Abstract
Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.
Highlights
Molecular tools are increasingly being used to understand the transmission histories and phylogenies of infectious pathogens (Hall et al 2015)
After excluding single-nucleotide polymorphism (SNP) loci with >20% missing data and subsequently removing samples with >25% missing SNP data from further analysis, the complexity of infection (COI) was estimated for 2,419 samples from 95 primary schools in Western Kenya (1,363 from Nyanza province and 1,056 from Western province) and 584 samples from representative cross-sectional household surveys in three subcounties in Uganda (462 from Nagongera in Tororo District, 74 from Kihihi in Kanungu District, and 48 from Walukuba in Jinja District)
The model was extended to include individual mosquitoes, enabling parasite populations and their genotypes to be tracked throughout the full lifecycle, enabling the potential formation of multiple oocysts from an infectious event and multiple genetically distinct sporozoites to be onwardly transmitted
Summary
Molecular tools are increasingly being used to understand the transmission histories and phylogenies of infectious pathogens (Hall et al 2015). It is possible to estimate the historic prevalence of infection directly from molecular data, even in organisms with relatively complex lifecycles (Volz et al 2009). These tools typically rely on pathogens having an elevated mutation rate and not undergoing sexual recombination, which allows for the application of coalescent theory (Grenfell et al 2004).
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