Genomic and geospatial analyses of malaria parasite migration to inform elimination

项目总结 为应对大湄公河次区域出现耐多药恶性疟原虫， 世界卫生组织正在与当地合作伙伴合作，从这个地理位置完全消除疟疾 到2030年。该地区消除疟疾的努力已导致疟疾病例大幅减少。 和死亡。然而，随着物种组成的变化，消除这种现象将变得越来越困难 从恶性疟原虫到间日疟原虫(更难消灭的物种)，疟疾负担变得更大 集中在边境地区，那里的人口和蚊子媒介经常流动 边界和在不同国家之间进行监视和分配资源的困难使得 淘汰赛具有挑战性。关于疟疾风险驱动因素的当地信息对于确定优先顺序将是重要的 消除疟疾的资源和优化战略，特别是在边境地区。对寄生虫的估计 移徙在划分疟疾风险方面很重要。种群基因组学方法正开始被用于 了解寄生虫种群之间的联系；然而，这些研究中的许多主要集中在 在区域地理比例尺上和/或仅使用地理空间数据使后继地理 解读。在这里，我们提出了一种显式建模基因组数据中的空间结构的方法 了解新药耐药地区的寄生虫迁移模式 柬埔寨和泰国。这项工作将通过两个目标来完成。首先，我们将估计当地的人口 北方两侧高密度采样区间日疟原虫和恶性疟原虫的结构和迁移 柬埔寨与泰国接壤的边界。为了实现这一目标，我们将为P. 恶性疟原虫和间日疟原虫，并利用基于稀有变异和 根据血统推断恶性疟原虫和间日疟原虫种群在不同研究地点之间的连通性。 其次，我们将估计当地的人类旅行模式及其与寄生虫迁徙等高线的关联 从目标1开始。为了实现这一目标，我们将开发一个时空上的本地旅游网络模型 在村庄级别明确，这是该区域影响人类的关键地理空间要素 流动和有效的迁徙。估计的当地人类旅行模式与寄生虫之间的联系 将对移徙模式进行评估，并将有助于确定旅行网络中符合的部分 具有高寄生虫迁移率的区域，可用于定义用于目标消除的地理单元 干预措施。如果成功，拟议的研究将阐明当地人口流动的贡献 人口群体对寄生虫迁移的空间模式的了解，并将提供一个框架，以确定特定的 有针对性干预的地理区域，可适用于其他疟疾流行地区 中间级别的传播。

PROJECT SUMMARY In response to the emergence of multi-drug-resistant Plasmodium falciparum in the Greater Mekong Subregion, the World Health Organization is working with local partners to completely eliminate malaria from this geographic region by 2030. Elimination efforts in the region have led to drastic reductions in the number of malaria cases and deaths. However, elimination will become increasingly difficult to achieve as the species composition shifts from P. falciparum to P. vivax (the more difficult species to eliminate), and the malaria burden becomes more concentrated in border areas, where frequent movement of human populations and mosquito vectors across borders and the difficulties of conducting surveillance and allocating resources between different countries make elimination challenging. Local information about factors driving malaria risk will be important for prioritizing resources and optimizing strategies for malaria elimination, particularly in border areas. Estimates of parasite migration are important in stratifying malaria risk. Population genomics approaches are beginning to be used to understand connectivity between parasite populations; however, many of these studies have focused primarily on regional geographic scales and/or have only used geospatial data to make post hoc geographic interpretations. Here, we propose an approach that explicitly models the spatial structure in genomic data to understand parasite migration patterns in an area of emerging drug resistance along the northern border of Cambodia with Thailand. The work will be accomplished in two aims. First, we will estimate the local population structure and migration of P. falciparum and P. vivax in an area of dense sampling on either side of the northern border of Cambodia with Thailand. To achieve this aim, we will generate whole-genome sequence data for P. falciparum and P. vivax and utilize estimated effective migration surfaces (EEMS) based on rare variation and identity-by-descent to infer connectivity of P. falciparum and P. vivax populations between different study sites. Second, we will estimate local human travel patterns and their association with the parasite migration contours from Aim 1. To achieve this aim, we will develop a model of local travel networks that is spatially and temporally explicit at the village level and that accounts for key geospatial features in the region that impact human movement and effective migration. The association between estimated local human travel patterns and parasite migration patterns will be assessed and will facilitate identification of segments of the travel network that coincide with regions of high parasite migration that can be used to define geographical units for targeting elimination interventions. If successful, the proposed research will illuminate the contribution of movement by local population groups to spatial patterns of parasite migration and will provide a framework to identify specific geographic areas for targeted intervention, which can be adapted to other malaria-endemic areas with intermediate levels of transmission.