Qi Zhan

High multiplicity of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, characterizes falciparum malaria and other infectious diseases under high transmission. High MOI in Plasmodium falciparum accompanies high prevalence of asymptomatic infection despite high exposure, creating a large transmission reservoir that challenges intervention. This pattern is enabled by parasite immune evasion through extensive antigenic diversity. The force of infection (FOI), the number of new infections acquired by an individual host over a given time interval, is the dynamic counterpart of MOI and a key epidemiological parameter for monitoring antimalarial interventions. FOI is difficult and costly to measure, especially in high-transmission regions, requiring cohort studies or model-based inference from repeated cross-sectional surveys. Here, we apply queuing theory to estimate FOI from MOI with two approaches: a two-moment approximation and Little’s Law. We illustrate these methods using MOI estimates obtained under sparse sampling schemes with the ‘ var coding’ approach. Both methods rely on infection duration data from naive malaria therapy patients and are therefore suitable for subpopulations with limited immunity, such as toddlers. We evaluate their performance using output from a stochastic agent-based model and apply the methods to an interrupted time-series study in northern Ghana, before and immediately after a three-round transient indoor residual spraying intervention. By accounting for sampling limitations with a Bayesian framework and bootstrap imputation, both methods yield good and replicable FOI estimates across various simulated scenarios. Their application to the surveys of 1- to 5-year-old children in Ghana indicates a larger than 70% reduction in annual FOI immediately after intervention.

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. This high MOI 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 antimalarial interventions. 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 from MOI, in the form of either a two-moment approximation method or Little’s Law. We illustrate these two methods with MOI estimates obtained under sparse sampling schemes with the “varcoding” approach. The two methods use infection duration data from naive malaria therapy patients with neurosyphilis. Consequently, they are suitable for FOI inference in subpopulations with a similar immune profile and the highest vulnerability, for example, infants or toddlers. Both 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. The sampling of the simulation output incorporates limitations representative of those encountered in the collection of field data, including under-sampling of var genes, missing data, and antimalarial drug treatment. We address these limitations in MOI estimates with a Bayesian framework and an imputation bootstrap approach. Both methods yield good and replicable FOI estimates across various simulated scenarios. Applying these methods to the subpopulation of children aged 1-5 years in Ghana field surveys shows over a 70% reduction in annual FOI immediately post-intervention. The proposed methods should be applicable to geographical locations lacking cohort or cross-sectional studies with regular and frequent sampling but having single-time-point surveys under sparse sampling schemes, and for MOI estimates obtained in different ways. They should also be relevant to other pathogens whose immune evasion strategies are based on large antigenic variation resulting in high MOI.

Here, we introduce a new endpoint ‘census population size’ to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOI _var ), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOI _var from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOI _var in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high, seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by >90% and decreased parasite prevalence by ~40–50%, significant reductions in var diversity, MOI _var , and population size were observed in ~2000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short-lived and 32 months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1–5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity), demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Background: Mixed-species, mixed-strain plasmodia infections are known to occur in humans in malaria endemic areas. It may be surprising that to date, the extent of this complexity has not been systematically explored in high-burden countries of sub-Saharan Africa, especially in the reservoir of asymptomatic infections in all ages, which sustains transmission. Methods: Here we take a metagenomic lens to these infections by sampling variable blood volumes from 188 afebrile residents living in high, seasonal transmission in Northern Sahelian Ghana. We estimated multiplicity of infection for different Plasmodium spp. through genotyping of antigens and microsatellites. We further defined ‘metagenomic complexity’ as a measure of overall within-host complexity across the combination of species and strains. Results: We show that prevalence of Plasmodium spp. and inter-/intra-species complexity is significantly higher in larger blood volumes from these individuals. Overall, malaria infections display high levels of metagenomic complexity comprising single-, double-, and triple-species infections with varying levels of intra-species complexity for P. falciparum, P. malariae, P. ovale curtisi, and P. ovale wallikeri. We also report a subset of individuals with highly-complex infections that cannot be explained by age or location. The implications of these findings to malaria epidemiology and control are illustrated by a geographic scaling exercise to district and region levels in northern Ghana. Conclusions: Our metagenomic investigation underscores the need to more sensitively measure within-host Plasmodium spp. complexity in asymptomatic carriers of infection. This will optimise strategies for malaria surveillance and control.

Major surface antigens in many pathogens are encoded by rapidly diversifying multigene families, generating fitness variation through antigenic and functional differences. These variations align with the niche and absolute fitness axes of Modern Coexistence Theory (MCT). Yet, how such gene families evolve along these axes under competition for hosts and across transmission gradients remains poorly understood, as prior MCT studies have not explicitly accounted for evolutionary dynamics in high dimensions. We use a stochastic computational model of Plasmodium falciparum transmission to examine how transmission intensity and selection shape var multigene family evolution and composition within parasite genomes. Results show that selection alone cannot maintain the observed stable ratio of two gene groups within parasite genomes, indicating that group-based classifications do not clearly reflect transmission strategy or virulence. When a trade-off exists between diversification rates and absolute fitness, strong immune selection under high transmission favors fast-recombining genes while attenuating functional selection on R ₀ -associated traits. In general, stronger immune selection increases the invasion probability of novel antigens and the niche differentiation among parasite genomes, while reducing the variance in gene-level transmissibility and expression duration, and therefore R ₀ . This outcome, combining enhanced niche differentiation and reduced absolute fitness variation, departs from MCT predictions.

Intervention efforts against falciparum malaria in high-transmission regions remain challenging, with rapid resurgence typically following their relaxation. Such resilience co-occurs with incomplete immunity and a large transmission reservoir from high asymptomatic prevalence. Incomplete immunity relates to the large antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene and recombinant family known as var. With a stochastic agent-based model, we investigate the existence of a sharp transition in resurgence ability with intervention intensity and identify molecular indicators informative of its proximity. Their application to survey data with deep sampling of var sequences from individual isolates in northern Ghana suggests that the transmission system was brought close to transition by intervention with indoor residual spraying. These results indicate that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

High multiplicity of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, characterizes falciparum malaria and other infectious diseases under high transmission. High MOI in Plasmodium falciparum accompanies high prevalence of asymptomatic infection despite high exposure, creating a large transmission reservoir that challenges intervention. This pattern is enabled by parasite immune evasion through extensive antigenic diversity. The force of infection (FOI), the number of new infections acquired by an individual host over a given time interval, is the dynamic counterpart of MOI and a key epidemiological parameter for monitoring antimalarial interventions. FOI is difficult and costly to measure, especially in high-transmission regions, requiring cohort studies or model-based inference from repeated cross-sectional surveys. Here, we apply queuing theory to estimate FOI from MOI with two approaches: a two-moment approximation and Little’s Law. We illustrate these methods using MOI estimates obtained under sparse sampling schemes with the “ var coding” approach. Both methods rely on infection duration data from naive malaria therapy patients and are therefore suitable for subpopulations with limited immunity, such as toddlers. We evaluate their performance using output from a stochastic agent-based model and apply the methods to an interrupted time-series study in northern Ghana, before and immediately after a three-round transient indoor residual spraying intervention. By accounting for sampling limitations with a Bayesian framework and bootstrap imputation, both methods yield good and replicable FOI estimates across various simulated scenarios. Their application to the surveys of 1-5-year-old children in Ghana indicates a larger than 70% reduction in annual FOI immediately after intervention.

Intervention against falciparum malaria in high transmission regions remains challenging, with relaxation of control efforts typically followed by rapid resurgence. Resilience to intervention co-occurs with incomplete immunity, whereby children eventually become protected from severe disease but not infection and a large transmission reservoir results from high asymptomatic prevalence across all ages. Incomplete immunity relates to the vast antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene family known as var . Recent deep sampling of var sequences from individual isolates in northern Ghana showed that parasite population structure exhibited persistent features of high-transmission regions despite the considerable decrease in prevalence during transient intervention with indoor residual spraying (IRS). We ask whether despite such apparent limited impact, the transmission system had been brought close to a transition in both prevalence and resurgence ability. With a stochastic agent-based model, we investigate the existence of such a transition to pre-elimination with intervention intensity, and of molecular indicators informative of its approach. We show that resurgence ability decreases sharply and nonlinearly across a narrow region of intervention intensities in model simulations, and identify informative molecular indicators based on var gene sequences. Their application to the survey data indicates that the transmission system in northern Ghana was brought close to transition by IRS. These results suggest that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction. One Sentence Summary: Population genomics of hyper-diverse var genes inform resurgence dynamics in falciparum malaria.

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the var coding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While var coding can underestimate MOI, it allows robust estimation, especially under high-transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low- and moderate-transmission. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs. Author Summary: Despite control and elimination efforts, malaria continues to be a serious public health threat especially in high-transmission regions. Molecular tools for evaluating these efforts include those seeking to estimate multiplicity (or complexity) of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, a key epidemiological parameter. MOI estimation remains challenging in high-transmission regions where hosts typically carry multiple co-infections by Plasmodium falciparum . THE REAL McCOIL and the var coding are two cost-effective methods relying on distinct parts of the parasite genome, those respectively under neutrality and selection. The more recent var coding approach relies on the var multigene family encoding for the major blood-stage antigen and contributing to a complex immune evasion strategy of the parasite. We compare the performance of the two methods by simulating disease dynamics under different transmission intensities with a stochastic agent-based model tracking infection by different parasite genomes and immune memory in individual hosts, then sampling resulting infections to estimate MOI. Although THE REAL McCOIL provides reasonable estimates for low- and moderate-transmission, var coding allows more robust estimates especially under high-transmission. Defining the most suitable approach to estimate MOI based on local transmission intensity is highly recommended for hyper-diverse pathogens such as malaria.

Obesity genes working together: The biological causes of obesity are not well understood. Sobreira et al. examined the chromatin interactions between key genes in a locus known for its associations with obesity in human patients. In addition to directly interrogating the connections between these genes and examining the mechanisms that regulate their activity, the authors used mouse models to study the target genes' effects on both adipose tissue and brain cells that play a role in regulating dietary preferences. Science , abf1008, this issue p. 1085