Genomic and geospatial analyses of malaria parasite migration to inform elimination

项目摘要 针对大湄公河次区域出现的多重抗药性恶性疟原虫， 世界卫生组织正在与当地合作伙伴合作，以彻底消除这一地理区域的疟疾， 到2030年的区域。该区域消灭疟疾的努力已导致疟疾病例数量大幅减少 和死亡然而，随着物种组成的变化， 从恶性疟原虫到间日疟原虫（更难消灭的物种），疟疾负担变得更加严重。 集中在边境地区，那里人口和蚊子病媒频繁流动， 边界以及在不同国家之间进行监视和分配资源的困难， 挑战淘汰。关于疟疾风险驱动因素的当地信息对于优先考虑 资源和优化消除疟疾的战略，特别是在边境地区。寄生虫的估计 移徙对划分疟疾风险很重要。人口基因组学方法开始用于 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 在区域地理尺度上，和/或仅使用地理空间数据， 解释。在这里，我们提出了一种方法，显式地建模基因组数据中的空间结构， 了解寄生虫在沿着北北方边界出现耐药性的地区的迁移模式， 柬埔寨与泰国。这项工作将在两个目标中完成。首先，我们将估计当地人口 恶性疟原虫和间日疟原虫在北方两侧密集采样区的结构和迁移 柬埔寨与泰国接壤。为了实现这一目标，我们将生成P的全基因组序列数据。 恶性疟原虫和间日疟原虫，并利用基于罕见变异的估计有效迁移表面（EEMS）， 同一性的下降，以推断恶性疟原虫和间日疟原虫种群之间的连接不同的研究地点。 其次，我们将估计当地的人类旅行模式及其与寄生虫迁移轮廓的关联 目标1为了实现这一目标，我们将开发一个在空间和时间上都是本地旅游网络的模型， 在村庄一级明确，并说明该区域影响人类活动的关键地理空间特征， 流动和有效移徙。估计的当地人类旅行模式与寄生虫之间的关联 将对移民模式进行评估，并将有助于确定旅游网络中重合的部分 具有高寄生虫迁移的区域，可用于确定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运动的贡献， 人口群体的寄生虫迁移的空间格局，并将提供一个框架，以确定具体的 有针对性的干预措施的地理区域，可根据其他疟疾流行地区进行调整， 传输的中间层。

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.