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.数学建模与基因组学方法是解析传染病传播动态的重要工具。此类方法可助力精细化传染病监测，以及制定数据驱动的疾病防控策略。例如，病原体群体基因组数据能够揭示传播连通模式，明确传播路径并指向潜在的疾病防控手段。 本学位论文旨在评估季节性空间驱避剂（spatial repellent, SR）的使用效果，解析疟疾传播动态，并结合建模与基因组学方法开发一款广谱病原体筛查检测方法。 其一，本研究采用基于智能体的疟疾传播模型（agent-based malaria transmission model），评估了40种季节性空间驱避剂部署方案对肯尼亚西部高疟疾传播地区的防控效果。研究发现，全年全覆盖使用空间驱避剂可最大程度降低恶性疟原虫（Plasmodium falciparum）感染人数；同时发现，时机恰当的6个月周期驱避剂部署方案，其感染防控效果可与全年方案媲美，且单位产品可规避更多感染，提示这是一种资源利用效率更高的驱避剂部署策略。 其二，本研究针对孟加拉国疟疾预消除阶段的原住民群体疟疾传播展开研究，对42份携带恶性疟原虫与间日疟原虫（P. vivax）的样本进行扩增子测序。结果显示，感染病例在两个群体中高度聚集，且具有较高的遗传多样性，未检测到群体遗传结构，表明该区域疟疾仍处于持续传播状态。 其三，本研究通过筛查661份临床样本，并对198份携带恶性疟原虫的样本进行扩增子测序，评估了埃塞俄比亚高传播地区的疟疾传播情况。研究发现，感染病例具有较高遗传多样性，未按采样位点聚集，但存在中等程度的群体遗传结构。不同时间点的定量聚合酶链式反应（quantitative polymerase chain reaction, qPCR）阳性率、寄生虫多克隆性与遗传多样性均有所下降，提示某一采样点的传播强度随时间有所降低。 其四，本研究应用扩增子测序技术开发了一款新型广谱靶向下一代测序（next-generation sequencing）检测方法，并利用该方法对加纳的110份临床样本进行了多种病原体筛查。本次检测检出了多种病原体：细菌、一种病毒以及真核寄生虫；同时也明确了该检测方法尚需通过进一步优化解决的问题。 综上，本研究深化了人们对疟疾传播与防控手段的认知，开发了一款新型广谱病原体诊断检测方法。本研究结果可为优化传染病监测与防控策略提供支撑。