Plasmodium falciparum develop clinical malaria is still limited. Correlates of immune protection are often sought through prospective cohort studies, where measured host factors are correlated against the incidence of clinical disease over a set period of time. However, robustly inferring individual-level protection from these population-level findings has proved difficult due to small effect sizes and high levels of variance underlying such data. In order to better understand the nature of these inter-individual variations, we analysed the long-term malaria epidemiology of children ≤12 years old growing up under seasonal exposure to the parasite in the sub-location of Junju, Kenya. Despite the cohort’s limited geographic expanse (ca. 3km x 10km), our data reveal a high degree of spatial and temporal variability in malaria prevalence and incidence rates, causing individuals to experience varying levels of exposure to the parasite at different times during their life. Analysing individual-level infection histories further reveal an unexpectedly high variability in the rate at which children experience clinical malaria episodes. Besides exposure to the parasite, measured as disease prevalence in the surrounding area, we find that the birth time of year has an independent effect on the individual’s risk of experiencing a clinical episode. Furthermore, our analyses reveal that those children with a history of an above average number of episodes are more likely to experience further episodes during the upcoming transmission season. These findings are indicative of phenotypic differences in the rates by which children acquire clinical protection to malaria and offer important insights into the natural variability underlying malaria epidemiology.After decades of research, our understanding of when and why individuals infected with P. falciparum malaria endemic areas acquire a general state of immunity against clinical malaria through repeated exposure to the parasite. This process of naturally acquired protection is still poorly understood but believed to involve the build-up of a repertoire of immune responses against the myriad of antigenic targets that the parasite displays over its lifecycle duringin vivo infections. Although protection against life-threatening disease may be acquired through a small number of infections only, individuals remain prone to experience clinical episodes throughout childhood and sometimes even into their late teens or early adulthood, depending on the intensity of transmission .Individuals growing up in. Unfortunately, finding robust correlates of protection is made complicated by at least two factors. First, the observed effect sizes are often small, in particular with respect to the variability of the underlying data, and it is difficult to ascertain how much these reflect the true effects sizes due to the many confounding factors influencing the observations. Second, protection itself is not a dichotomous phenotype but rather related to an individual’s risk, or probability of developing disease if infected. The latter is particularly problematic because in many cases we simply do not know whether someone got infected during the study period or not.Research into the complexity of clinical protection often relies on cohort studies, where antigen-specific immune responses are correlated against the incidence of disease. These studies have been key in advancing our understanding of the role of variant surface antigens in both disease severity and natural acquired protection, for example, and are instrumental for the discovery of novel candidate targets for vaccine researchper se, Olotuet al. previously proposed an exposure index, which provides a quantitative marker for the risk of infection experienced by an individual in space and time. What this and other research found was that disease prevalence, or transmission intensity, is not necessarily homogeneous across space or time. In fact, even small geographic regions can exhibit malariahotspots, where individuals have a notably higher chance of getting infected than in surrounding areas. The temporal stability of these hotspots is variable, however. Whereas some hotspots can be persistent because of environmental or geographic factors, such as proximity to standing water and so to mosquito breeding sites, others might just persist for the duration of a transmission season or two. One way or the other, this heterogeneity in exposure must be considered as an important source of variability in an individual’s risk of experiencing a clinical episode in a given year, irrespective of their degree of protection.In order to capture some of the spatial and temporal variation in disease transmission, and thus some of the uncertainty underlying the risk of an infectionLongitudinal cohort studies are ideally placed to offer more detailed insights into the process of naturally acquired protection e.g.. That isThe study was conducted through the KEMRI-Wellcome Trust Research Programme (KWTRP), Kilifi, Kenya. The children investigated were part of the long-term birth cohort in Junju. Recruitment into the cohort was voluntary following sensitisation of the whole area without specific spatial or demographic selection criteria, such thatthe cohort provides an unbiased spatial representation of the village as a whole. Children were recruited into the cohort at birth and actively monitored on a weekly basis for detection of malaria episodes until 15 years of age. SeeP. falciparum (Pf) episodes, defined as a body temperature > 37.5°C and 2,500 parasites per microlitre of blood, are diagnosed during weekly active surveillance, where auxiliary body temperature and/or recent history of fever were recorded. Blood samples were taken from febrile children andPf infection initially detected by rapid diagnostic test (RDT) and confirmed by microscopy. Apart from the children’s infection status and history of clinical malaria, we also had access to their date of birth, spatial location of their homestead, and sickle cell trait status  individuals were removed from this analysis).Approval for human participation in this cohort studies was given by Kenya Medical Research Institute Ethics Research Committee, and research was conducted according to the principles of the Declaration of Helsinki, which included the administration of informed consenting in the participant’s local language.n = 544, total number of samplesN = 3767). Unless stated otherwise, these samples include children with the known malaria protective sickle cell trait . Note, throughout the analysis we refer ton as the number of individuals, and refer toN as the number of samples .Our statistical analyses are based on a subset of samples taken between 2006 and 2018 and include children between the ages of 1 and 12 years , which provides a summary statistic of the annual prevalence at locationi in yeart by summing over all individualsj with recorded malaria episodes in that year in the neighbourhood ofi, weighted by their spatial distances,ijD. It is given asTo capture and compare the spatial distribution of malaria over time within this cohort we devised aj,tZ is a binary operator indicating whether whether the individual had a recorded episode or not. Note, locations and individuals are used somewhat interchangeably, which also accounts for the occasions where multiple children from the same homestead are simultaneously enrolled. Although clustering at the individual household-level has previously been described  has a significant influence on the spatial heterogeneity ofPI, with smaller values leading to smaller differences inPI between distant locations, and vice versa. Initial attempts to estimatea form the data were not satisfactory due to large variations between estimates based on individual years. It was therefore decided to determinea by maximising the correlation between the resulting prevalence index and clinical episode for all individuals and all time points (PI ~Z).The spatial weighting factor a determines how rapidly the influence of an infection atGotEpisode, a binary response variable (yes/no) indicating whether an individual experienced a clinical episode in a given year or not. The explanatory variables werePI,Age,BirthQuarter, andGenotype . To test the effect of an individual’s previous episode history, we built an additional model usingPrevEpisodes along-sidePI andAge, and the interaction betweenAge andPrevEpisodes, as explanatory variables. Here,PrevEpisodes refers to the child’s number of previously recorded clinical episodes. In both cases we included a child-specific intercept (commonly known as a random effect in the frequentist literature), to accommodate the longitudinal nature of these data, where individuals were repeatedly sampled over multiple years over the duration of the study. To assess the effect of between-child variation, we compared the model against an equivalent non-hierarchical model assuming complete independence of all data points. Model comparison was done based on the models’ expected log predictive density (ELPD) using Pareto smoothed importance-sampling leave-one-out cross-validation (PSIS-LOO) from theloo package in R. All continuous variables were centred and scaled (i.e. set mean = 0 and standard deviation = 1) to improve model convergence and to allow for better comparison between the estimated effect sizes. A zero-mean normal prior (with standard deviation = 10) was placed on all regression coefficients, and a gamma prior  was placed on the standard deviation for the group-level effect (i.e. the standard deviation for the child-specific intercepts). The model parameters were jointly inferred by means of MCMC sampling, using therstanarm (version 2.21.1) R package. A total of 2000 iterations (including 1000 iterations for warm-up) were used for each of four chains run in parallel. Convergence was assessed via trace plots and the Rhat convergence diagnostic. Marginal effects for the explanatory variables on the response were plotted using themarginal_effects function in thebrms (version 2.13.5) R package. R version 3.6.3 was used to produce all the figures and perform all of the analyses.In order to determine potential risk factors underlying an individual’s probability of experiencing a clinical malaria episode, we built a Bayesian hierarchical logistic regression model. The modelled outcome wasPI, see Methods) for the years 2007 to 2018 inPI is based on recorded symptomatic episodes in children only we cannot say whether the observed spatial patterns are indicative of dynamically changing transmission hotspots or of potential heterogeneities in the immunity landscape across the cohort, or both.The strong inter-annual variability is also mirrored in the spatial distribution of malaria prevalence, as shown by means of the prevalence index , age and birth quarter on the probability of a child experiencing a clinical episode in a given year.The exploratory analyses presented above have revealed a high degree of heterogeneity in both malaria prevalence and in individual episode histories. In order to determine if and by how much the latter is simply a function of the former, and to what degree other factors have an independent effect on an individual’s risk of experiencing a clinical malaria episode we built a Bayesian hierarchical logistic regression model (see Methods). Specifically we investigated the effect ofPI) has the strongest effect on the risk of a clinical episode . Equally expected is the protective effect of the sickle cell trait . In line with the above exploratory analysis , individual-level differences in susceptibility could have a significant effect on the risk of experiencing a clinical episode in a given year. Apart from the well-known protective sickle cell trait, these differences may stem from the birth time of year, for example, as demonstrated here, as well as other inter-individual differences accounted for by the hierarchical nature of our model .Our results suggest that beside exposure, by means of disease prevalence in the surrounding area  differences in the qualitative and quantitative nature by which children experience clinical malaria episodes and acquire clinical protection under repeated exposure to the parasite.P. falciparum malaria. Our analyses reveal high levels of spatial and temporal heterogeneity in malaria incidence, which lead to significant differences in exposure levels to the parasite as children grow up in this cohort. However, we found that the variability in the rate at which individuals experience clinical episodes is not solely explained by exposure alone but points towards child-specific differences in disease susceptibility. We believe that these are important yet often neglected sources of variation in the data collected from prospective cohort studies aimed at identifying correlates of protection.Here we used long-term epidemiological cohort data to investigate individual-level differences in children’s development of clinical protection against. These findings, which are broadly in line with the results presented here, add further evidence that children seem to exhibit inherent (phenotypic) differences in their susceptibility to clinical malaria episodes. Here we explicitly accounted for the inter-individual variability and concentrated primarily on an individual’s risk of experiencing a clinical episode in a given year or not. What was interesting to observe is that age itself only appeared to have a significant effect when accounting for this inter-individual variability. That is, although age usually needs to be factored in as a confounding factor when looking for immune correlates of protection, as older children are more likely to have experienced more episodes and have thus developed a higher degree of protection, we find that in this relatively low transmission setting and below the age of 12 years it only has a marginal effect on someone’s risk of experiencing a clinical episode.It has previously been reported that some children growing up in a malaria endemic region appear to suffer from excess malaria episodes compared to other children before reaching a similar state of clinical protection, even when accounting for exposure and ageet al. but yields more spatially smoothed estimates and in this case revealed clear temporal trends in the spatial distribution of infection prevalence  had a reduced risk of experiencing clinical episodes, which also manifested in a lower number of total episodes compared to other age-matched children under similar exposure conditions. In terms of timing, being born during Q4 in this cohort means that infants may acquire their first infection at the age of 4–6 months towards the end of maternal protection, assuming that the main transmission season in Junju starts around April. Alternatively, it might open up the possibility that their mothers got infected during the second trimester. It has previously been reported that malaria infections during pregnancy can alter an infant’s susceptibility to clinical malaria e.g.–19. It . However, as the observed effect declined rapidly over time and was also seen across all age groups it is questionable whether it describes the same phenomenon as reported here. Using a systems immunological approach, we have previously described how repeated malaria episodes can lead to measurable modifications of the immune system, based on the comparison between children with few versus a large number of episodes. Using the insights gained from this analysis it would thus be interesting to follow a similar approach and compare children based on the risk factors identified herein. Another important finding is that the risk of a clinical malaria episode is not simply a decreasing function of the number of episodes experienced in the past. In fact, we found that those children who have experienced many more episodes by a certain age have a much higher baseline risk than other, similar aged children. What we cannot conclude from this analysis is whether this might be due to inherent, i.e. genetic differences in susceptibility to clinical malaria, or whether these differences are somehow induced whereby an exposure to the parasite early in life can lead to impaired immunity to malaria, which in turn leads to further episodes of malaria in the future. A recent study has also found that individuals who carry asymptomatic infections are at an increased risk of experiencing a symptomatic episode in the near futureplateaued in experiencing new episodes and are thus believed to have acquired a state of clinical protection can succumb to a malaria episode once more in years of high transmission (e.g. 2018). This, on the other hand, is an important point to re-emphasize: protection itself is not a dichotomous state but can best be understood as the probability of developing clinical symptoms if infected. Monitoring individual children and their level of exposure to malaria over long periods of time is therefore required to robustly infer immunological changes underlying their transition to clinical protection. The other caveat is that our measure of prevalence is retrospective, i.e. it is a summary measure based on all recorded infections over the entire year. One could thus argue that its high correlation with clinical episodes follows a circular argument. However, as the index case was not included in its calculation and because its computation is done over the same period of time as the response , this should not be a reason for concern.There are a number of caveats in this study. The first is that we only monitored children under the age of 12 years. What is clear from our analyses is that by this age, and in this setting, many of the children are still experiencing clinical episodes at a rate similar to younger children, even though their risk of severe and life-threatening malaria had declined long before that. Due to limited sample sizes and high variability in malaria transmission over space and time, identifying children who have acquired a level of natural protection is almost impossible simply by comparing incidence of clinical malaria. In fact, even children who appeared to have, but can also make comparisons between studies challenging. What we have demonstrated here is that individual-level variations in observed outcomes, in this case the incidence of clinical episodes in a given year, are more than noise in the collected data and have to be taken into consideration when trying to make inferences from population-level measures. The highlighted heterogeneity in exposure to the parasite implies that children experience different transmission intensities at different times during their life. So far we do not know whether there is a (long-term) difference in children who experience the majority of their clinical episodes before the age of 6 years or after, for example. What is clear, though, is that there are pronounced differences in the children’s pathway to becoming clinically protected. Identifying those who get there much faster than others might be key for broadening our understanding of natural acquired protection against malaria.Cohort studies and results based on them are often affected by the high variability in the underlying data. This may not only drown out real effects, which can be small even when comparing clearly define phenotypes at the gene expression levelhttps://doi.org/10.7910/DVN/WQCKJJ).Kemri Wellcome Trust Research Programme (KWTRP) Research Data Repository: Replication Data for: Individual-level variations in malaria susceptibility and acquisition of clinical protection, (dgc@kemri-wellcome.org) who will manage the process and ensure that appropriate ethical approval is in place (if applicable), consent obtained caters for uses outlined in the data request, and the request is in line with the institution’s policies on data sharing.Note, spatial location data of individual homesteads has been removed due to confidentiality reasons. Data sharing requests will be reviewed by the Data Governance Committee at KEMRI CGMRC Kilifi (https://doi.org/10.7910/DVN/WQCKJJ).Kemri Wellcome Trust Research Programme (KWTRP) Research Data Repository: Replication Data for: Individual-level variations in malaria susceptibility and acquisition of clinical protection, . The authors considered our comments and made useful additions to the methods in particular. It is disappointing that most of their responses to our more technical comments will remain hidden in the reviewer dialogue. Some of these limitations seem to be related to the very lengthy review processes and restrictions of the journal. I still feel that fitting the parameter 'a' to maximize the correlation of PI is a bit circular. They are basically fitting PI to the outcome and then finding that it’s a risk factor. a determines how rapidly the influence of an infection at j on the prevalence atj declines with increased spatial distance.” I’m pretty sure they mean: “…influence of an infection j on the PI at i…” Pg 4, when discussing the spatial weighting factor a: “The spatial weighting factor  Also, please consider the following to check for a typo: Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Malaria, epidemiology, implementation science/trialsWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This manuscript presents an analysis of how individual differences in exposure affect children’s risk of experiencing a clinical malaria episode. Based on weekly surveillance in a 12-year longitudinal birth cohort in Kenya, the authors describe spatio-temporal variability in malaria incidence and heterogeneity in clinical episode histories for children living in a relatively small geographic area. They found that children with a high number of previous clinical episodes are at a higher risk of an additional episode, challenging the expectation that protective immunity, and therefore decreased risk of clinical episode, is acquired with increasing symptomatic exposure. While the manuscript is well-written and of interest to the malaria community, we have some concerns regarding the metrics used to define local exposure and potential sampling bias, which are detailed below. Major concerns:PI is used as a metric of local risk, but it is not clear how spatially representative the cohort is. There is no information about intervening households not enrolled in the cohort. PI assigns a number to only the local malaria cases only among cohort members which may not reflect something fundamental about the underlying epidemiology. It would be helpful to comment on how spatially representative cohort households are in Junju or how children were selected for enrollment. We went back to some previous papers and still could not quite get this clearly.j, which is described as a location in the text. If the latter, and sincej,tZ is binary for at least one clinical episode at locationj, locations/households with vastly different rates of clinical malaria in a given year would be weighted equally, diminishing some heterogeneity in the data.Some clarification of the variable definitions for PI is needed. The introduction to PI in the methods describes summing over all recorded malaria episodes in a year; however, the sums are defined overGiven the previous two points, and particularly since this number is based on symptomatic cases, we feel that the term “prevalence” index could be misleading. “Cohort incidence index” or similar may be more appropriate.a, a description of how wella fit the data, a sensitivity analysis of the effect ofa on the relationship between PI & episodes all would be useful in understanding this central parameter.It would be useful to see more information about ‘a’. It is given as 2 with an indication that it was estimated from the data, but more information aboutIt is interesting that the zones of highest ‘PI’ seem to shift from north to central to south over the course of the study period. This is consistent with our finding of dynamic hotspots of fever (Platt Sci Rep). Could this be related to local levels of population immunity? Or is it influenced by the open cohort model where you have more cohort members in different zones over time? Regarding the birth exposure analysis, is it possible that Q4 births are at lower risk simply because they have less accumulated exposure/episodes before entering the cohort? Given the incremental differences between Q1--> Q2 -->Q3-->Q4, with the largest difference seen between Q1 and Q4 births, and the effect waning with age, this seems like one possible explanation. Based on your annualized outcome , I assume that a child enters the analysis only for the first calendar year AFTER their first birthday which could introduce the above bias. With the detailed data available, could a more precise metric of pregnancy exposure be estimated, based on rainfall, total cohort cases in the 9 months before their birth, or even PI associated to a birth (or pregnancy). This would be a more compelling comparison. The Prevalence Index (PI) was described in the manuscript as capturing the spatial distribution of malaria over time within the cohort. It was also found to have the strongest effect on risk of clinical malaria compared to other covariates. However, we are concerned:Did you observe any household clustering of children with high numbers of cumulative episodes? Our work has demonstrated that infections are primarily clustered  and also related (Nelson Nat Comm) at the household level (as measured by haplotype sharing) indicating that spatial structure of local ‘prevalence’ beyond the household is probably measuring a risky environment (ie mosquito density) rather than the actual risk of a nearby infection on another susceptible individual. Additional consideration/discussion of household-level factors would strengthen the overall conclusions of the manuscript.Figure 5: The y axis values (0 - 3.5 previous episodes) in Figure 5 do not seem to agree with those in Figures 3 and 4B, where it appears some children experienced up to 30 previous episodes. How do you reconcile these numbers?et al. BMC Med 2015).Given the large differences between years in overall episodes, shouldn’t year be a covariate in the model given that children will have episode data spanning years with much different overall morbidity? In a previous analysis that included this cohort, calendar year was identified as a significant independent predictor of clinical malaria  for all children against age.Clarifying the units to effect sizes/regression coefficients for Fig 7-8Additional interpretation in the text for Figure 8C and 8DReading your responses to Dr. Okell’s thoughtful comments significantly aided in our understanding of different aspects of the manuscript; however, some of the indicated modifications still seem to be missing from version 2 of the manuscript, including:Other studies have also found that previous exposure increases risk of future exposure . Other concerns:Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Malaria, epidemiology, implementation science/trialsWe confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. We would like to thank the reviewers for their careful evaluation of our manuscript, which we hope has helped to improve clarity regarding the methodology, incl. sampling, and presentation of our results. Please see below for a point-by-point response to all concerns raised by the reviewers. Note, we have included here some additional figures to address specific points; these are not part of the manuscript as we felt they would distract from the main findings.Major concerns:PI is used as a metric of local risk, but it is not clear how spatially representative the cohort is. There is no information about intervening households not enrolled in the cohort. PI assigns a number to only the local malaria cases only among cohort members which may not reflect something fundamental about the underlying epidemiology. It would be helpful to comment on how spatially representative cohort households are in Junju or how children were selected for enrollment. We went back to some previous papers and still could not quite get this clearly.Response: This is an important point. Recruitment into the cohort was based on which homesteads volunteered following sensitisation of the whole area and was not subject to specific spatial or demographic selection criteria. We therefore believe that the cohort analysed here does provide an unbiased spatial representation of the village as a whole and have added this information in our revised manuscript (Methods section). Furthermore, although the prevalence index (PI) is based on recorded malaria episodes in only a small subset of the population, it strongly correlates with an individual’s risk of malaria in a given year. And whilst we agree that this simple index does not capture all of the underlying factors contributing to risk, we cannot think of any reasons why it would not be reflective of the general spatio-temporal epidemiology in this area.   The Prevalence Index (PI) was described in the manuscript as capturing the spatial distribution of malaria over time within the cohort. It was also found to have the strongest effect on risk of clinical malaria compared to other covariates. However, we are concerned:j, which is described as a location in the text. If the latter, and sincej,tZ is binary for at least one clinical episode at locationj, locations/households with vastly different rates of clinical malaria in a given year would be weighted equally, diminishing some heterogeneity in the data.Some clarification of the variable definitions for PI is needed. The introduction to PI in the methods describes summing over all recorded malaria episodes in a year; however, the sums are defined overResponse: We agree that the description of how the prevalence index is derived lacked clarity, which we hope is now easier to follow in the revised version. In brief, for each individual we sum over all individuals (not homesteads) within a certain spatial neighbourhood (radius around the index location). For this we used a binary classification: 1 if the neighbouring individual had a recorded episode that year, and 0 otherwise. This way, locations or households with vastly different rates of clinical malaria, that the reviewers were alluding to, will be weighted differently; that is, a location / household with many individuals who had a recorded infection contribute more than an equally distant location with only a single individual. Spatial heterogeneity can therefore be captured whilst also achieving some spatial smoothing.   Given the previous two points, and particularly since this number is based on symptomatic cases, we feel that the term “prevalence” index could be misleading. “Cohort incidence index” or similar may be more appropriate.Response: This is a fair point; however, at this stage of the reviewing process, with the manuscript having been online for over a year, we feel it might be better to stick with prevalence index but to clarify this point in the revised manuscript to avoid potential misinterpretation.   a, a description of how wella fit the data, a sensitivity analysis of the effect ofa on the relationship between PI & episodes all would be useful in understanding this central parameter.It would be useful to see more information about ‘a’. It is given as 2 with an indication that it was estimated from the data, but more information aboutResponse: This is another important point, which also lacked detail in the original submission. The parameter a acts like spatial smoothing, with larger values corresponding to smaller influences of spatially distant locations contributing to the prevalence index (PI), leading to more spatial heterogeneity, as shown in the graph below plotting the standard deviation of PI against a.   Graph 1 We initially tried to use spatial statistics to infer the value from the data but found that it varied too much between individual years. In the end we therefore decided to use a value that maximised the correlation between PI and number of episodes for all individuals and all years, as shown in the graph below.Graph 2This value was also found to be a good ‘visual’ compromise between spatial smoothing and maintaining sufficient heterogeneity to demonstrate spatio-temporal trends . We would usually add these graphs as supplementary material but here, given the WT OpenResearch publishing format, felt that they would distract from the main findings. We hope that interested readers will still be able to find this information in the comments section.It is interesting that the zones of highest ‘PI’ seem to shift from north to central to south over the course of the study period. This is consistent with our finding of dynamic hotspots of fever (Platt Sci Rep). Could this be related to local levels of population immunity? Or is it influenced by the open cohort model where you have more cohort members in different zones over time?Response: We agree that this was an interesting finding and partly motivated some of the subsequent analyses. We speculate that this might indeed be driven by a dynamically changing immunity landscape, as previously indicated in Bejon et al. PloS Med (2010) and also in line with the paper referred to by the reviewers, rather than spatially biased enrolment into the cohort (for reasons mentioned above). We have added a brief discussion on this point in our revised manuscript.   Regarding the birth exposure analysis, is it possible that Q4 births are at lower risk simply because they have less accumulated exposure/episodes before entering the cohort? Given the incremental differences between Q1--> Q2 -->Q3-->Q4, with the largest difference seen between Q1 and Q4 births, and the effect waning with age, this seems like one possible explanation. Based on your annualized outcome , I assume that a child enters the analysis only for the first calendar year AFTER their first birthday which could introduce the above bias. With the detailed data available, could a more precise metric of pregnancy exposure be estimated, based on rainfall, total cohort cases in the 9 months before their birth, or even PI associated to a birth (or pregnancy). This would be a more compelling comparison.Response: This is an interesting hypothesis but there are reasons to believe why this cannot fully explain our observations. Children are enrolled at birth but for our analyses we only selected children 1 year old or above. It is true that individuals born early January have nearly an extra year of exposure compared to those born late in December and therefore could theoretically have more cumulative episodes. However, we did not find an incremental differences between Q1->Q2->Q3->Q4 that the reviewers are referring to . This becomes more apparent when we stratified by birth month, as shown below (due to low sample sizes we a birth quarter stratification in the manuscript).Graph 3Furthermore, although it is possible that the discrete nature of age and year can introduce the referred bias in the number of cumulative episodes, this would not affect their risk of getting a clinical episode in subsequent years, which our analysis showed was reduced for those born in Q4. We fully agree that these observations warrant more detailed analyses into the likely causes (e.g. exposure during pregnancy or exposure during first year of life), and in fact is something that we are currently working on, but we feel that this was outside the scope of this manuscript. Other concerns: Did you observe any household clustering of children with high numbers of cumulative episodes? Our work has demonstrated that infections are primarily clustered  and also related (Nelson Nat Comm) at the household level (as measured by haplotype sharing) indicating that spatial structure of local ‘prevalence’ beyond the household is probably measuring a risky environment (ie mosquito density) rather than the actual risk of a nearby infection on another susceptible individual. Additional consideration/discussion of household-level factors would strengthen the overall conclusions of the manuscript.Response: We did not look specifically at household clustering but agree that this might be a potentially important factor contributing to an individual’s risk of infection / clinical episodes. Of a total of over 500 individuals analysed, only around 50 shared homesteads, and because we only considered children aged 1-12 years, rather than all members in a household, we do not believe that this would make a significant difference to the results presented here. Figure 5: The y axis values (0 - 3.5 previous episodes) in Figure 5 do not seem to agree with those in Figures 3 and 4B, where it appears some children experienced up to 30 previous episodes. How do you reconcile these numbers?Response: Thank you for pointing this out, this was due to an unfortunate mislabelling of the y-axes; what was plotted in Fig. 5 was the average number of episodes per year, i.e. cumulative episodes / age. We have now revised this figure to show total number of previous infections (going up to >30 in the 12 year old age class).  et al. BMC Med 2015).Given the large differences between years in overall episodes, shouldn’t year be a covariate in the model given that children will have episode data spanning years with much different overall morbidity? In a previous analysis that included this cohort, calendar year was identified as a significant independent predictor of clinical malaria .   Graph 5Revised description of spatial homo/heterogeneity in results section text for Fig 2.Additional panel in Fig 4 showing the distribution of incidence  for all children against age.Clarifying the units to effect sizes/regression coefficients for Fig 7-8Additional interpretation in the text for Figure 8C and 8DReading your responses to Dr. Okell’s thoughtful comments significantly aided in our understanding of different aspects of the manuscript; however, some of the indicated modifications still seem to be missing from version 2 of the manuscript, including:Response: We apologise for this but are equally puzzled as to why none of our resubmitted and revised material (manuscript and figures) following Dr. Okell’s comments had been available online. We will try to navigate this system more carefully to make sure that all updated text and figures are available for review.  Other studies have also found that previous exposure increases risk of future exposure .Response: Thank you for pointing us towards this interesting study, which incidentally was published after our manuscript went online. We would like to point out that the effect described therein declined rapidly over time and was observed across all age groups. It is therefore questionable whether this describes the same phenomenon underlying our observations. This is now briefly discussed in the Discussion.      The authors have addressed all my comments very thoroughly - thank you for the insightful responses. I have no further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Epidemiology, mathematical modellingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This analysis harnesses a rich dataset following children over many years within one area and assessing the incidence of clinical malaria episodes. The findings are very interesting and challenge the idea that immunity to disease could be acquired after a specific number of previous disease episodes. Disease appears to be more concentrated within particular individuals than might be expected within a given area. The findings have important implications for studies of malaria susceptibility, risk factors, immune protection, and mathematical models. The analysis is detailed, thoughtful and well written.Could you clarify in the methods whether children were continuously recruited during the study period, or were all recruited at birth at the beginning of the study and followed up? This becomes clearer later on, but it would help interpretation e.g. of Figure 1, where it’s not quite clear if year also indicates increasing average age of the cohort.Can you briefly state whether any interventions were provided as part of the cohort study Are all episodes uncomplicated malaria, or do you count severe malaria also?Statistical modelling – can you explain a bit more the ‘got episode’ outcome versus the ‘previous episodes’ explanatory variable. Does ‘got episode’ depend on the most recent year of data for a given child, while the previous episodes are those occurring in the years before that?In this part of the results: “in those years we not only see more individuals with a clinical episode but a disproportional increase in the numbers of episodes per individual” – how is the disproportional part assessed – maybe based on a Poisson/negative binomial distribution or similar?Figure 2 is a nice depiction. Are the years with high prevalence definitely more spatially homogeneous? Visually the lower prevalence years, e.g. 2007 and 2012 also look quite homogeneous.Fig 4 is very interesting. Why are sickle cells carriers removed from panel B but not A?It could be helpful to have a standard plot of incidence of episodes by age near the beginning, to help the reader compare this cohort to those from other endemic settings.P6 Can you clarify the units of the effect size estimates - are they the regression coefficients on the log odds scale? i.e. 0 = no effect? Same in the fig 7A legend.Fig 7 – I like these plots, but is the blue just 1-yellow? It may be clearer with just one or the other. It would be good to also define ‘GotEpisode’ again in the legend to save the reader referring back – i.e. probability of a current clinical episode – per year, I think?Fig 8. Given the interaction, more explanation is needed for panels A, C and D. What is the baseline group for age and previous episodes in the regression, and what units are used? For what age does C show the effect of previous episodes? . It could be helpful to plot panel C twice, once the effect of previous episodes for a younger age and once for an older age, so the reader has more intuition how the interaction works (there should be less effect of previous episodes at older ages given the direction of interaction?). Does the effect of previous episodes ever become protective at older ages in the multivariable model? .Same query for panel D – for what number of previous episodes does this age effect apply?Can you comment on the strength of correlation between previous episodes and the prevalence index? Is it possible to distinguish the two effects from each other during the regression? E.g. can a child with say 20 previous episodes come from a lower PI area?A very interesting discussion. Could you explain this sentence in the last paragraph some more? “children experience different transmission intensities at different times during their life” – I was not sure how this was inferred. I have some comments and clarifications:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Epidemiology, mathematical modellingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We would like thank the reviewer for their positive evaluation of our work and critical comments, which we believe has helped to improve the quality and readability of our manuscript. Please see below for a point-by-point response to issues raised.Could you clarify in the methods whether children were continuously recruited during the study period, or were all recruited at birth at the beginning of the study and followed up? This becomes clearer later on, but it would help interpretation e.g. of Figure 1, where it’s not quite clear if year also indicates increasing average age of the cohort.Response: Since the initial setup of this cohort in 2005, children have been continuously recruited at or shortly after birth and actively monitored on a weekly basis for detection of malaria episodes. Furthermore, we aimed to maintain a similar average age over time by only selecting children that were between 1 and 12 years old in any given year. We have clarified this in the Method section.Can you briefly state whether any interventions were provided as part of the cohort study Response: This was unfortunately omitted in the description of the data / cohort. All malaria positive children are treated with co-artemether. Bednet coverage is high in this cohort (doi: 10.1186/s12879-017-2822-x) and hence we did not consider it necessary to distribute nets specifically to study participants.  We found no correlation between bednet use and outcome; hence bednet use was not considered for further analyses. This has now been added to the Methods section.Are all episodes uncomplicated malaria, or do you count severe malaria also? Response: This community based active surveillance of malaria with prompt provision of anti-malarials did not identify episodes of severe malaria. A malaria episode (which relates to our response variableGotEpisode) is defined as axillary temperature ≥37.5°C associated with >2,500P. falciparum parasites per microliter of blood. This has been clarified.Statistical modelling – can you explain a bit more the ‘got episode’ outcome versus the ‘previous episodes’ explanatory variable. Does ‘got episode’ depend on the most recent year of data for a given child, while the previous episodes are those occurring in the years before that?Response: As outlined in the Methods section,GotEpisode is a binary response variable (yes/no) indicating whether an individual experienced a clinical episode (defined as above) in a given year or not. The variablePrevEpisodes, on the other hand, is the recorded total number of episodes a child has experienced up until but not including the year in question. We have tried to make this clearer in the Method section. In this part of the results: “in those years we not only see more individuals with a clinical episode but a disproportional increase in the numbers of episodes per individual” – how is the disproportional part assessed – maybe based on a Poisson/negative binomial distribution or similar? Response: This was originally assessed by eye only as it was clear from Figure 1B that in years with low-to-medium levels of infections, less than a third of children experience more than 2 episodes per year, whereas this increases to around two thirds in years of high prevalence. We have now added another panel showing the frequency distribution of 1, 2, and 3+ episodes to the graph to make this point clearer. Figure 2 is a nice depiction. Are the years with high prevalence definitely more spatially homogeneous? Visually the lower prevalence years, e.g. 2007 and 2012 also look quite homogeneous.Response: This is a good point and unfortunate wording on our part. We have replaced this sentence with “Furthermore, we find that in high prevalence years (2014 and 2018), malaria incidence was not confined to particular areas but was observed almost across the entire cohort, which could be indicative of more intense and/or longer transmission seasons.”Fig 4 is very interesting. Why are sickle cells carriers removed from panel B but not A?Response: The reason for removing sickle cell carriers in Figure 4B is because it is a known protective trait. AS children will therefore have a lower total number of previous episodes and are also less likely to experience an episode in a given year, such that leaving them in would confound the results. On the other hand, they would not influence the relationship between age and the risk of an episode. We have added a note on this in the main text.    It could be helpful to have a standard plot of incidence of episodes by age near the beginning, to help the reader compare this cohort to those from other endemic settings. Response: Thank you for the suggestion. We have now added an additional panel to Fig 4 showing the distribution of incidence  for all children against age. This graph demonstrates that age on its own, at least in this cohort within the age range considered, is not a good predictor of clinical protection.  P6 Can you clarify the units of the effect size estimates - are they the regression coefficients on the log odds scale? i.e. 0 = no effect? Same in the fig 7A legend. Response: All continuous variables were centred and scaled for the statistical analyses to permit better comparison between effect sizes. For those variables a unit represents one standard deviations from the mean. And yes, the regression coefficient are log odds, with 0 indicating no effect, negative values a protective effect and positive values an increase in risk. We have added a corresponding note to the Methods section and the main text and changed the x-axis labels in Figs 7A and 8A.  Fig 7 – I like these plots, but is the blue just 1-yellow? It may be clearer with just one or the other. It would be good to also define ‘GotEpisode’ again in the legend to save the reader referring back – i.e. probability of a current clinical episode – per year, I think? Response: Thank you for the suggestion, we have amended the figure accordingly (as blue was indeed 1-yellow). We have also added a reminder to the main text and figure legends thatGotEpisode refers to a child experiencing a clinical malaria episode in a given year or not. Fig 8. Given the interaction, more explanation is needed for panels A, C and D. What is the baseline group for age and previous episodes in the regression, and what units are used? For what age does C show the effect of previous episodes? . It could be helpful to plot panel C twice, once the effect of previous episodes for a younger age and once for an older age, so the reader has more intuition how the interaction works (there should be less effect of previous episodes at older ages given the direction of interaction?). Does the effect of previous episodes ever become protective at older ages in the multivariable model? .Response: We fully agree that Fig 8 was hard to interpret without further explanation. As mentioned above, all numerical variables are centred and scaled, so the effects shown in panel A are with respect to a standard deviation from the mean. Panel B is probably the easiest to interpret and simply shows that an individual’s risk of an episodes increases with increasing malaria prevalence in their surrounding area. What panel C shows is that of similarly aged individuals, those with a higher number of previous episodes are at a higher risk of subsequent episodes. Panel D shows this the other way around, i.e. that of children with similar numbers of previous infections, those that are older are more protected. And this interaction between age and previous number of episodes is what explains the overall small effect size of age when not accounting for it . We have now added a more detailed interpretation of these results to the main text. With regards to age itself, and plotting panel C twice, as shown in Fig 5, the effect of the previous number of episodes does not appear to decline within the age groups considered in this analysis. Furthermore, as the data is centred, the results implicitly compare young and old children. We do agree, however, that at some point in their lives this effect should diminish; unfortunately, though, we do not have the data to explore this further.  Same query for panel D – for what number of previous episodes does this age effect apply?Response: Please see response to previous comment.Can you comment on the strength of correlation between previous episodes and the prevalence index? Is it possible to distinguish the two effects from each other during the regression? E.g. can a child with say 20 previous episodes come from a lower PI area?Response: This is unfortunately not possible for this regression analysis simply because the prevalence index varies so much both over time and space, meaning it is not possible to categorise children based on PI in a meaningful way for more than a year or two. Furthermore, we have mapped the cohort by number of previous episodes (results not shown) and could not find any clear spatial clustering of individuals with high a low number of episodes. And although we would expect that children with many episodes must come from areas where there was a lot of transmission, it is possible to find other children living in the same area with much fewer recorded episodes, which to us suggests substantial individual-level variation in the risk of experiencing a malaria episode. A very interesting discussion. Could you explain this sentence in the last paragraph some more? “children experience different transmission intensities at different times during their life” – I was not sure how this was inferred.Response: This statement is based on the fact that malaria prevalence in this cohort varies significantly across space and time, as illustrated in Fig. 2. So for example, a child living in the South-West might have experienced high malaria transmission during early childhood and much less so in later years, whereas a child growing up in the North-East would have been exposed to less malaria during the first few years of their life but more so at a later stage during their childhood. We currently do not know if or how this affects an individual’s ability to acquire protective immunity, but maybe this is a first step in acknowledging that these differences exist and can arise even within a small geographic area.


Malaria is one of the most prevalent parasitic diseases and the foremost cause of morbidity in the tropical regions of the world. Strategies for the efficient management of this parasitic infection include adequate treatment with anti-malarial therapeutics and vaccination. However, the emergence and spread of resistant strains of malaria parasites to the majority of presently used anti-malarial medications, on the other hand, complicates malaria treatment. Other shortcomings of anti-malarial drugs include poor aqueous solubility, low permeability, poor bioavailability, and non-specific targeting of intracellular parasites, resulting in high dose requirements and toxic side effects. To address these limitations, liposome-based nanotechnology has been extensively explored as a new solution in malaria management. Liposome technology improves anti-malarial drug encapsulation, bioavailability, target delivery, and controlled release, resulting in increased effectiveness, reduced resistance progression, and fewer adverse effects. Furthermore, liposomes are exploited as immunological adjuvants and antigen carriers to boost the preventive effectiveness of malaria vaccine candidates. The present review discusses the findings from studies conducted over the last 40 years (1980–2020) using in vitro and in vivo settings to assess the prophylactic and curative anti-malarial potential of liposomes containing anti-malarial agents or antigens. This paper and the discussion herein provide a useful resource for further complementary investigations and may pave the way for the research and development of several available and affordable anti-malarial-based liposomes and liposomal malaria vaccines by allowing a thorough evaluation of liposomes developed to date for the management of malaria. Plasmodium falciparum, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax and/or Plasmodium knowlesi, which mostly affect people in over 87 countries located in tropical and subtropical regions of the world. Malaria has been recognized as one of the leading causes of illnesses and deaths in the world . On. On98]. The rigidity, permeability, and charge of these bilayers depend on their lipid composition. The major components of liposomes are natural or synthetic phospholipids (55–100%) and cholesterol (30–45%) .Among the lipids frequently used in the formulation of liposomes, one can cite soybean phosphatidylcholine (SPC), egg phosphatidylcholine (EPC), dimyristoylphosphatidylcholine (DMPC), dipalmitoylphosphatidylcholine (DPPC), distearoylphosphatidylcholine (DSPC), dibehenoylphosphatidylcholine (DBPC), dipalmitoylphosphatidylglycerol (DPPG), dimyristoylphosphatidylglycerol (DMPG), distearoylglycerophosphoethanolamine (DSPE), and cholesterol , 102. Ch., passive and active targeting. Passive drug targeting is achieved by the use of conventional liposomes and long-circulating liposomes (PEGylated liposomes). In contrary, active drug targeting is realized by the surface activation of the liposomes through inclusion of surface ligands  binding to the infected red blood cells (RBCs) associated receptors . The safety, immunogenicity and prophylactic activity of these injectable liposome-based vaccines was confirmed in human subjects -hypoxanthine incorporation method. However, it would also be interesting to screen the antiplasmodial sensitivity of these liposomes against the erythrocytic stages of others Plasmodium species as well as clinical isolates. Moreover, the ability of drug-encapsulated liposomes to cure the liver stage infections could be evaluated by the type II fatty acid synthase (FAS)-target-based anti-malarial screening approach or the real time measurements of bioluminescence of in vitro cultured liver stages [The in vitro antiplasmodial activity of anti-malarial-loaded liposomes was mainly evaluated on blood stages of r stages , 97, 289Plasmodium  have shown some analogies with human parasites such as basic biology , life cycle and the molecular basis of drug-sensitivity and resistance. Nevertheless, the efficacy of liposomal formulations against P. vivax malaria should also be investigated by using Plasmodium cynomolgi and Aotus monkeys, an appropriate host-parasite combination [Plasmodium mouse model of experimental cerebral malaria does not replicate exactly the pathophysiology of cerebral disease in humans [To assess the therapeutic in vivo response of anti-malarial compounds loaded in liposomes, rodent models were typically used. These models have been validated and extensively used in different aspects of research on the human infection and are recognized as useful model parasite for evaluation of in vivo anti-malarial efficacy of drugs, in particular for both liver and blood stages of uncomplicated falciparum malaria . Indeed,bination , 292. Ofn humans –293. Even humans . IdentifPlasmodium proteins found in the plasma membrane of pRBCs and the lack of sufficiently specific markers for Plasmodium-infected cells [Plasmodium biomarkers makes a great avenue for future research. On the other hand, numerous liposomal vaccines for malaria prophylaxis have shown promising results in pre-clinical studies, however, only a handful of candidate vaccines have made it to clinical trials. Among them, RTS,S/AS01 (Mosquirix®) has been approved in 2015 by the European Medicines Agency for immunization of children aged 6 weeks to 17 months against Plasmodium malaria. However, this vaccine provides relatively little protection and its efficacy decreases rapidly [From 1980 to 2020, the term liposomes had scored in more than 66,000 publications (Pubmed database). However, over the same period, less than 200 articles (i.e. around 5 articles per year) devoted to liposomes applied to malaria therapeutics have been identified. These articles were produced by less than 20 research groups, which appear to be far lower than those dedicated to “liposome for cancer” ; thereby confirming that liposome technology for malaria is still an under-researched topic. Additionally, one can argue that liposome-based administration has not progressed yet towards a working strategy for malaria therapeutics. Indeed, among the 19 clinically approved liposomal formulations, none is indicated for malaria therapy, probably because of the highly variability of known  rapidly , 296. He rapidly .Future efforts are to be dedicated to the evaluation of the safety profile of liposomes and the toxicity of their material components (e.g. ligands) prior to clinical translation . The disFinally, despite their very limited number and the numerous pharmacological, pharmacotechnical and economic challenges, the studies related to anti-malarial liposomes evidenced some potential for the treatment of malaria considering the significant therapeutic advantages of liposomal formulations over conventional drug products.Plasmodium species from a single source (mostly laboratory strains), instead of considering a systematic assessment using various species from multiple origins . In addition, complementary studies  as well as key prerequisites  are needed to fully realize the potential of liposome technology for malaria as a tropical disease, which will further boost the translational development of anti-malarial liposomes towards clinical settings.The potentials of liposome technology as a technique for building personalized tools to treat and prevent malaria by overcoming the limitations associated with existing and developing anti-malarial medicines and antigens are demonstrated in this overview of works published between 1980 and 2020. The shortcomings of these therapeutic and immunological agents include poor solubility, low permeability, chemical instability, low bioavailability, uncontrolled pharmacokinetics, undesirable side effects, toxicity, development of resistance, non-specific targeting and poor immunogenicity. The impacts of passive targeting liposome systems and antibody-mediated erythrocytes targeting liposomes as well as liposome-based vaccines have been demonstrated both in vitro and in vivo using various pre-clinical settings. However, these promising performances are still in their infancy due to the lack of extensive investigations, as most studies are focused on testing liposomes against one


Congenital malaria, which is caused by vertical transmission of malaria parasites, is a potentially fatal condition. Despite Africa’s high malaria burden, congenital malaria is not routinely screened for, and thus may go undiagnosed. Malaria, if not treated promptly, can quickly progress to severe forms and result in death. Severe congenital malaria is believed to be uncommon in neonates due to maternal antibodies, fetal haemoglobin, and the placenta’s sieving effect. The majority of reported cases were classified as having severe anaemia. Following a thorough review of the literature, only one case of congenital cerebral malaria (CCM) has been reported, and it was misdiagnosed.A 5-day-old Nigerian neonate born to an apparently healthy mother initially displayed characteristics consistent with neonatal sepsis and severe neonatal hyperbilirubinaemia. He quickly developed characteristics consistent with meningitis. Surprisingly, the peripheral blood film revealed evidence of malaria parasites, which was immediately confirmed by Giemsa-stained thick and thin blood film microscopy for malaria. The patient was diagnosed with congenital cerebral malaria. The medication was modified to parenteral artesunate followed by oral artemisinin combination therapy. The neonate recovered fully and had no neurological deficits on follow up.Because CCM and infant meningitis have similar clinical presentations, CCM could be misdiagnosed and lead to death if there isn’t a high index of suspicion. Malaria is primarily transmitted to humans through the bites of infected female Anopheles mosquitoes, but it can also be transmitted via blood transfusion, organ donation or from a pregnant woman to her fetus –3. MalarPlasmodium falciparum infection during pregnancy does not always result in symptomatic illness [In endemic areas such as Nigeria, where mothers have developed significant immunity to malaria,  illness . As a re illness , 10. Add illness . Further illness . These f illness . This st illness . Further illness .Congenital malaria is diagnosed by the detection of asexual malaria parasites on a blood smear of the newborn’s peripheral blood within the first 7 days of life , 15. PasP. falciparum parasites on peripheral blood smears, and no other explanation for the coma [Cerebral malaria is defined by the World Health Organization (WHO) as a clinical state characterized by a coma lasting at least 1 h after the end of a seizure or correction of hypoglycaemia, asexual the coma . Physicathe coma . Subtle the coma . These ithe coma . Failurethe coma . If leftthe coma . Malariathe coma . It is pthe coma . IPTp rethe coma . Nigeriathe coma . Accordithe coma .In endemic locales, the reported incidence of congenital malaria ranged from 0 to 37% –27, and A 5-day-old male neonate presented with 3 days history of yellowness of the body and 2 days history of poor suckling and excessive sleep. The yellowness of the body began on the face on the 2nd day of life and spread to other parts of the body. He sucked for less than 2 min on the mother’s breast on the 3rd day of life, compared to approximately 20 min prior to the illness. He was constantly asleep, and even when a perceived painful stimulus was applied, he cried poorly and low pitched. No fever or obvious convulsions were observed by the parents. Since birth, his mother had been administering 2.5 mls of oral paracetamol three times daily on the advice of a nurse. While pregnant, the mother received two doses of tetanus toxoid, but did not receive intermittent preventive treatment for malaria. She had no febrile illness throughout the pregnancy, but experienced foul-smelling creamy and itchy vaginal discharge in the final trimester, which was resolved with self-prescribed nystatin pessary. There was no prior history of premature membrane rupture. The pregnancy was carried to term, and the birth occurred vaginally. He was born crying and weighing 3.2 kg. The mother had no history of postpartum fever, and her lochia was not offensive. Due to a delay in lactation, the baby was given pre-lacteal feeds (infant formula) only on the 1st day of life, but by the second day, the baby was exclusively on breast milk and sucking effectively. On his 2nd day of life, he received BCG, oral polio 1, and hepatitis B virus vaccinations. He is the second child of civil servant parents, and his female elder sibling had no history of neonatal jaundice. The parents had O Rhesus positive blood groups, and there was no family history of glucose-6-phosphate dehydrogenase deficiency or the use of naphthalene balls on the baby’s clothing.2) was 92 percent, and he weighed 2.98 kg. He had a Blantyre coma score (BCS) of 4/5 and a flat and soft anterior fontanelle (2.5 cm in diameter). He had normotonia but suboptimal primitive reflexes . He also had hepatosplenomegaly and a wet but clean umbilical stump. The examination of the respiratory and cardiovascular systems revealed no abnormalities. A presumptive diagnosis of early onset neonatal sepsis and severe neonatal hyperbilirubinaemia was made. The laboratory result that was immediately available (random blood glucose) was 70 mg/dl. Serum bilirubin, urinalysis, urine microscopy, culture and sensitivity (m/c/s), umbilical swab m/c/s, complete blood count, peripheral blood film, direct and indirect Coombs tests, glucose-6-phosphate dehydrogenase, cerebrospinal fluid m/c/s, protein and glucose, serum electrolytes, urea and creatinine were also requested. He was started on intravenous ceftazidime (100 mg/kg) and geniticin (5 mg/kg), both in two divided doses 12 h apart, and a maintenance dose of 10% dextrose water. The patient received a double exchange blood transfusion (EBT) that was well tolerated. Following that, intensive phototherapy was continued. Breastmilk was fed 4 h after EBT via a nasogastric tube and was tolerated.He was extremely irritable, in respiratory distress, febrile with an axillary temperature of 38 °C, moderately pale, icteric to the palms and soles, and acyanosed upon presentation. The respiratory rate (RR) was 68 breaths per minute, the heart rate (HR) was 162 beats per minute, the blood pressure (BP) was 102/51 mmHg, the oxygen saturation  during periods when his SPO2 decreased. His RR was 64 to 70 breaths per minute (in between apneic attacks) and his HR was 120 beats per minute during the attack and increasing to 182 beats per minute afterwards. There was also an increase in his temperature (38.2 °C) and blood pressure (115/75 mmHg). His BCS was 2/5, his anterior fontanelle was tense, and he was hypertonic with an opthostonic posture. RBG was 118 mg/dl on repeat. The baby was placed on intranasal oxygen, and his drug prescription was modified to include three doses of intravenous (IV) mannitol (1 g/kg/dose over 30 min) 8 h apart, frusemide (2 mg/kg/dose to follow each dose of mannitol), diazepam (0.2 mg/kg/dose over 10 min), phenobarbitone , and dexamethasone 0.2 mg/kg/dose every 12 h. Even though apneic attacks did not recur following anticonvulsants, the patient maintained an opithostonic posture.Thirteen hours into the admission, while still being monitored continuously for vital signs, his SPOP. falciparum with a parasite density of 8341 parasites/µl. Other investigations conducted prior to the EBT revealed a total serum bilirubin of 30 mg/dl and conjugated bilirubin of 1.9 mg/dl. Complete blood count showed leucopenia of 2.0 × 109/l, with differential neutrophils, lymphocytes and monocytes percentage of 30%, 69% and 1% respectively. Haematocrit level was 35.1% and he had thrombocytopenia . Urinalysis result revealed a deep amber and clear urine with presence of protein, urobilinogen and bilirubin. CSF analysis, urine m/c/s, umbilical swab m/c/s, serum electrolytes, urea and creatinine, both direct and indirect Coombs test returned no significant abnormality. Total serum bilirubin was 8.5 mg/dl following EBT, while conjugated bilirubin was 1.5 mg/dl. The patient was diagnosed with severe congenital malaria . Intravenous dexamethasone and antibiotics were stopped. Intravenous artesunate was prescribed at a dose of 3 mg/kg to be given at 0 h, 12 h and subsequently 24 hourly till the patient regains consciousness.The peripheral blood film result (requested prior to the EBT) revealed normocytic, normochromic red blood cells, but also significant malaria parasites. A Giemsa-stained thick and thin blood film was immediately requested for malaria microscopy and revealed trophozoites of ®), the patient’s consciousness gradually improved; by the second dose, he was no longer in an opisthotonic state, and by the fourth dose, he had regained complete consciousness and was able to suckle on his mother’s breast. After the fifth IV dose of artesunate, a 3-day course of oral artesunate–amodiaquine was initiated . Intravenous phenobarbitone was gradually tapered off. The mother complained of a headache, fever, and chills on the 3rd day following presentation. A rapid diagnostic test for malaria histidine-rich protein 2 (HRP2) was positive, and she received a full course of ACT with clinical recovery. On the 5th day, the patient was discharged, and a repeat malaria microscopy performed that day was negative. The mother was counseled on the importance of intermittent preventive treatment during pregnancy, in addition to other malaria prevention measures. The patient was followed up for 1 year and no neurologic deficit was discovered during follow-up examinations of the central nervous system.Following commencement of intravenous artesunate (RekmalCongenital malaria is defined as the presence of malarial parasites in a baby’s cord blood or peripheral smear within 7 days of birth , 29. ThiP. falciparum malaria referred to as cerebral malaria [Seizures and unarousable coma are known features of severe  malaria . For cer malaria . Due to  malaria . As requ malaria , but nonMalaria was confirmed using the gold standard of Giemsa-stained thick and thin blood film microscopy . AlthougCerebral malaria can present as a manifestation of congenital malaria and should be considered in patients who present with seizures and an unarousable coma. Sick neonates born to asymptomatic mothers in malaria-endemic areas should also be promptly investigated for malaria. Although additional data are needed in neonates, intravenous artesunate followed by oral artesunate–amodiaquine treatment appeared to be safe and effective.


Plasmodium falciparum parasites lacking histidine-rich protein 2 and 3 (pfhrp2/3) genes have been reported in several parts of the world. These deletions are known to compromise the effectiveness of HRP2-based malaria rapid diagnostic tests (HRP2-RDT). The National Malaria Control Programme (NMCP) in Tanzania adopted HRP2-RDTs as a routine tool for malaria diagnosis in 2009 replacing microscopy in many Health facilities. We investigated pfhrp2/3 deletions in 122 samples from two areas with diverse malaria transmission intensities in Northeastern Tanzania. Pfhrp2 deletion was confirmed in 1.6% of samples while pfhrp3 deletion was confirmed in 50% of samples. We did not find parasites with both pfhrp2 and pfhrp3 deletions among our samples. Results from this study highlight the need for systematic surveillance of pfhrp2/3 deletions in Tanzania to understand their prevalence and determine their impact on the performance of mRDT. A recent resurgence of the disease is evident in areas where a significant decline in malaria cases was previously observed and this inevitably calls for scrutiny of the malaria control interventions currently in use1, which comprise a number of strategies2. Artemisinin-based combination therapy (ACT) is the approved regimen for malaria treatment3. The World Health Organization (WHO) recommends for parasitological confirmation to be carried out before this treatment is provided4. Microscopy, although still regarded as the gold standard in malaria diagnosis, faces several operational challenges6. In the resource-limited settings of SSA, shortage of personnel with expertise, and long turn-around time of results from the laboratory have been identified as obstacles to the effective microscopic diagnosis of malaria, particularly in rural settings8.Malaria continues to be a health problem in Sub-Saharan Africa (SSA), where the 10 countries with the highest infection rates and deaths in the world are found9. The National Malaria Control Program (NMCP) in Tanzania rolled out malaria Rapid Diagnostic Tests (mRDT) in 2009, achieving diagnostic coverage of around 90% in public and private health facilities in 2014, with microscopy being used in the remaining 10% of facilities in the country10. Over 80% of mRDTs manufactured worldwide are sold or distributed in SSA. Tanzania Medicines and Medical Devices Authority (TMDA) approved five brands of mRDTs to be used in Tanzania11. Accredited Drug Dispensing Outlets (ADDOs) sells an average of 40 RDT units every week, most of them are HRP2-based and there are more than 6000 outlets in Tanzania mainland13. Rapid diagnostic tests have different detection thresholds, but sensitivity and specificity decrease when parasitaemia is below 200 parasites/µl15. Given the high detection threshold, mRDT might miss malaria parasites that are at low densities.Rapid diagnostic tests have proven to be reliable and sensitive enough to replace microscopy as a routine technique for malaria diagnosis in symptomatic patientsPlasmodium falciparum detection16, mostly utilizing the histidine-rich protein (HRP2), as the antigenic marker. HRP2 is a 60–105 kD water-soluble protein secreted by P. falciparum trophozoites , encoded by the sub-telomeric pfhrp2 locus on chromosome 818. HRP2 is abundantly secreted and easily detected in the peripheral blood circulation of the host even when the parasite has sequestered in the microvasculature of the organs20, making the protein an important diagnostic target.The majority of mRDTs distributed in the SSA are for P. falciparum parasites lacking both the loci encoding HRP2 and its isoform HRP3 (encoded by pfhrp3), which has sufficient similarity to HRP2 and is recognised by the monoclonal antibodies used on the RDT test strips. P. falciparum parasites lacking the locus are thus not detected by HRP2-RDT. In the Amazon region of South America, these deletions have a reported prevalence of 28.6%, leading to recommendations to immediately stop using HRP2-RDTs21. Evidence from Asian countries also suggests the presence of P. falciparum lacking pfhrp2, including in India with a prevalence of 4%22 and China-Maynmar border with a prevalence of 5%23. Across Africa, Eritrea reported the highest prevalence of pfhrp2 and pfhrp3 deletions of 80.8% and 92.3%, respectively, which prompted a switch to non-HRP2-RDTs25. Studies elsewhere in East Africa also showed evidence of low proportions of pfhrp2/3 gene deletions from field isolates29.Studies in the last decade have reported the presence of As Tanzania embarks on a malaria pre-elimination strategy, RDTs will play a crucial role in case detection, and assessment of their performance is vital. This study set out to assess the performance of HRP2-RDTs in two areas in Tanzania with different malaria transmission intensities.A total of 998 blood samples, 472 from Moshi and 526 from Handeni sites were collected and investigated for pfhrp2/3 deletions using LDH/HRP2-RDT, microscopy and nested PCR. In Moshi site, only 1 sample (0.2%) was positive by mRDT while in the Handeni site, 203 (38.6%) samples were positive. Analysis by nested PCR revealed that 135 (13.5%) samples were positive, of which 19 (4%) positive samples were from Moshi and 116 (22%) positives were from Handeni as shown in Table P. falciparum species-specific PCR or microscopy were analysed for pfhrp2 and pfhrp3 deletions. Of the 122 samples, four samples were pfhrp2 PCR negative, and two of the four samples (1.6%) had relative parasitaemia of ≥ 5 p/µl and were considered true pfhrp2 deletion. The two samples with the confirmed pfhrp2 deletion had parasiteamia of 144 p/µl and 440 p/µl. Pfhrp3 analysis showed that 52% (63/122) of the samples were negative, and further analysis on parasitaemia revealed that the majority  had a relative parasite density of ≥ 5/µl  were not observed in this study.In this study, we report evidence of pfhrp2/3 deletions rely solely on discordant results between microscopy and mRDT or a dual-antigen HRP2-RDT as an algorithm for suspecting the deletions36. All the microscopy positive samples were screened in this analysis, irrespective of symptoms, even if they had positive mRDT results. Studies have shown persistence of HRP2 in the plasma up to 28 days after treatment particularly in high parasitaemia infections33, in that case, a person can have a new infection with P. falciparum parasites lacking pfhrp2/3 genes but have a circulating HRP2 from the previous infection commonly observed in areas with high malaria transmission. These findings suggest the importance of timing of the surveillance and considerations should be given to find optimum time when to survey pfhrp2/3 deletions during the transmission season. In the absence of the pfhrp2 locus, circulating HRP3 protein alone can give a positive HRP-RDT signal when parasitaemia is more than 1000 parasite/µl37. High sequence and structural homology between the two isoform proteins is the accepted reason for this39. This phenomenon can mask the deletion effect on either of the genes on the performance of mRDT at moderate to high parasite density.Guidelines on pfhrp2/3 deletions in asymptomatic individuals highlights the importance of surveying individuals with different disease spectrum and documenting the importance pfhrp2/3 deletion in disease outcome40. The WHO master protocol for surveillance of pfhrp2/pfhrp3 deletion, which emphasizes a health-center-based approach targeting symptomatic individuals, could miss deletions in asymptomatic individuals41. However, it is not clear what proportion of the deletions is contributed from asymptomatic individuals, and their inclusion may not be operationally feasible. The use of HRP2-based RDT will likely exert selective pressure on the parasite population and could lead to the spread of pfhrp2/3-deleted parasites42.The presence of pfhrp2 and pfhrp3 deletions in P. falciparum isolates from Tanzania. This makes it urgent for systematic surveillance of pfhrp2/3 deletions to understand the prevalence and extent of such deletions in Tanzania. The high proportion of pfhrp3 deletion attracts attention and there is now a need to understand what drives these deletions through the transmission season. Findings from this study support the idea of screening for pfhrp2/3 deletions even in mRDT positive samples, bearing in mind the cross-reactivity between the two proteins but also the false positivity of mRDTs due to persistence of plasma HRP2/HRP3 after treatment.This study provides evidence of P. falciparum infections44. We might have underestimated gene deletions since we did not estimate the multiplicity of infection in the study area. A positive signal from non-falciparum parasites may have also caused a reaction in the LDH line but we couldn’t verify this as we did not have DNA left. The use of a high throughput multiplex qPCR targeting single copy parasite gene can resolve the veiled effect of polyclonal infection in pfhrp2/3 gene deletion39.The study was conducted in the middle of peak transmission season (April–June 2018) and in an area previously reported to have high polyclonal 46. The study area has two rainy seasons per year, which denotes the peaks of malaria transmission. The long rainy season is from March-June and the short rainy season is from October–November. The area is located at 309 m above sea level, 5°22′60″ N and 38°34′60″ E.This study was conducted in Lower-Moshi in Kilimanjaro and Handeni Tanga region Fig. . Handeni47.The second study site was Lower Moshi , The area is about 800 m above sea level, south of Mount Kilimanjaro. It is one of the sentinel sites for insecticide resistance surveillance under the National Malaria Control Programme. Transmission of malaria in Lower Moshi occurs throughout the year with a prevalence of < 0.1%pfhrp2/3 gene deletion PCR is 0.5 (proportion in the population), Power = 0.80, Alpha = 0.05 (two-sided), Anticipated difference = 0.1, Alternative p = 0.4, Design effect = 2. The estimated required sample size in each site was 194*2 = 388 per study site, (Using STATA software). The total estimated minimum sample size was 776 in the two study sites. In this study we enrolled 1013 participants but presented results for only 998 participants, enrolment forms for 15 individuals had missing information and were dropped from the analysis.The sample size was calculated assuming the following parameters, proportion for Community sensitization meetings were organized, whereby the study personnel explained the study and answered any questions in an open forum before participant recruitment. Enrolment occurred in health facilities in each site, participants were enrolled only after verification of potential eligibility, explaining the study in Swahili  and consent to participate. Each member of the community had an equal chance of being selected as a participant.Plasmodium falciparum and Plasmodium lactate dehydrogenase (pLDH) for the Plasmodium species.Whole blood was collected just after the rainy season, from April to June 2018. The diagnosis on enrolled participants was done on-site, whereby a qualitative SD BIOLINE Malaria Ag P.f/Pan test (Standard diagnostics INC. Korea) detects histidine-rich protein II (HRP-II) antigen of 48.Thick and thin smears were prepared and left to air dry at room temperature, thin smears were carefully sprayed with absolute methanol for fixation. Staining was done with 10% Giemsa stain for 30 min, thereafter the stain was washed away by tap water and left to dry. Double reading of the slides was done by a separate microscopist and discordant results were then resolved by a third reader and results were presented as the number of parasites in every 200 white blood cells. Microscopists received their training from the Malaria Diagnostic Centre of Excellence in Kisumu Kenya50.Genomic DNA was extracted from dried blood spots (DBS) using a robotic DNA extraction system  at the London School of Hygiene Tropical Medicine (LSHTM)- UK, the method previously validated and publishedPlasmodium falciparum was detected using a standard nested-PCR technique targeting 18S ribosomal RNA, primers and PCR conditions were as previously published51.Plasmodium (PgMET) gene and human beta-tubulin gene (HumTUBB) was used to relatively estimate parasite density in the samples. HumTUBB was used as an internal DNA extraction control and Plasmodium falciparum international standard as a calibrator52. Primers and probes used  and Pfhrp2-R1 (5′-AATAAATTTAATGGCGTAGGCA-3′). Nest-2 primers used were Pfhrp2-F2 (5′-ATTATTACACGAAACTCAAGCAC-3′) and Pfhrp2-R1. PCR reaction mix contained a final concentration of 0.2 µM for each primer, 0.5 µM of deoxynucleoside triphosphate (dNTP) mix (Promega), 0.2 U of Taq-polymerase (AmpliTaq Gold-Applied biosystems), and 5 µl of the DNA template. Amplification thermo-profile for the reaction was 95 °C for 10 min (Enzyme activation) followed by 40 cycles of 94 °C for 50 s, 50 °C for 30 s and 65 °C for 1 min. Final elongation was done at 70 °C for 15 min then at 4 °C until removed from the machine. Primers used for the amplification of Pfhrp3 gene were Pfhrp3-F1 (5′-AATGCAAAAGGACTTAATTC-3′), Pfhrp3-R1 (5′-TGGTGTAAGTGATGCGTAGT-3′), Pfhrp3-F2 (5′-AAATAAGAGATTATTACACGAAAG-3′) and Pfhrp3-R1. Master mix and amplification profiles were the same as for Pfhrp2. Laboratory strains Dd2 and HB3 were used as positive controls for pfhrp2 and pfhrp3 deletion respectively.The amplification of A descriptive analysis was performed using Stata 16 software , with proportions and frequencies detailed in tables and figures. A log transformation of relative parasitaemia was applied to compare the mean and median values. Box plots were used to show the median, the 25th, and 75th percentiles of the non-parametric test.This study was assessed and approved by the Kilimanjaro Christian Medical University College Research Ethics Review Committee and given ethical clearance certificate # 2238 of the research proposal # 1084. Consent from study participants was sought before enrolment, guidelines and regulations that safeguard participants were also observed.