Temporal strain structure and response to interventions in a high endemicity region of Plasmodium falciparum

恶性疟原虫高流行区的时间应变结构和对干预措施的反应

• 批准号: 9980295
• 负责人: Karen Patricia Day
• 金额: $ 61.28万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-19 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980295
• 关键词: Address; Africa South of the Sahara; Age; Antigenic Diversity; Antigens; Area; Blood; Chemoprevention; Clinical; Complex; Computer Models; Data; Data Analyses; Effectiveness; Epidemiology; Evolution; Family; Gene Family; Ghana; Immune; Infection; Intervention; Laboratories; Malaria; Modeling; Molecular; Monitor; Multigene Family; Parasites; Pathway Analysis; Plasmodium falciparum; Population; Recombinants; Recording of previous events; Research; Residual state; Sampling; Structure; System; Time; Work; base; epidemiologic data; global health; indexing; molecular marker; pathogen; population genetic structure; resilience; response; theories; tool; transmission process

Malaria control and elimination in areas of high transmission in sub-Saharan Africa present a significant challenge to global health. A large fraction of the population across all ages in these areas harbor Plasmodium falciparum without clinical manifestations, providing a vast reservoir of infection for transmission. This asymptomatic reservoir is sustained by the enormous antigenic diversity of the parasite. Thus, the challenge for hyperendemic regions requires that the malaria field comes to terms with such diversity, studying it as a complex adaptive system. This study addresses the two-way interaction between epidemiology and P. falciparum diversity from the perspective of the multigene and highly recombinant family known as var, which encodes for the major antigen of the blood stage of infection. This project combines theory with field and laboratory work lo generate new understanding of the diverse transmission reservoir of P. falciparum and its resilience to elimination. The first aim is the longitudinal deep sampling of this reservoir in the Bongo District, Ghana following two control interventions (i.e. indoor residual spraying and seasonal malaria chemoprevention). On the basis of age-stratified serial cross-sectional data, this study will assess how informative the var system is to monitor this reservoir under conditions of the control interventions, compared to traditional malariometric indices and neutral molecular markers. The second aim develops strain theory based on a stochastic agent-based model that tracks the history of infection of each host and the evolutionary change of the parasite. Pathogen population structure over time and responses of the var system to intervention are investigated in ways that inform both molecular data analyses and control efforts. The third aim develops a transmission model of intermediate complexity that can be fitted to routine epidemiological data but still incorporates the major effects of parasite diversity on epidemiology. In particular, the existence of a threshold in transmission intensity that impacts parasite antigenic diversity will be investigated. Contributions include computational models at the interface of epidemiology and evolution, and network analyses of population genetic structure applicable to other multigene families of P. falciparum, as well as to other pathogens whose immune escape relies on highly-recombinant gene families.

在撒哈拉以南非洲高传播地区控制和消灭疟疾是对全球卫生的重大挑战。这些地区所有年龄段的大部分人口携带恶性疟原虫而没有临床表现，为传播提供了巨大的感染库。这种无症状的宿主是由寄生虫的巨大抗原多样性维持的。因此，高流行地区面临的挑战要求疟疾领域接受这种多样性，将其作为一个复杂的适应系统进行研究。本研究从多基因和高度重组家族（称为var）的角度探讨了流行病学和恶性疟原虫多样性之间的双向相互作用，var编码感染血液阶段的主要抗原。该项目将理论与现场和实验室工作相结合，以产生对恶性疟原虫的多样性传播库及其消除恢复力的新认识。第一个目标是在加纳邦戈区的这一水库中进行纵向深度采样，采取两种控制措施（即室内滞留喷洒和季节性疟疾化学预防）。基于年龄分层的连续横断面数据，本研究将评估与传统疟疾指数和中性分子标记物相比，var系统在控制干预条件下监测该水库的信息量。第二个目标是发展应变理论的基础上随机代理为基础的模型，跟踪历史的感染每个主机和寄生虫的进化变化。病原体种群结构随着时间的推移和反应的var系统干预的方式，通知分子数据分析和控制工作进行了调查。第三个目标是开发一个中等复杂性的传播模型，该模型可以拟合常规流行病学数据，但仍然包含寄生虫多样性对流行病学的主要影响。特别是，传播强度的阈值，影响寄生虫抗原多样性的存在将进行调查。贡献包括在流行病学和进化的接口计算模型，适用于其他多基因家族的恶性疟原虫，以及其他病原体的免疫逃逸依赖于高度重组基因家族的人口遗传结构的网络分析。