Satellite and geospatial dynamic modeling of malaria risk

疟疾风险的卫星和地理空间动态建模

• 批准号: 9912077
• 负责人: Tatiana V Loboda
• 金额: $ 13.14万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912077
• 关键词: Address; Africa; Area; Automobile Driving; Bangladesh; Beds; Big Data; Car Phone; Cellular Phone; China; Computer Models; Culicidae; DNA; Data; Decision Support Systems; Development; Drug resistance; Ecosystem; Elements; Environmental Risk Factor; Epidemiology; Exposure to; Falciparum Malaria; Genomics; Goals; Habitats; Heritability; Human; Image Analysis; Imagery; Immunity; Incidence; Individual; Infection; Intervention; Knowledge; Link; Malaria; Measures; Meteorology; Modeling; Molecular; Monitor; Mosquito Control; Movement; Myanmar; Outcome; Parasites; Participant; Pathway interactions; Pattern; Planet Earth; Plasmodium falciparum; Plasmodium vivax; Play; Population; Population Distributions; Population Dynamics; Population Group; Prevalence; Recording of previous events; Records; Reporting; Research; Risk; Role; Route; Sampling; Serological; Site; Source; Southeastern Asia; Speed; Standardization; Structure; System; Techniques; Time; Transportation; Travel; Universal Coverage; Vector Ecology; Vivax Malaria; Water; Work; base; data modeling; density; epidemiological model; epidemiology study; genetic information; genomic epidemiology; improved; infection risk; insight; land use; malaria transmission; migration; network models; population movement; predictive modeling; programs; random forest; serological marker; support network; targeted treatment; tool; trait; transmission process; treatment strategy; vector

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.

缅甸是该地区疟疾负担最重的国家，是新发起的行动的核心 从东南亚消灭疟疾。疟疾的流行率、发病率和传播风险是 两者高度异质且不完全重叠，这对目标构成了严重挑战 排除性干预。加强对时空疟疾风险的理解的研究 动力学和传播途径将改善淘汰前景。项目3涉及 研究领域B，传播，侧重于两个主要的传播途径：人类和 蚊子。将引入一套地理空间技术来模拟病媒和寄生虫 在空间和时间上的存在/丰富性，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 环境条件、媒介丰度和疟疾负担之间的关系 时间梯度，以便能够在#年的研究地点开发预测疟疾风险系统 缅甸以及与中国和孟加拉国接壤的边界附近。这将通过以下方式实现 结合现场采集高时间密度的蚊子数量，DNA- 基于蚊子、恶性疟原虫和间日疟原虫存在/丰度的物种分类 (来自项目1)、卫星得出的环境参数和随机森林分析框架。 将开发预测恶性疟和间日疟负担的工具，以此作为 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，量化研究参与者对特定部位寄生虫的暴露 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 关于疟疾传播以及人类流动模式和风险之间的关系。空间 将使用网络建模方法来研究村庄级别的人员流动，基于 通过旅行历史和交通分析报告的个人日常活动。更远距离 区域一级的流动，通过旅行历史和其他旅行数据(如铁路、水)报告 旅行、航空公司)将被用来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在一起 从项目2关于寄生虫种群结构和运动的角度来评估 人口流动和寄生虫迁徙。与地图核心一起，该项目将 提高国家疟疾控制方案更有针对性的干预措施的能力 准确地-因此更有效和高效地-比使用当前工具和 接近了。