Satellite and geospatial dynamic modeling of malaria risk

缅甸是该地区疟疾负担最重的国家， 消灭东南亚的疟疾疟疾流行率、发病率和传播风险是 这两个领域的差异很大，而且没有完全重叠， 消除干预措施。加强对疟疾时空风险认识的研究 动力学和传播途径将改善消除的前景。项目3地址 研究领域B，传播，侧重于两个主要的传播途径：人类和 蚊子一套地理空间技术将被引入到模型矢量和寄生虫 存在/丰富的空间和时间，并建立人类流动性和 疟疾在多个尺度上的传播。第一个目标是建立空间上明确的关系 沿着一个多季节的气候变化， 时间梯度，以便能够在研究地点建立预测疟疾风险系统， 缅甸及其与中国和孟加拉国边境附近。这将通过以下方式实现： 应用高时间密度的蚊子丰度实地采样，DNA- 基于蚊子、恶性疟原虫和间日疟原虫存在/丰度的物种形成 (from项目1）、卫星衍生环境参数和随机森林分析框架。 将开发各种工具，预测恶性疟和间日疟负担， 环境条件模拟结果将与项目1的结果进行比较 血清学分析，量化研究参与者暴露于特定地点的寄生虫亚群， 项目2基因组流行病学研究中定义的人群。目标2将确定空间驱动因素 疟疾传播以及人类流动模式与风险之间的关系。空间 网络建模方法将被用来研究人口流动在村庄一级的基础上， 通过旅行历史和交通分析报告的个人日常活动。长距离 通过旅行历史和其他旅行数据（如铁路、水路）报告的区域一级的流动 旅行，航空公司）将用于评估区域传播模式以及人口如何 变得容易受到感染。人类的流动模式也将与遗传信息联系起来 从项目2关于寄生虫种群结构和运动，以评估之间的关系 人口流动和寄生虫迁移。与Mapping Core一起，该项目将 提高国家疟疾控制计划的能力， 精确地-因此更有效和高效地-比现有的工具和 接近。

With the heaviest malaria burden in the region, Myanmar is central to the newly launched campaign to eliminate malaria from Southeast Asia. Malaria prevalence, incidence, and transmission risk are both highly heterogeneous and do not fully overlap, posing serious challenges for targeting elimination interventions. Research that enhances the understanding of space-time malaria risk dynamics and transmission pathways will improve elimination prospects. Project 3 addresses Research Area B, Transmission, focusing on the two major transmission pathways: human and mosquito. A suite of geospatial techniques will be introduced to model vector and parasite presence/abundance in space and time and establish relationships between human mobility and malaria transmission at multiple scales. The first aim will establish spatially explicit relationships between environmental conditions, vector abundance, and malaria burden along a multi-seasonal temporal gradient to enable the development of a predictive malaria risk system at study sites in Myanmar and near its borders with China and Bangladesh. This will be accomplished through applying a combination of field sampling of mosquito abundance at high temporal density, DNA- based speciation of mosquitos, Plasmodium falciparum and Plasmodium vivax presence/abundance (from Project 1), satellite-derived environmental parameters, and random forest analytical framework. Tools will be developed to forecast falciparum and vivax malaria burden as a function of environmental conditions. The modeling results will be compared with the outcomes of Project 1 serological analyses that quantify the exposure of study participants to site-specific parasite sub- populations as defined in Project 2 genomic epidemiology studies. Aim 2 will identify spatial drivers for malaria transmission and the relationships between patterns of human mobility and risk. Spatial network modeling approaches will be used to study human mobility at the village level based on the daily activities of individuals as reported through travel histories and traffic analyses. Longer-distance movements at a regional level, reported through travel histories and other travel data (e.g. rail, water travel, airways) will be used to assess regional transmission patterns and how populations may become vulnerable to infection. Human mobility patterns will also be linked to genetic information from Project 2 about parasite population structure and movement to assess the relationships between population movements and parasite migration. Together with the Mapping Core, this project will improve the ability of National Malaria Control Programs to target interventions much more precisely—and therefore more effectively and efficiently—than is possible with current tools and approaches.