Inferring the transmission intensity of malaria using genetic data

Despite a recent decline in prevalence, malaria still incurs a substantial burden on global health. Further progress towards elimination will require developing control strategies that are guided by detailed epidemiological surveillance. Yet, many existing surveillance approaches – including estimating parasite prevalence with community-level surveys, or tracking resistance with therapeutic efficacy studies -- are time consuming and labour intensive. There is an interest in harnessing the increasing availability of P. falciparum genetic data for surveillance, but several challenges remain outstanding. First, the relationships between genetic and epidemiological processes are poorly understood, hindering epidemiological inference from genetic data. Second, the ubiquity of mixed infections complicates analysis of malaria genetic data. Third, how the collection of genetic data can be efficiently integrated into malaria control is still being resolved. In this thesis, we first elucidate relationships between genetic diversity statistics and epidemiological parameters affecting parasite prevalence, by developing an agent-based simulation that includes a model of parasite DNA. We find many genetic diversity statistics are correlated with parasite prevalence, but that the strength and timelines of these relationships varies. In particular, statistics related to mixed infections are strongly correlated with, and rapidly responsive to, changes in parasite prevalence. Second, we leverage identity-by-descent patterns present within mixed infections to make inferences about the microepidemiology of malaria. We separate mixed infections comprised of two strains into those generated by a single mosquito bite (co-infection) and those generated by two mosquito bites (super-infection). Applying our approach to the Pf3k data resource, we find evidence for serial co-infection across several hosts, and show that the regional rate of serial co-infection is related to parasite prevalence. Finally, we develop an approach to genetic data collection that can be deployed in malaria-endemic countries. Using nanopore sequencing and working with local scientists, we infer the drug resistance status of ten samples from Zambia, in-country and in two days. In addition, we present evidence suggesting the approach could be used to identify mixed infections and infer their complexity. We end by discussing what statistical modelling approaches are most likely to permit parasite prevalence inference in the future, and outline some of the challenges associated with sequencing in the malaria-endemic world.