Abstract
There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (∼months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology.
Highlights
It is widely accepted that relationships exist between the regional epidemiology of malaria and the genetic diversity of local parasite populations
We find that several genetic diversity statistics are correlated with prevalence, to differing degrees, and over different time scales
Statistics related to mixed infection are robustly and rapidly responsive to changes in prevalence, suggesting they may be a useful focal point for the development of malaria surveillance methods that harness genetic data
Summary
It is widely accepted that relationships exist between the regional epidemiology of malaria and the genetic diversity of local parasite populations. From a modelling perspective, exploring these relationships would require that both genetic processes (including mutation, drift and meiosis) and epidemiological ones (including the transmission dynamics and life cycle of malaria) are combined into a single framework. At present such integrated models are rare, yet without them, parasite genetic data will be under-utilized as a resource for malaria surveillance. A means to either estimate or improve existing estimates of transmission intensity with genetic data would be valuable
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