Genomic and mathematical modeling approaches to understanding infectious disease dynamics

Mathematical modeling and genomic approaches are valuable tools for understanding infectious disease transmission dynamics. These methods facilitate fine-scale infectious disease surveillance and data-driven disease control strategies. For example, population genomic data from pathogens can reveal connectivity patterns, highlighting routes of transmission and pointing toward potential disease control measures. This dissertation aimed to evaluate seasonal spatial repellent (SR) use, investigate malaria transmission dynamics, and develop a broad pathogen screening assay utilizing modeling and genomic methods. First, I employed an agent-based malaria transmission model to assess the impact of 40 seasonal SR deployment schedules on malaria transmission in a high-transmission setting in western Kenya. I determined that year round, maximum coverage SR use had the greatest impact on reducing Plasmodium falciparum infection numbers. I also discovered that well-timed six-month SR deployments had nearly as much impact on reducing infections and averted more infections per product, suggesting a resource-efficient SR deployment option. Second, I investigated malaria transmission among indigenous groups in pre-elimination Bangladesh by amplicon sequencing 42 samples containing P. falciparum and P. vivax. Infections were highly clustered in two groups and showed high diversity with no population structure, indicating sustained transmission throughout the region. Third, I assessed malaria transmission in high-transmission Ethiopia by screening 661 clinical samples and amplicon sequencing 198 samples containing P. falciparum. I discovered that infections were diverse and did not cluster by site, although there was moderate population structure. Decreasing qPCR positivity rate, polyclonality, and parasite diversity between timepoints indicated that transmission intensity may have decreased over time in one site. Fourth, I applied amplicon sequencing methods to develop a novel broadly targeted next-generation sequencing assay and used it to screen 110 clinical samples from Ghana for a variety of pathogens. I detected a variety of pathogens: bacteria, a virus, and eukaryotic parasites. I also identified challenges to be addressed through further assay optimization. In conclusion, my work contributes to our understanding of malaria transmission and control measures and produced a novel assay for broad pathogen diagnosis. My findings can support the development of improved infectious disease surveillance and control strategies.