Genomic and geospatial analyses of malaria parasite migration to inform elimination

疟疾寄生虫迁移的基因组和地理空间分析为消除提供信息

• 批准号: 10577799
• 负责人: SHANNON Takala Harrison
• 金额: $ 73.31万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577799
• 关键词: Admixture; Affect; Area; Artemisinins; Automobile Driving; Buffers; Cambodia; Cessation of life; Combined Modality Therapy; Country; Data; Drug resistance; Environmental Risk Factor; Exhibits; Genomic approach; Genomics; Geographic Locations; Geography; Haplotypes; Human; Infection; Intervention; Malaria; Maps; Methods; Modeling; Movement; Multi-Drug Resistance; Occupations; Outcome; Parasites; Pattern; Pharmaceutical Preparations; Plasmodium falciparum; Plasmodium vivax; Population; Population Group; Property; Recording of previous events; Relapse; Research; Resource Allocation; Resources; Risk; Sampling; Side; Site; Source; Structure; Surface; Thailand; Travel; Validation; Variant; Work; World Health Organization; falls; genome-wide; genomic data; identity by descent; migration; preservation; prevent; programs; resistant Plasmodium falciparum; response; transmission process; vector mosquito; whole genome

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.

项目摘要 响应于大湄公河区域中多药耐药疟原虫的出现， 世界卫生组织正在与当地合作伙伴合作，完全消除了这个地理的疟疾 到2030年，该地区的消除努力导致疟疾病例数量急剧减少 和死亡。但是，随着物种组成的变化，消除将变得越来越难以实现 从恶性疟原虫到杜瓦人（消除较困难的物种），疟疾负担变得更多 集中在边境地区，在这里，人口和蚊子向量频繁移动 边界和进行监视和分配资源之间的困难 消除具有挑战性。有关驱动疟疾风险因素的本地信息对于确定优先级非常重要 资源并优化消除疟疾的策略，尤其是在边境地区。寄生虫的估计 迁移对于分层疟疾风险很重要。人口基因组学方法开始使用 了解寄生虫种群之间的连通性；但是，其中许多研究主要集中在 在区域地理尺度上和/或仅使用地理空间数据来使事后地理 解释。在这里，我们提出了一种将基因组数据中的空间结构显式建模到的方法 了解沿着北部边界的新兴耐药性地区的寄生虫迁移模式 柬埔寨与泰国。这项工作将以两个目标完成。首先，我们将估计当地人口 北部两侧的致密采样区域中恶性疟原虫和Vivax的结构和迁移 柬埔寨与泰国边界。为了实现这一目标，我们将生成P。 恶性菌和疟原虫，并基于罕见变化和使用估计的有效迁移表面（EEM） 逐个发现，以推断出不同研究地点之间恶性疟原虫和维瓦克斯种群的连通性。 其次，我们将估计当地的人类旅行模式及其与寄生虫迁移轮廓的关联 从AIM 1开始。为了实现此目标，我们将开发出在空间和时间上的本地旅行网络模型 在乡村层面上明确说明了该地区影响人类的关键地理空间特征 运动和有效的迁移。估计的当地人类旅行模式与寄生虫之间的关联 将评估迁移模式，并有助于识别一致的旅行网络的细分市场 与可用于定义靶向消除的地理单位的高寄生虫迁移区域 干预措施。如果成功，拟议的研究将阐明当地运动的贡献 人口群体到寄生虫迁移的空间模式，并将提供一个框架以识别特定 有针对性干预的地理区域，可以适应其他疟疾流行区域 中等水平的传播水平。