Community-serving research addressing climate change impacts on vector-borne diseases.

The impacts of climate change on vector-borne diseases are uneven across human populations. This pattern reflects the effect of changing environments on the biology of transmission, which is also modulated by social and other inequities. These disparities are also linked to research outcomes that could be translated into tools for transmission reduction, but are not necessarily actionable in the communities where transmission occurs. The transmission of vector-borne diseases could be averted by developing research that is both hypothesis-driven and community-serving for populations affected by climate change, where local communities interact as equal partners with scientists, developing and implementing research projects with the aim of improving community health. In this Personal View, we share five principles that have guided our research practice to serve the needs of communities affected by vector-borne diseases.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria.

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.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study.

BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.

An immune memory-structured SIS epidemiological model for hyperdiverse pathogens.

A hyperdiverse class of pathogens of humans and wildlife, including the malaria parasite Plasmodium falciparum, relies on multigene families to encode antigenic variation. As a result, high (asymptomatic) prevalence is observed despite high immunity in local populations under high-transmission settings. The vast diversity of "strains" and genes encoding this variation challenges the application of established models for the population dynamics of such infectious diseases. Agent-based models have been formulated to address theory on strain coexistence and structure, but their complexity can limit application to gain insights into population dynamics. Motivated by P. falciparum malaria, we develop an alternative formulation in the form of a structured susceptible-infected-susceptible population model in continuous time, where individuals are classified not only by age, as is standard, but also by the diversity of parasites they have been exposed to and retain in their specific immune memory. We analyze the population dynamics and bifurcation structure of this system of partial-differential equations, showing the existence of alternative steady states and an associated tipping point with transmission intensity. We attribute the critical transition to the positive feedback between parasite genetic diversity and force of infection. Basins of attraction show that intervention must drastically reduce diversity to prevent a rebound to high infection levels. Results emphasize the importance of explicitly considering pathogen diversity and associated specific immune memory in the population dynamics of hyperdiverse epidemiological systems. This statement is discussed in a more general context for ecological competition systems with hyperdiverse trait spaces.

Comparison of molecular surveillance methods to assess changes in the population genetics of Plasmodium falciparum in high transmission.

A major motivation for developing molecular methods for malaria surveillance is to measure the impact of control interventions on the population genetics of Plasmodium falciparum as a potential marker of progress towards elimination. Here we assess three established methods (i) single nucleotide polymorphism (SNP) barcoding (panel of 24-biallelic loci), (ii) microsatellite genotyping (panel of 12-multiallelic loci), and (iii) varcoding (fingerprinting var gene diversity, akin to microhaplotyping) to identify changes in parasite population genetics in response to a short-term indoor residual spraying (IRS) intervention. Typical of high seasonal transmission in Africa, multiclonal infections were found in 82.3% (median 3; range 1-18) and 57.8% (median 2; range 1-12) of asymptomatic individuals pre- and post-IRS, respectively, in Bongo District, Ghana. Since directly phasing multilocus haplotypes for population genetic analysis is not possible for biallelic SNPs and microsatellites, we chose ~200 low-complexity infections biased to single and double clone infections for analysis. Each genotyping method presented a different pattern of change in diversity and population structure as a consequence of variability in usable data and the relative polymorphism of the molecular markers (i.e., SNPs < microsatellites < var). Varcoding and microsatellite genotyping showed the overall failure of the IRS intervention to significantly change the population structure from pre-IRS characteristics (i.e., many diverse genomes of low genetic similarity). The 24-SNP barcode provided limited information for analysis, largely due to the biallelic nature of SNPs leading to a high proportion of double-allele calls and a view of more isolate relatedness compared to microsatellites and varcoding. Relative performance, suitability, and cost-effectiveness of the methods relevant to sample size and local malaria elimination in high-transmission endemic areas are discussed.

A paradoxical population structure of var DBLα types in Africa.

The var multigene family encodes the P. falciparum erythrocyte membrane protein 1 (PfEMP1), which is important in host-parasite interaction as a virulence factor and major surface antigen of the blood stages of the parasite, responsible for maintaining chronic infection. Whilst important in the biology of P. falciparum, these genes (50 to 60 genes per parasite genome) are routinely excluded from whole genome analyses due to their hyper-diversity, achieved primarily through recombination. The PfEMP1 head structure almost always consists of a DBLα-CIDR tandem. Categorised into different groups (upsA, upsB, upsC), different head structures have been associated with different ligand-binding affinities and disease severities. We study how conserved individual DBLα types are at the country, regional, and local scales in Sub-Saharan Africa. Using publicly-available sequence datasets and a novel ups classification algorithm, cUps, we performed an in silico exploration of DBLα conservation through time and space in Africa. In all three ups groups, the population structure of DBLα types in Africa consists of variants occurring at rare, low, moderate, and high frequencies. Non-rare variants were found to be temporally stable in a local area in endemic Ghana. When inspected across different geographical scales, we report different levels of conservation; while some DBLα types were consistently found in high frequencies in multiple African countries, others were conserved only locally, signifying local preservation of specific types. Underlying this population pattern is the composition of DBLα types within each isolate DBLα repertoire, revealed to also consist of a mix of types found at rare, low, moderate, and high frequencies in the population. We further discuss the adaptive forces and balancing selection, including host genetic factors, potentially shaping the evolution and diversity of DBLα types in Africa.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Humidity - The overlooked variable in the thermal biology of mosquito-borne disease.

Vector-borne diseases cause significant financial and human loss, with billions of dollars spent on control. Arthropod vectors experience a complex suite of environmental factors that affect fitness, population growth and species interactions across multiple spatial and temporal scales. Temperature and water availability are two of the most important abiotic variables influencing their distributions and abundances. While extensive research on temperature exists, the influence of humidity on vector and pathogen parameters affecting disease dynamics are less understood. Humidity is often underemphasized, and when considered, is often treated as independent of temperature even though desiccation likely contributes to declines in trait performance at warmer temperatures. This Perspectives explores how humidity shapes the thermal performance of mosquito-borne pathogen transmission. We summarize what is known about its effects and propose a conceptual model for how temperature and humidity interact to shape the range of temperatures across which mosquitoes persist and achieve high transmission potential. We discuss how failing to account for these interactions hinders efforts to forecast transmission dynamics and respond to epidemics of mosquito-borne infections. We outline future research areas that will ground the effects of humidity on the thermal biology of pathogen transmission in a theoretical and empirical framework to improve spatial and temporal prediction of vector-borne pathogen transmission.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions.

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 (MOIvar), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOIvar 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 MOIvar 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, MOIvar, and population size were observed in ~2,000 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.

Balancing economic and epidemiological interventions in the early stages of pathogen emergence.

The global pandemic of COVID-19 has underlined the need for more coordinated responses to emergent pathogens. These responses need to balance epidemic control in ways that concomitantly minimize hospitalizations and economic damages. We develop a hybrid economic-epidemiological modeling framework that allows us to examine the interaction between economic and health impacts over the first period of pathogen emergence when lockdown, testing, and isolation are the only means of containing the epidemic. This operational mathematical setting allows us to determine the optimal policy interventions under a variety of scenarios that might prevail in the first period of a large-scale epidemic outbreak. Combining testing with isolation emerges as a more effective policy than lockdowns, substantially reducing deaths and the number of infected hosts, at lower economic cost. If a lockdown is put in place early in the course of the epidemic, it always dominates the "laissez-faire" policy of doing nothing.

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections.

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 varcoding 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 varcoding 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. Regardless of transmission intensity, results for THE REAL McCOIL indicate that an inaccurate tail at high MOI values is generated, and that at high transmission, an apparently reasonable estimated MOI distribution can arise from some degree of compensation between overestimation and underestimation. 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.

Indoor residual spraying with a non-pyrethroid insecticide reduces the reservoir of Plasmodium falciparum in a high-transmission area in northern Ghana.

High-malaria burden countries in sub-Saharan Africa are shifting from malaria control towards elimination. Hence, there is need to gain a contemporary understanding of how indoor residual spraying (IRS) with non-pyrethroid insecticides when combined with long-lasting insecticidal nets (LLINs) impregnated with pyrethroid insecticides, contribute to the efforts of National Malaria Control Programmes to interrupt transmission and reduce the reservoir of Plasmodium falciparum infections across all ages. Using an interrupted time-series study design, four age-stratified malariometric surveys, each of ~2,000 participants, were undertaken pre- and post-IRS in Bongo District, Ghana. Following the application of three-rounds of IRS, P. falciparum transmission intensity declined, as measured by a >90% reduction in the monthly entomological inoculation rate. This decline was accompanied by reductions in parasitological parameters, with participants of all ages being significantly less likely to harbor P. falciparum infections at the end of the wet season post-IRS (aOR = 0.22 [95% CI: 0.19-0.26], p-value < 0.001). In addition, multiplicity of infection (MOI var ) was measured using a parasite fingerprinting tool, designed to capture within-host genome diversity. At the end of the wet season post-IRS, the prevalence of multi-genome infections declined from 75.6% to 54.1%. This study demonstrates that in areas characterized by high seasonal malaria transmission, IRS in combination with LLINs can significantly reduce the reservoir of P. falciparum infection. Nonetheless despite this success, 41.6% of the population, especially older children and adolescents, still harboured multi-genome infections. Given the persistence of this diverse reservoir across all ages, these data highlight the importance of sustaining vector control in combination with targeted chemotherapy to move high-transmission settings towards pre-elimination. This study also points to the benefits of molecular surveillance to ensure that incremental achievements are not lost and that the goals advocated for in the WHO's High Burden to High Impact strategy are realized.

Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics.

The spread of dengue and other arboviruses constitutes an expanding global health threat. The extensive heterogeneity in population distribution and potential complexity of movement in megacities of low and middle-income countries challenges predictive modeling, even as its importance to disease spread is clearer than ever. Using surveillance data at fine resolution from Rio de Janeiro, we document a scale-invariant pattern in the size of successive epidemics following DENV4 emergence. Using surveillance data at fine resolution following the emergence of the DENV4 dengue serotype in Rio de Janeiro, we document a pattern in the size of successive epidemics that is invariant to the scale of spatial aggregation. This pattern emerges from the combined effect of herd immunity and seasonal transmission, and is strongly driven by variation in population density at sub-kilometer scales. It is apparent only when the landscape is stratified by population density and not by spatial proximity as has been common practice. Models that exploit this emergent simplicity should afford improved predictions of the local size of successive epidemic waves.

Age-specific patterns of DBLα var diversity can explain why residents of high malaria transmission areas remain susceptible to Plasmodium falciparum blood stage infection throughout life.

Immunity to Plasmodium falciparum is non-sterilising, thus individuals residing in malaria-endemic areas are at risk of infection throughout their lifetime. Here we seek to find a genomic epidemiological explanation for why residents of all ages harbour blood stage infections despite lifelong exposure to P. falciparum in areas of high transmission. We do this by exploring, for the first known time, the age-specific patterns of diversity of variant antigen encoding (var) genes in the reservoir of infection. Microscopic and submicroscopic P. falciparum infections were analysed at the end of the wet and dry seasons in 2012-2013 for a cohort of 1541 residents aged from 1 to 91 years in an area characterised by high seasonal malaria transmission in Ghana. By sequencing the near ubiquitous Duffy-binding-like alpha domain (DBLα) that encodes immunogenic domains, we defined var gene diversity in an estimated 1096 genomes detected in sequential wet and dry season sampling of this cohort. Unprecedented var (DBLα) diversity was observed in all ages with 42,399 unique var types detected. There was a high degree of maintenance of types between seasons (>40% seen more than once), with many of the same types, especially upsA, appearing multiple times in isolates from different individuals. Children and adolescents were found to be significant reservoirs of var DBLα diversity compared with adults. Var repertoires within individuals were highly variable, with children having more related var repertoires compared to adolescents and adults. Individuals of all ages harboured multiple genomes with var repertoires unrelated to those infecting other hosts. High turnover of parasites with diverse isolate var repertoires was also observed in all ages. These age-specific patterns are best explained by variant-specific immune selection. The observed level of var diversity for the population was then used to simulate the development of variant-specific immunity to the diverse var types under conservative assumptions. Simulations showed that the extent of observed var diversity with limited repertoire relatedness was sufficient to explain why adolescents and adults in this community remain susceptible to blood stage infection, even with multiple genomes.

The impact of indoor residual spraying on Plasmodium falciparum microsatellite variation in an area of high seasonal malaria transmission in Ghana, West Africa.

Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November-May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long-lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age-stratified cross-sectional surveys at the end of the wet seasons pre- and post-IRS. Three-rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p < .001). Despite these declines, population genetic analysis of the infection haplotypes revealed no dramatic changes with only a slight, but significant increase in genetic diversity (He : pre-IRS = 0.79 vs. post-IRS = 0.81, p = .048). Reduced relatedness of the parasite population (p < .001) was observed post-IRS, probably due to decreased opportunities for outcrossing. Spatiotemporal genetic differentiation between the pre- and post-IRS surveys (D = 0.0329 [95% CI: 0.0209 - 0.0473], p = .034) was identified. These data provide a genetic explanation for the resilience of P. falciparum to short-term IRS programmes in high-transmission settings in sub-Saharan Africa.

What happens when forests fall?

Combining spatial and temporal data is helping researchers to understand how deforestation influences the risk of malaria.

Malaria trends in Ethiopian highlands track the 2000 'slowdown' in global warming.

A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.

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 Rdiv; 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.

Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity.

The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.