Dissecting the determinants of malaria chronicity: why within-host models struggle to reproduce infection dynamics

Proliferation in malaria parasites: How resource limitation can prevent evolution of greater virulence

For parasites, robust proliferation within hosts is crucial for establishing the infection and creating opportunities for onward transmission. While faster proliferation enhances transmission rates, it is often assumed to curtail transmission duration by killing the host (virulence), a trade-off constraining parasite evolution. Yet in many diseases, including malaria, the preponderance of infections with mild or absent symptoms suggests that host mortality is not a sufficient constraint, raising the question of what restrains evolution toward faster proliferation. In malaria infections, the maximum rate of proliferation is determined by the burst size, the number of daughter parasites produced per infected red blood cell. Larger burst sizes should expand the pool of infected red blood cells that can be used to produce the specialized transmission forms needed to infect mosquitoes. We use a within-host model parameterized for rodent malaria parasites (Plasmodium chabaudi) to project the transmission consequences of burst size, focusing on initial acute infection where resource limitation and risk of host mortality are greatest. We find that resource limitation restricts evolution toward higher burst sizes below the level predicted by host mortality alone. Our results suggest resource limitation could represent a more general constraint than virulence-transmission trade-offs, preventing evolution towards faster proliferation.

From multiplicity of infection to force of infection for sparsely sampled Plasmodium falciparum populations at high transmission

High multiplicity of infection or MOI, the number of genetically distinct parasite strains co-infecting a single human host, characterizes infectious diseases including falciparum malaria at high transmission. It accompanies high asymptomatic Plasmodium falciparum prevalence despite high exposure, creating a large transmission reservoir challenging intervention. High MOI and asymptomatic prevalence are enabled by immune evasion of the parasite achieved via vast antigenic diversity. Force of infection or FOI, the number of new infections acquired by an individual host over a given time interval, is the dynamic sister quantity of MOI, and a key epidemiological parameter for monitoring the impact of antimalarial interventions and assessing vaccine or drug efficacy in clinical trials. FOI remains difficult, expensive, and labor-intensive to accurately measure, especially in high-transmission regions, whether directly via cohort studies or indirectly via the fitting of epidemiological models to repeated cross-sectional surveys. We propose here the application of queuing theory to obtain FOI on the basis of MOI, in the form of either a two-moment approximation method or Little's law. We illustrate these methods with MOI estimates obtained under sparse sampling schemes with the recently proposed " v a r coding" method, based on sequences of the v a r multigene family encoding for the major variant surface antigen of the blood stage of malaria infection. The methods are evaluated with simulation output from a stochastic agent-based model, and are applied to an interrupted time-series study from Bongo District in northern Ghana before and immediately after a three-round transient indoor residual spraying (IRS) intervention. We incorporate into the sampling of the simulation output, limitations representative of those encountered in the collection of field data, including under-sampling of v a r genes, missing data, and usage of antimalarial drug treatment. We address these limitations in MOI estimates with a Bayesian framework and an imputation bootstrap approach. We demonstrate that both proposed methods give good and consistent FOI estimates across various simulated scenarios. Their application to the field surveys shows a pronounced reduction in annual FOI during intervention, of more than 70%. The proposed approach should be applicable to the many geographical locations where cohort or cross-sectional studies with regular and frequent sampling are lacking but single-time-point surveys under sparse sampling schemes are available, and for MOI estimates obtained in different ways. They should also be relevant to other pathogens of humans, wildlife and livestock whose immune evasion strategies are based on large antigenic variation resulting in high multiplicity of infection.

Red blood cell dynamics during malaria infection violate the assumptions of mathematical models of infection dynamics

For decades, mathematical models have been used to understand the course and outcome of malaria infections (i.e., infection dynamics) and the evolutionary dynamics of the parasites that cause them. A key conclusion of these models is that red blood cell (RBC) availability is a fundamental driver of infection dynamics and parasite trait evolution. The extent to which this conclusion holds will in part depend on model assumptions about the host-mediated processes that regulate RBC availability i.e., removal of uninfected RBCs and supply of RBCs. Diverse mathematical functions have been used to describe host-mediated RBC supply and clearance, but it remains unclear whether they adequately capture the dynamics of RBC supply and clearance during infection. Here, we use a unique dataset, comprising time-series measurements of erythrocyte (i.e., mature RBC) and reticulocyte (i.e., newly supplied RBC) densities during Plasmodium chabaudi malaria infection, and a quantitative data-transformation scheme to elucidate whether RBC dynamics conform to common model assumptions. We found that RBC clearance and supply are not well described by mathematical functions commonly used to model these processes. Furthermore, the temporal dynamics of both processes vary with parasite growth rate in a manner again not captured by existing models. Together, these finding suggest that new model formulations are required if we are to explain and ultimately predict the within-host population dynamics and evolution of malaria parasites.

Investigating the dose-dependency of the midgut escape barrier using a mechanistic model of within-mosquito dengue virus population dynamics

Flaviviruses are arthropod-borne (arbo)viruses which can emerge rapidly and cause explosive epidemics of severe disease. Some of the most epidemiologically important flaviviruses, including dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), are transmitted by Aedes mosquitoes, most notably Aedes aegypti and Aedes albopictus. After a mosquito blood feeds on an infected host, virus enters the midgut and infects the midgut epithelium. The virus must then overcome a series of barriers before reaching the mosquito saliva and being transmitted to a new host. The virus must escape from the midgut (known as the midgut escape barrier; MEB), which is thought to be mediated by transient changes in the permeability of the midgut-surrounding basal lamina layer (BL) following blood feeding. Here, we present a mathematical model of the within-mosquito population dynamics of flaviviruses that includes the interaction of the midgut and BL which can account for the MEB. Our results indicate a dose-dependency of midgut establishment of infection as well as rate of escape from the midgut: collectively, these suggest that the extrinsic incubation period (EIP) - the time taken for DENV virus to be transmissible after infection - is shortened when mosquitoes imbibe more virus. Additionally, our experimental data indicates that multiple blood feeding events, which more closely mimic mosquito-feeding behavior in the wild, can hasten the course of infections, and our model predicts that this effect is sensitive to the amount of virus imbibed. Our model indicates that mutations to the virus which impact its replication rate in the midgut could lead to even shorter EIPs when double-feeding occurs. Mechanistic models of within-vector viral infection dynamics provide a quantitative understanding of infection dynamics and could be used to evaluate novel interventions that target the mosquito stages of the infection.

Author summary

Aedes mosquitoes are the main vectors of dengue virus (DENV), Zika virus (ZIKV) and yellow fever virus (YFV), all of which can cause severe disease in humans with dengue alone infecting an estimated 100-400 million people each year. Understanding the processes that affect whether, and at which rate, mosquitoes may transmit such viruses is, hence, paramount. Here, we present a mathematical model of virus dynamics within infected mosquitoes. By combining the model with novel experimental data, we show that the course of infection is sensitive to the initial dose of virus ingested by the mosquito. The data also indicates that mosquitoes which blood feed subsequent to becoming infected may be able to transmit infection earlier, which is reproduced in the model. This is important as many mosquito species feed multiple times during their lifespan and, any reduction in time to dissemination will increase the number of days that a mosquito is infectious and so enhance the risk of transmission. Our study highlights the key and complementary roles played by mathematical models and experimental data for understanding within-mosquito virus dynamics.

Dynamics of parasite growth in genetically diverse Plasmodium falciparum isolates

Multiple parasite lineages with different proliferation rates or fitness may coexist within a clinical malaria isolate, resulting in complex growth interactions and variations in phenotype. To elucidate the dynamics of parasite growth in multiclonal isolates, we measured growth rates (GRs) of three Plasmodium falciparum Cambodian isolates, including IPC_3445 (MRA-1236), IPC_5202 (MRA-1240), IPC_6403 (MRA-1285), and parasite lineages previously cloned from each of these isolates by limiting dilution. Following synchronization, in vitro cultures of each parasite line were maintained over four consecutive asexual cycles (192 h), with thin smears prepared at each 48-h cycle to estimate GR and fold change in parasitemia (FCP). Cell cycle time (CCT), the duration it takes for ring-stage parasites to develop into mature schizonts, was measured by monitoring the development of 0-3-h post-invasion rings for up to 52 h post-incubation. Laboratory lines 3D7 (MRA-102) and Dd2 (MRA-150) were used as controls. Significant differences in GR, FCP, and CCT were observed between parasite isolates and clonal lineages from each isolate. The parasite lines studied here have well-defined growth phenotypes and will facilitate basic malaria research and development of novel malaria interventions. These lines are available to malaria researchers through the MR4 collection of NIAID's BEI Resources Program.

The mystery of persistent, asymptomatic Plasmodium falciparum infections

Plasmodium falciparum causes millions of malaria infections and hundreds of thousands of deaths annually. These parasites avoid the adaptive immune response by systematically cycling through a limited repertoire of variant surface antigens after which the number of circulating parasites drops to extremely low levels, coinciding with a loss of symptoms and eventual clearance of the infection. However, in regions with extended dry seasons or in individuals who no longer reside in endemic areas, asymptomatic infections have been observed to persist for many months or years, potentially serving as reservoirs for transmission. Recent work suggests the possibility that parasites can assume a state in which no variant surface antigens are expressed, thus rendering them virtually invisible to the immune system and enabling them to persist at low levels indefinitely.

Patient variability in the blood-stage dynamics of Plasmodium falciparum captured by clustering historical data

Background

Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation.

Methods

Patients’ parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization).

Results

To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles.

Conclusion

This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04317-0.

Declines in prevalence alter the optimal level of sexual investment for the malaria parasite Plasmodium falciparum

Like most human pathogens, the malaria parasite Plasmodium falciparum experiences strong selection pressure from public health interventions such as drug treatment. While most commonly studied in the context of drug targets and related pathways, parasite adaptation to control measures likely extends to phenotypes beyond drug resistance. Here, we use modeling to explore how control measures can reduce levels of within-host competition between P. falciparum genotypes and favor higher rates of sexual investment. We validate these predictions with longitudinally sampled genomic data from French Guiana during a period of malaria decline and find that the most strongly selected genes are enriched for transcription factors involved in commitment to and development of the parasite’s sexual gametocyte form.

Successful infectious disease interventions can result in large reductions in parasite prevalence. Such demographic change has fitness implications for individual parasites and may shift the parasite’s optimal life history strategy. Here, we explore whether declining infection rates can alter Plasmodium falciparum’s investment in sexual versus asexual growth. Using a multiscale mathematical model, we demonstrate how the proportion of polyclonal infections, which decreases as parasite prevalence declines, affects the optimal sexual development strategy: Within-host competition in multiclone infections favors a greater investment in asexual growth whereas single-clone infections benefit from higher conversion to sexual forms. At the same time, drug treatment also imposes selection pressure on sexual development by shortening infection length and reducing within-host competition. We assess these models using 148 P. falciparum parasite genomes sampled in French Guiana over an 18-y period of intensive intervention (1998 to 2015). During this time frame, multiple public health measures, including the introduction of new drugs and expanded rapid diagnostic testing, were implemented, reducing P. falciparum malaria cases by an order of magnitude. Consistent with this prevalence decline, we see an increase in the relatedness among parasites, but no single clonal background grew to dominate the population. Analyzing individual allele frequency trajectories, we identify genes that likely experienced selective sweeps. Supporting our model predictions, genes showing the strongest signatures of selection include transcription factors involved in the development of P. falciparum’s sexual gametocyte form. These results highlight how public health interventions impose wide-ranging selection pressures that affect basic parasite life history traits.

Systems biology of malaria explored with nonhuman primates

"The Primate Malarias" book has been a uniquely important resource for multiple generations of scientists, since its debut in 1971, and remains pertinent to the present day. Indeed, nonhuman primates (NHPs) have been instrumental for major breakthroughs in basic and pre-clinical research on malaria for over 50 years. Research involving NHPs have provided critical insights and data that have been essential for malaria research on many parasite species, drugs, vaccines, pathogenesis, and transmission, leading to improved clinical care and advancing research goals for malaria control, elimination, and eradication. Whilst most malaria scientists over the decades have been studying Plasmodium falciparum, with NHP infections, in clinical studies with humans, or using in vitro culture or rodent model systems, others have been dedicated to advancing research on Plasmodium vivax, as well as on phylogenetically related simian species, including Plasmodium cynomolgi, Plasmodium coatneyi, and Plasmodium knowlesi. In-depth study of these four phylogenetically related species over the years has spawned the design of NHP longitudinal infection strategies for gathering information about ongoing infections, which can be related to human infections. These Plasmodium-NHP infection model systems are reviewed here, with emphasis on modern systems biological approaches to studying longitudinal infections, pathogenesis, immunity, and vaccines. Recent discoveries capitalizing on NHP longitudinal infections include an advanced understanding of chronic infections, relapses, anaemia, and immune memory. With quickly emerging new technological advances, more in-depth research and mechanistic discoveries can be anticipated on these and additional critical topics, including hypnozoite biology, antigenic variation, gametocyte transmission, bone marrow dysfunction, and loss of uninfected RBCs. New strategies and insights published by the Malaria Host-Pathogen Interaction Center (MaHPIC) are recapped here along with a vision that stresses the importance of educating future experts well trained in utilizing NHP infection model systems for the pursuit of innovative, effective interventions against malaria.

Understanding dynamics of Plasmodium falciparum gametocytes production: Insights from an age-structured model

Many models of within-host malaria infection dynamics have been formulated since the pioneering work of Anderson et al. in 1989. Biologically, the goal of these models is to understand what governs the severity of infections, the patterns of infectiousness, and the variation thereof across individual hosts. Mathematically, these models are based on dynamical systems, with standard approaches ranging from K-compartments ordinary differential equations (ODEs) to delay differential equations (DDEs), to capture the relatively constant duration of replication and bursting once a parasite infects a host red blood cell. Using malariatherapy data, which offers fine-scale resolution on the dynamics of infection across a number of individual hosts, we compare the fit and robustness of one of these standard approaches (K-compartments ODE) with a partial differential equations (PDEs) model, which explicitly tracks the "age" of an infected cell. While both models perform quite similarly in terms of goodness-of-fit for suitably chosen K, the K-compartments ODE model particularly overestimates parasite densities early on in infections when the number of repeated compartments is not large enough. Finally, the K-compartments ODE model (for suitably chosen K) and the PDE model highlight a strong qualitative connection between the density of transmissible parasite stages (i.e., gametocytes) and the density of host-damaging (and asexually-replicating) parasite stages. This finding provides a simple tool for predicting which hosts are most infectious to mosquitoes -vectors of Plasmodium parasites- which is a crucial component of global efforts to control and eliminate malaria.

Genetic surveillance for monitoring the impact of drug use on Plasmodium falciparum populations

The use of antimalarial drugs is an effective strategy in the fight against malaria. However, selection of drug resistant parasites is a constant threat to the continued use of this approach. Antimalarial drugs are used not only to treat infections but also as part of population-level strategies to reduce malaria transmission toward elimination. While there is strong evidence that the ongoing use of antimalarial drugs increases the risk of the emergence and spread of drug-resistant parasites, it is less clear how population-level use of drug-based interventions like seasonal malaria chemoprevention (SMC) or mass drug administration (MDA) may contribute to drug resistance or loss of drug efficacy. Critical to sustained use of drug-based strategies for reducing the burden of malaria is the surveillance of population-level signals related to transmission reduction and resistance selection. Here we focus on Plasmodium falciparum and discuss the genetic signatures of a parasite population that are correlated with changes in transmission and related to drug pressure and resistance as a result of drug use. We review the evidence for MDA and SMC contributing to malaria burden reduction and drug resistance selection and examine the use and impact of these interventions in Senegal. Throughout we consider best strategies for ongoing surveillance of both population and resistance signals in the context of different parasite population parameters. Finally, we propose a roadmap for ongoing surveillance during population-level drug-based interventions to reduce the global malaria burden.

Immune selection suppresses the emergence of drug resistance in malaria parasites but facilitates its spread

Although drug resistance in Plasmodium falciparum typically evolves in regions of low transmission, resistance spreads readily following introduction to regions with a heavier disease burden. This suggests that the origin and the spread of resistance are governed by different processes, and that high transmission intensity specifically impedes the origin. Factors associated with high transmission, such as highly immune hosts and competition within genetically diverse infections, are associated with suppression of resistant lineages within hosts. However, interactions between these factors have rarely been investigated and the specific relationship between adaptive immunity and selection for resistance has not been explored. Here, we developed a multiscale, agent-based model of Plasmodium parasites, hosts, and vectors to examine how host and parasite dynamics shape the evolution of resistance in populations with different transmission intensities. We found that selection for antigenic novelty (“immune selection”) suppressed the evolution of resistance in high transmission settings. We show that high levels of population immunity increased the strength of immune selection relative to selection for resistance. As a result, immune selection delayed the evolution of resistance in high transmission populations by allowing novel, sensitive lineages to remain in circulation at the expense of the spread of a resistant lineage.

In contrast, in low transmission settings, we observed that resistant strains were able to sweep to high population prevalence without interference. Additionally, we found that the relationship between immune selection and resistance changed when resistance was widespread. Once resistance was common enough to be found on many antigenic backgrounds, immune selection stably maintained resistant parasites in the population by allowing them to proliferate, even in untreated hosts, when resistance was linked to a novel epitope. Our results suggest that immune selection plays a role in the global pattern of resistance evolution.

Drug resistance in the malaria parasite, Plasmodium falciparum, presents an ongoing public health challenge, but aspects of its evolution are poorly understood. Although antimalarial resistance is common worldwide, it can typically be traced to just a handful of evolutionary origins. Counterintuitively, although Sub Saharan Africa bears 90% of the global malaria burden, resistance typically originates in regions where transmission intensity is low. In high transmission regions, infections are genetically diverse, and hosts have significant standing adaptive immunity, both of which are known to suppress the frequency of resistance within infections. However, interactions between immune-driven selection, transmission intensity, and resistance have not been investigated. Using a multiscale, agent-based model, we found that high transmission intensity slowed the evolution of resistance via its effect on host population immunity. High host immunity strengthened selection for antigenic novelty, interfering with selection for resistance and allowing sensitive lineages to suppress resistant lineages in untreated hosts. However, once resistance was common in the circulating parasite population, immune selection maintained it in the population at a high prevalence. Our findings provide a novel explanation for observations about the origin of resistance and suggest that adaptive immunity is a critical component of selection.

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

Background

Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host’s immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models.

Methods

Mechanistic within-host models of parasite dynamics were identified through a review of published literature. For a subset of these, model code was reproduced and descriptive statistics compared between the models using fitted data. Through simulation and model analysis, key features of the models were compared, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability.

Results

The assessed within-host malaria models generally replicate infection dynamics in malaria-naïve individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitaemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models.

Conclusions

This study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, it is proposed that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterization and large stochasticity which inaccurately represent unknown disease mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03813-z.

Evolution of transcriptional control of antigenic variation and virulence in human and ape malaria parasites

Background

The most severe form of human malaria is caused by the protozoan parasite Plasmodium falciparum. This unicellular organism is a member of a subgenus of Plasmodium called the Laverania that infects apes, with P. falciparum being the only member that infects humans. The exceptional virulence of this species to humans can be largely attributed to a family of variant surface antigens placed by the parasites onto the surface of infected red blood cells that mediate adherence to the vascular endothelium. These proteins are encoded by a large, multicopy gene family called var, with each var gene encoding a different form of the protein. By changing which var gene is expressed, parasites avoid immune recognition, a process called antigenic variation that underlies the chronic nature of malaria infections.

Results

Here we show that the common ancestor of the branch of the Laverania lineage that includes the human parasite underwent a remarkable change in the organization and structure of elements linked to the complex transcriptional regulation displayed by the var gene family. Unlike the other members of the Laverania, the clade that gave rise to P. falciparum evolved distinct subsets of var genes distinguishable by different upstream transcriptional regulatory regions that have been associated with different expression profiles and virulence properties. In addition, two uniquely conserved var genes that have been proposed to play a role in coordinating transcriptional switching similarly arose uniquely within this clade. We hypothesize that these changes originated at a time of dramatic climatic change on the African continent that is predicted to have led to significant changes in transmission dynamics, thus selecting for patterns of antigenic variation that enabled lengthier, more chronic infections.

Conclusions

These observations suggest that changes in transmission dynamics selected for significant alterations in the transcriptional regulatory mechanisms that mediate antigenic variation in the parasite lineage that includes P. falciparum. These changes likely underlie the chronic nature of these infections as well as their exceptional virulence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01872-z.

Deconstructing the parasite multiplication rate of Plasmodium falciparum

Epidemiological indicators describing population-level malaria transmission dynamics are widely used to guide policy recommendations. However, the determinants of malaria outcomes within individuals are still poorly understood. This conceptual gap partly reflects the fact that there are few indicators that robustly predict the trajectory of individual infections or clinical outcomes. The parasite multiplication rate (PMR) is a widely used indicator for the Plasmodium intraerythrocytic development cycle (IDC), for example, but its relationship to clinical outcomes is complex. Here, we review its calculation and use in P. falciparum malaria research, as well as the parasite and host factors that impact it. We also provide examples of metrics that can help to link within-host dynamics to malaria clinical outcomes when used alongside the PMR.

Expansion of Functional Myeloid-Derived Suppressor Cells in Controlled Human Malaria Infection

Malaria can cause life-threatening complications which are often associated with inflammatory reactions. More subtle, but also contributing to the burden of disease are chronic, often subclinical infections, which result in conditions like anemia and immunologic hyporesponsiveness. Although very frequent, such infections are difficult to study in endemic regions because of interaction with concurrent infections and immune responses. In particular, knowledge about mechanisms of malaria-induced immunosuppression is scarce. We measured circulating immune cells by cytometry in healthy, malaria-naïve, adult volunteers undergoing controlled human malaria infection (CHMI) with a focus on potentially immunosuppressive cells. Infectious Plasmodium falciparum (Pf) sporozoites (SPZ) (PfSPZ Challenge) were inoculated during two independent studies to assess malaria vaccine efficacy. Volunteers were followed daily until parasites were detected in the circulation by RT-qPCR. This allowed us to analyze immune responses during pre-patency and at very low parasite densities in malaria-naïve healthy adults. We observed a consistent increase in circulating polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) in volunteers who developed P. falciparum blood stage parasitemia. The increase was independent of preceding vaccination with a pre-erythrocytic malaria vaccine. PMN-MDSC were functional, they suppressed CD4⁺ and CD8⁺ T cell proliferation as shown by ex-vivo co-cultivation with stimulated T cells. PMN-MDSC reduced T cell proliferation upon stimulation by about 50%. Interestingly, high circulating PMN-MDSC numbers were associated with lymphocytopenia. The number of circulating regulatory T cells (T_reg) and monocytic MDSC (M-MDSC) showed no significant parasitemia-dependent variation. These results highlight PMN-MDSC in the peripheral circulation as an early indicator of infection during malaria. They suppress CD4⁺ and CD8⁺ T cell proliferation in vitro. Their contribution to immunosuppression in vivo in subclinical and uncomplicated malaria will be the subject of further research. Pre-emptive antimalarial pre-treatment of vaccinees to reverse malaria-associated PMN-MDSC immunosuppression could improve vaccine response in exposed individuals.

An antigenic diversification threshold for falciparum malaria transmission at high endemicity

In malaria and several other important infectious diseases, high prevalence occurs concomitantly with incomplete immunity. This apparent paradox poses major challenges to malaria elimination in highly endemic regions, where asymptomatic Plasmodium falciparum infections are present across all age classes creating a large reservoir that maintains transmission. This reservoir is in turn enabled by extreme antigenic diversity of the parasite and turnover of new variants. We present here the concept of a threshold in local pathogen diversification that defines a sharp transition in transmission intensity below which new antigen-encoding genes generated by either recombination or migration cannot establish. Transmission still occurs below this threshold, but diversity of these genes can neither accumulate nor recover from interventions that further reduce it. An analytical expectation for this threshold is derived and compared to numerical results from a stochastic individual-based model of malaria transmission that incorporates the major antigen-encoding multigene family known as var. This threshold corresponds to an “innovation” number we call R_div; it is different from, and complementary to, the one defined by the classic basic reproductive number of infectious diseases, R₀, which does not readily is better apply under large and dynamic strain diversity. This new threshold concept can be exploited for effective malaria control and applied more broadly to other pathogens with large multilocus antigenic diversity.

The vast diversity of the falciparum malaria parasite, as seen by the immune system of hosts in high transmission regions, underlies both high prevalence of asymptomatic infections and partial protection to re-infection despite previous exposure. This large antigenic diversity of the parasite challenges control and elimination efforts. We propose a threshold quantity for antigenic innovation, we call R_div, measuring the potential of transmission to accumulate new antigenic variants over time. When R_div is pushed below one by reduced transmission intensity, new genes encoding this variation can no longer accumulate, resulting in a lower number of strains and facilitating further intervention. This innovation number can be applied to other infectious diseases with fast turnover of antigens, where large standing diversity similarly opposes successful intervention.

Understanding Host-Pathogen-Vector Interactions with Chronic Asymptomatic Malaria Infections

The last malaria parasite standing will display effective adaptations to selective forces. While substantial progress has been made in reducing malaria mortality, eradication will require elimination of all Plasmodium parasites, including those in asymptomatic infections. These typically chronic, low-density infections are difficult to detect, yet can persist for months. We argue that asymptomatic infection is the parasite's best asset for survival but it can be exploited if studied as a new model for host-pathogen-vector interactions. Regular sampling from cohorts of asymptomatic individuals can provide a means to investigate continuous parasite development within its natural host. State-of-the-art techniques can now be applied to such infections. This approach may reveal key molecular drivers of chronic infections - a critical step for malaria eradication.

Evolutionary consequences of feedbacks between within-host competition and disease control

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Partitioning the influence of ecology across scales on parasite evolution

Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Pairwise growth competitions identify relative fitness relationships among artemisinin resistant Plasmodium falciparum field isolates

Background

Competitive outcomes between co-infecting malaria parasite lines can reveal fitness disparities in blood stage growth. Blood stage fitness costs often accompany the evolution of drug resistance, with the expectation that relatively fitter parasites will be more likely to spread in populations. With the recent emergence of artemisinin resistance, it is important to understand the relative competitive fitness of the metabolically active asexual blood stage parasites. Genetically distinct drug resistant parasite clones with independently evolved sets of mutations are likely to vary in asexual proliferation rate, contributing to their chance of transmission to the mosquito vector.

Methods

An optimized in vitro 96-well plate-based protocol was used to quantitatively measure-head-to-head competitive fitness during blood stage development between seven genetically distinct field isolates from a hotspot of emerging artemisinin resistance and the laboratory strain, NF54. These field isolates were isolated from patients in Southeast Asia carrying different alleles of kelch13 and included both artemisinin-sensitive and artemisinin-resistant isolates. Fluorescent labeled microsatellite markers were used to track the relative densities of each parasite throughout the co-growth period of 14–60 days. All-on-all competitions were conducted for the panel of eight parasite lines (28 pairwise competitions) to determine their quantitative competitive fitness relationships.

Results

Twenty-eight pairwise competitive growth outcomes allowed for an unambiguous ranking among a set of seven genetically distinct parasite lines isolated from patients in Southeast Asia displaying a range of both kelch13 alleles and clinical clearance times and a laboratory strain, NF54. This comprehensive series of assays established the growth relationships among the eight parasite lines. Interestingly, a clinically artemisinin resistant parasite line that carries the wild-type form of kelch13 outcompeted all other parasites in this study. Furthermore, a kelch13 mutant line (E252Q) was competitively more fit without drug than lines with other resistance-associated kelch13 alleles, including the C580Y allele that has expanded to high frequencies under drug pressure in Southeast Asian resistant populations.

Conclusions

This optimized competitive growth assay can be employed for assessment of relative growth as an index of fitness during the asexual blood stage growth between natural lines carrying different genetic variants associated with artemisinin resistance. Improved understanding of the fitness costs of different parasites proliferating in human blood and the role different resistance mutations play in the context of specific genetic backgrounds will contribute to an understanding of the potential for specific mutations to spread in populations, with the potential to inform targeted strategies for malaria therapy.

The Challenge of Quantifying Synchrony in Malaria Parasites

Malaria infection is often accompanied by periodic fevers, triggered by synchronous cycles of parasite replication within the host. The degree of synchrony in parasite development influences the efficacy of drugs and immune defenses and is therefore relevant to host health and infectiousness. Synchrony is thought to vary over the course of infection and across different host-parasite genotype or species combinations, but the evolutionary significance - if any - of this diversity remains elusive. Standardized methods are lacking, but the most common metric for quantifying synchrony is the percentage of parasites in a particular developmental stage. We use a heuristic model to show that this metric is often unacceptably biased. Methodological challenges must be addressed to characterize diverse patterns of synchrony and their consequences for disease severity and spread.

The temporal dynamics and infectiousness of subpatent Plasmodium falciparum infections in relation to parasite density

Malaria infections occurring below the limit of detection of standard diagnostics are common in all endemic settings. However, key questions remain surrounding their contribution to sustaining transmission and whether they need to be detected and targeted to achieve malaria elimination. In this study we analyse a range of malaria datasets to quantify the density, detectability, course of infection and infectiousness of subpatent infections. Asymptomatically infected individuals have lower parasite densities on average in low transmission settings compared to individuals in higher transmission settings. In cohort studies, subpatent infections are found to be predictive of future periods of patent infection and in membrane feeding studies, individuals infected with subpatent asexual parasite densities are found to be approximately a third as infectious to mosquitoes as individuals with patent (asexual parasite) infection. These results indicate that subpatent infections contribute to the infectious reservoir, may be long lasting, and require more sensitive diagnostics to detect them in lower transmission settings.

The role of subpatent infections for malaria transmission and elimination is unclear. Here, Slater et al. analyse several malaria datasets to quantify the density, detectability, course of infection and infectiousness of subpatent infections.

Within-host competition can delay evolution of drug resistance in malaria

In the malaria parasite P. falciparum, drug resistance generally evolves first in low-transmission settings, such as Southeast Asia and South America. Resistance takes noticeably longer to appear in the high-transmission settings of sub-Saharan Africa, although it may spread rapidly thereafter. Here, we test the hypothesis that competitive suppression of drug-resistant parasites by drug-sensitive parasites may inhibit evolution of resistance in high-transmission settings, where mixed-strain infections are common. We employ a cross-scale model, which simulates within-host (infection) dynamics and between-host (transmission) dynamics of sensitive and resistant parasites for a population of humans and mosquitoes. Using this model, we examine the effects of transmission intensity, selection pressure, fitness costs of resistance, and cross-reactivity between strains on the establishment and spread of resistant parasites. We find that resistant parasites, introduced into the population at a low frequency, are more likely to go extinct in high-transmission settings, where drug-sensitive competitors and high levels of acquired immunity reduce the absolute fitness of the resistant parasites. Under strong selection from antimalarial drug use, however, resistance spreads faster in high-transmission settings than low-transmission ones. These contrasting results highlight the distinction between establishment and spread of resistance and suggest that the former but not the latter may be inhibited in high-transmission settings. Our results suggest that within-host competition is a key factor shaping the evolution of drug resistance in P. falciparum.

The malaria parasite Plasmodium falciparum has evolved resistance to most antimalarial drugs, greatly complicating treatment and control of the disease. Curiously, although sub-Saharan Africa accounts for the majority of the global burden of malaria, the evolution of drug resistance in Africa has been markedly delayed compared to Asia and the Americas. One reason might be that, in a population in which the prevalence of infection is high, a newly emerged drug-resistant strain faces a high risk of extinction due to competition from drug-sensitive parasites that already “occupy” most of the host population. Using a mathematical model, we confirm that drug-resistant parasites face a much greater risk of extinction in a “high-transmission” setting like sub-Saharan Africa than in a “low-transmission” setting like Southeast Asia. However, we also find that when drug-resistant parasites manage to avoid extinction, their subsequent spread may be more rapid in high-transmission settings than in low-transmission settings, especially when selection is strong. These results offer a novel explanation for global patterns of drug resistance evolution in malaria and suggest a new dimension to consider in resistance prevention and containment efforts: namely, the intrinsic favorability of low- and high-transmission settings for establishment and spread of drug resistance.

Mathematical modeling of climate change and malaria transmission dynamics: a historical review

Malaria, one of the greatest historical killers of mankind, continues to claim around half a million lives annually, with almost all deaths occurring in children under the age of five living in tropical Africa. The range of this disease is limited by climate to the warmer regions of the globe, and so anthropogenic global warming (and climate change more broadly) now threatens to alter the geographic area for potential malaria transmission, as both the Plasmodium malaria parasite and Anopheles mosquito vector have highly temperature-dependent lifecycles, while the aquatic immature Anopheles habitats are also strongly dependent upon rainfall and local hydrodynamics. A wide variety of process-based (or mechanistic) mathematical models have thus been proposed for the complex, highly nonlinear weather-driven Anopheles lifecycle and malaria transmission dynamics, but have reached somewhat disparate conclusions as to optimum temperatures for transmission, and the possible effect of increasing temperatures upon (potential) malaria distribution, with some projecting a large increase in the area at risk for malaria, but others predicting primarily a shift in the disease's geographic range. More generally, both global and local environmental changes drove the initial emergence of P. falciparum as a major human pathogen in tropical Africa some 10,000 years ago, and the disease has a long and deep history through the present. It is the goal of this paper to review major aspects of malaria biology, methods for formalizing these into mathematical forms, uncertainties and controversies in proper modeling methodology, and to provide a timeline of some major modeling efforts from the classical works of Sir Ronald Ross and George Macdonald through recent climate-focused modeling studies. Finally, we attempt to place such mathematical work within a broader historical context for the "million-murdering Death" of malaria.

Modeling the genetic relatedness of Plasmodium falciparum parasites following meiotic recombination and cotransmission

Unlike in most pathogens, multiple-strain (polygenomic) infections of P. falciparum are frequently composed of genetic siblings. These genetic siblings are the result of sexual reproduction and can coinfect the same host when cotransmitted by the same mosquito. The degree with which coinfecting strains are related varies among infections and populations. Because sexual recombination occurs within the mosquito, the relatedness of cotransmitted strains could depend on transmission dynamics, but little is actually known of the factors that influence the relatedness of cotransmitted strains. Part of the uncertainty stems from an incomplete understanding of how within-host and within-vector dynamics affect cotransmission. Cotransmission is difficult to examine experimentally but can be explored using a computational model. We developed a malaria transmission model that simulates sexual reproduction in order to understand what determines the relatedness of cotransmitted strains. This study highlights how the relatedness of cotransmitted strains depends on both within-host and within-vector dynamics including the complexity of infection. We also used our transmission model to analyze the genetic relatedness of polygenomic infections following a series of multiple transmission events and examined the effects of superinfection. Understanding the factors that influence the relatedness of cotransmitted strains could lead to a better understanding of the population-genetic correlates of transmission and therefore be important for public health.

Genomic studies of P. falciparum reveal that multi-strain infections can include genetically related strains. P. falciparum must reproduce sexually in the mosquito vector. One consequence of sexual reproduction is that parasites cotransmitted by the same mosquito are related to one another. The degree of genetic relatedness of these parasites can be as great as that of full-siblings. However, our understanding of the cotransmission process is incomplete, and little is known of the role of cotransmission in influencing population genomic processes. To help bridge this gap, we developed a simulation model to determine which of the steps involved in transmission have the greatest impact on the relatedness of parasites cotransmitted by a mosquito vector. The primary goal of this study is to characterize the outcomes of cotransmission following single or multiple transmission events. Our model yields new insights into the cotransmission process, which we believe will be useful for understanding the results from more complicated population models and epidemiological conditions. Such an understanding is important for the use of population genomics to inform public health decisions as well as for understanding of parasite evolution.

Plasmodium knowlesi: a superb in vivo nonhuman primate model of antigenic variation in malaria

Antigenic variation in malaria was discovered in Plasmodium knowlesi studies involving longitudinal infections of rhesus macaques (M. mulatta). The variant proteins, known as the P. knowlesi Schizont Infected Cell Agglutination (SICA) antigens and the P. falciparum Erythrocyte Membrane Protein 1 (PfEMP1) antigens, expressed by the SICAvar and var multigene families, respectively, have been studied for over 30 years. Expression of the SICA antigens in P. knowlesi requires a splenic component, and specific antibodies are necessary for variant antigen switch events in vivo. Outstanding questions revolve around the role of the spleen and the mechanisms by which the expression of these variant antigen families are regulated. Importantly, the longitudinal dynamics and molecular mechanisms that govern variant antigen expression can be studied with P. knowlesi infection of its mammalian and vector hosts. Synchronous infections can be initiated with established clones and studied at multi-omic levels, with the benefit of computational tools from systems biology that permit the integration of datasets and the design of explanatory, predictive mathematical models. Here we provide an historical account of this topic, while highlighting the potential for maximizing the use of P. knowlesi - macaque model systems and summarizing exciting new progress in this area of research.

Assessing the impact of imperfect adherence to artemether-lumefantrine on malaria treatment outcomes using within-host modelling

Artemether-lumefantrine (AL) is the most widely-recommended treatment for uncomplicated Plasmodium falciparum malaria worldwide. Its safety and efficacy have been extensively demonstrated in clinical trials; however, its performance in routine health care settings, where adherence to drug treatment is unsupervised and therefore may be suboptimal, is less well characterised. Here we develop a within-host modelling framework for estimating the effects of sub-optimal adherence to AL treatment on clinical outcomes in malaria patients. Our model incorporates the data on the human immune response to the parasite, and AL's pharmacokinetic and pharmacodynamic properties. Utilising individual-level data of adherence to AL in 482 Tanzanian patients as input for our model predicted higher rates of treatment failure than were obtained when adherence was optimal (9% compared to 4%). Our model estimates that the impact of imperfect adherence was worst in children, highlighting the importance of advice to caregivers.

Simulating within-vector generation of the malaria parasite diversity

Plasmodium falciparum, the most virulent human malaria parasite, undergoes asexual reproduction within the human host, but reproduces sexually within its vector host, the Anopheles mosquito. Consequently, the mosquito stage of the parasite life cycle provides an opportunity to create genetically novel parasites in multiply-infected mosquitoes, potentially increasing parasite population diversity. Despite the important implications for disease transmission and malaria control, a quantitative mapping of how parasite diversity entering a mosquito relates to diversity of the parasite exiting, has not been undertaken. To examine the role that vector biology plays in modulating parasite diversity, we develop a two-part model framework that estimates the diversity as a consequence of different bottlenecks and expansion events occurring during the vector-stage of the parasite life cycle. For the underlying framework, we develop the first stochastic model of within-vector P. falciparum parasite dynamics and go on to simulate the dynamics of two parasite subpopulations, emulating multiply infected mosquitoes. We show that incorporating stochasticity is essential to capture the extensive variation in parasite dynamics, particularly in the presence of multiple parasites. In particular, unlike deterministic models, which always predict the most fit parasites to produce the most sporozoites, we find that occasionally only parasites with lower fitness survive to the sporozoite stage. This has important implications for onward transmission. The second part of our framework includes a model of sequence diversity generation resulting from recombination and reassortment between parasites within a mosquito. Our two-part model framework shows that bottlenecks entering the oocyst stage decrease parasite diversity from what is present in the initial gametocyte population in a mosquito’s blood meal. However, diversity increases with the possibility for recombination and proliferation in the formation of sporozoites. Furthermore, when we begin with two parasite subpopulations in the initial gametocyte population, the probability of transmitting more than two unique parasites from mosquito to human is over 50% for a wide range of initial gametocyte densities.

In-host modeling

Understanding the mechanisms governing host-pathogen kinetics is important and can guide human interventions. In-host mathematical models, together with biological data, have been used in this endeavor. In this review, we present basic models used to describe acute and chronic pathogenic infections. We highlight the power of model predictions, the role of drug therapy, and advantage of considering the dynamics of immune responses. We also present the limitations of these models due in part to the trade-off between the complexity of the model and their predictive power, and the challenges a modeler faces in determining the appropriate formulation for a given problem.

Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal

Background

As public health interventions drive parasite populations to elimination, genetic epidemiology models that incorporate population genomics can be powerful tools for evaluating the effectiveness of continued intervention. However, current genetic epidemiology models may not accurately simulate the population genetic profile of parasite populations, particularly with regard to polygenomic (multi-strain) infections. Current epidemiology models simulate polygenomic infections via superinfection (multiple mosquito bites), despite growing evidence that cotransmission (a single mosquito bite) may contribute to polygenomic infections.

Methods

Here, we quantified the relatedness of strains within 31 polygenomic infections collected from patients in Thiès, Senegal using a hidden Markov model to measure the proportion of the genome that is inferred to be identical by descent.

Results

We found that polygenomic infections can be composed of highly related parasites and that superinfection models drastically underestimate the relatedness of strains within polygenomic infections.

Conclusions

Our findings suggest that cotransmission is a major contributor to polygenomic infections in Thiès, Senegal. The incorporation of cotransmission into existing genetic epidemiology models may enhance our ability to characterize and predict changes in population structure associated with reduced transmission intensities and the emergence of important phenotypes like drug resistance that threaten to undermine malaria elimination activities.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0398-0) contains supplementary material, which is available to authorized users.

THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites

As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings.

Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria

Background

The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos of efforts to eliminate falciparum malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively.

Methods

To obtain a quantitative estimate of the serial interval for falciparum malaria, the sum of the components of the falciparum malaria transmission cycle was taken based on a combination of mathematical models and empirical data. During this process, a number of factors were identified that account for substantial variability in the serial interval across different contexts.

Results

Treatment with anti-malarial drugs roughly halves the serial interval due to an abbreviated period of human infectiousness, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behaviour. Furthermore, 24.5 % of secondary cases presenting clinically did so prior to the primary cases being identified through active detection of infection.

Conclusions

These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of falciparum malaria and other diseases characterized by prolonged infections and complex ecological drivers.

Variation in infection length and superinfection enhance selection efficiency in the human malaria parasite

The capacity for adaptation is central to the evolutionary success of the human malaria parasite Plasmodium falciparum. Malaria epidemiology is characterized by the circulation of multiple, genetically diverse parasite clones, frequent superinfection, and highly variable infection lengths, a large number of which are chronic and asymptomatic. The impact of these characteristics on the evolution of the parasite is largely unknown, however, hampering our understanding of the impact of interventions and the emergence of drug resistance. In particular, standard population genetic frameworks do not accommodate variation in infection length or superinfection. Here, we develop a population genetic model of malaria including these variations, and show that these aspects of malaria infection dynamics enhance both the probability and speed of fixation for beneficial alleles in complex and non-intuitive ways. We find that populations containing a mixture of short- and long-lived infections promote selection efficiency. Interestingly, this increase in selection efficiency occurs even when only a small fraction of the infections are chronic, suggesting that selection can occur efficiently in areas of low transmission intensity, providing a hypothesis for the repeated emergence of drug resistance in the low transmission setting of Southeast Asia.