To study the effects of malaria-control interventions on parasite population genomics, we examined a set of 1,007 samples of the malaria parasite Plasmodium falciparum collected in Thiès, Senegal between 2006 and 2013. The parasite samples were genotyped using a molecular barcode of 24 SNPs. About 35% of the samples grouped into subsets with identical barcodes, varying in size by year and sometimes persisting across years. The barcodes also formed networks of related groups. Analysis of 164 completely sequenced parasites revealed extensive sharing of genomic regions. In at least two cases we found first-generation recombinant offspring of parents whose genomes are similar or identical to genomes also present in the sample. An epidemiological model that tracks parasite genotypes can reproduce the observed pattern of barcode subsets. Quantification of likelihoods in the model strongly suggests a reduction of transmission from 2006–2010 with a significant rebound in 2012–2013. The reduced transmission and rebound were confirmed directly by incidence data from Thiès. These findings imply that intensive intervention to control malaria results in rapid and dramatic changes in parasite population genomics. The results also suggest that genomics combined with epidemiological modeling may afford prompt, continuous, and cost-effective tracking of progress toward malaria elimination.
Our results do establish a foundational link between observations of parasite population genomics and epidemiological models that incorporate genetic mechanisms. Combining genomic observations with epidemiological modeling provides a powerful and complementary tool for elucidating population-level details of transmission in low-prevalence settings from a small sample of parasite genomes. In particular, modeling parasite genetics allows one to take a collection of many different types of measurement—effective population size, multilocus linkage disequilibrium, heterozygosity of mixed infections, complexity of infection—and from these measurements form a quantitative assessment of the transmission dynamics most consistent with the full information available.