constrictions  trophozoites, which are a result of changes in viscosity and not surface area-to-volume ratio  is mediated by the variant adhesion protein family P. falciparum erythrocyte membrane protein 1 (PfEMP1), which is encoded by a repertoire of approximately 60 var genes, and expressed at the surface of the iRBC  have been proved to concur to iRBC membrane rigidity as assessed by microsphere filtrations and ektacytometry  due to fluid shear stress caused by blood flow  and physical  parameters , howeverPf-iRBC and merozoites. Phagocytes recognise target cells, particles, or debris which stimulates the cell to form a cup-like protrusion that spreads over the target and resulting in its eventual engulfment. The spatiotemporal forces that are exerted on a particle during phagocytosis have recently been determined by microparticle traction force microscopy and LLSM, revealing that macrophages generate F-actin and myosin-I rich “teeth”-like protrusions , 48 , and 72 hours , and number of daughter merozoites in a schizont (up to 16 for P. knowlesi and to 24 for P. vivax). P. knowlesi merozoites are 2-3 μm, twice the size of their falciparum counterparts, and therefore easier to follow by video microscopy. The first images of malaria merozoites were recorded in 1975 by Dvorak et al. , it was demonstrated for the first time that the apical complex is located at the wider end of the zoite (P. knowlesi have longer-lived (316 s) merozoites that glide onto RBC surfaces at 1.1 μm/s, faster than P. falciparum. Moreover, they can glide across multiple human RBC, up to 7, before selecting a cell to invade, and this behaviour resembles the travel of sporozoites along several hepatocytes before invading one  on their surface that is progressively lost during reticulocyte maturation into erythrocyte. Invasion accelerates the ejection of host material, including CD71, completing the maturation by 3 hours post invasion . MG is funded by the EIPOD4 fellowship programme (Grant agreement number 847543). PC and his lab are funded by the EPSRC (EP/R011443/1). JR and his lab are funded by the Wellcome Trust (220266/Z/20/Z). MB and her lab receive core program funding of the European Molecular Biology Laboratory (EMBL).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.