Genomic and geospatial analyses of malaria parasite migration to inform elimination

项目概要 针对大湄公河次区域出现多重耐药性恶性疟原虫， 世界卫生组织正在与当地合作伙伴合作，以彻底消除该地区的疟疾 到 2030 年，该地区的疟疾病例数量大幅减少。 和死亡。然而，随着物种组成的变化，消除将变得越来越困难 从恶性疟原虫到间日疟原虫（更难消除的物种），疟疾负担变得更加严重 集中在边境地区，人口和蚊媒频繁流动 边界以及不同国家之间进行监视和分配资源的困难使得 消除具有挑战性。有关疟疾风险驱动因素的当地信息对于确定优先顺序非常重要 消除疟疾的资源和优化战略，特别是在边境地区。寄生虫的估计 移徙对于疟疾风险分层非常重要。群体基因组学方法开始用于 了解寄生虫种群之间的联系；然而，其中许多研究主要集中于 在区域地理尺度上和/或仅使用地理空间数据来制作事后地理 解释。在这里，我们提出了一种对基因组数据中的空间结构进行显式建模的方法 了解沿美国北部边境出现的耐药性地区的寄生虫迁移模式 柬埔寨与泰国。这项工作将实现两个目标。首先我们要估算一下当地的人口 北部两侧密集采样区恶性疟原虫和间日疟原虫的结构和迁移 柬埔寨与泰国边境。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和估计的有效迁移表面（EEMS） 通过血统来推断不同研究地点之间恶性疟原虫和间日疟原虫种群的连通性。 其次，我们将估计当地人类旅行模式及其与寄生虫迁移轮廓的关联 来自目标 1。为了实现这一目标，我们将开发一个在空间和时间上的本地旅行网络模型 明确在村庄一级，并解释了该地区影响人类的关键地理空间特征 流动和有效迁移。估计的当地人类旅行模式与寄生虫之间的关联 将评估移徙模式，并将有助于确定旅行网络中相一致的部分 寄生虫高迁移区域可用于定义目标消除的地理单位 干预措施。如果成功，拟议的研究将阐明当地运动的贡献 人口群体寄生虫迁移的空间模式，并将提供一个框架来确定具体的 进行有针对性的干预的地理区域，可适应其他疟疾流行地区 中等传输水平。

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.