A Novel Hydrology-based Malaria Transmission Model and Field Applications

基于水文学的新型疟疾传播模型及现场应用

• 批准号: 10576220
• 负责人: Kuo-Lin Hsu
• 金额: $ 22.13万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-07 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576220
• 关键词: Africa; African; Agriculture; Architecture; Area; Behavior; Bite; Breeding; Calibration; Clinical; Country; Coupled; Culicidae; Data; Databases; Detection; Development; Ecology; Effectiveness; Entomology; Environmental Engineering technology; Environmental Impact; Environmental Risk Factor; Epidemic; Epidemiology; Ethiopia; Geographic Locations; Geography; Government; Habitats; Incidence; Insecticide Resistance; Insecticides; Investigation; Irrigation; Kenya; Location; Malaria; Malaria prevention; Maps; Methods; Modeling; Modification; Nature; Outcome; Performance; Prevalence; Process; Production; Public Health; Recommendation; Regulation; Research; Research Project Grants; Research Support; Residual state; Resolution; Risk; Site; Source; Surface; Surveys; Testing; United States National Institutes of Health; Vector Ecology; Water; World Health Organization; epidemiologic data; hydrology; improved; malaria transmission; novel; parallel computer; physical process; programs; prospective; remote sensing; risk prediction; scale up; simulation; success; tool; transmission process; vector; vector control

A Novel Hydrology-based Malaria Transmission Model and Field Applications Project Summary Malaria is a major public health challenge in Africa. Scale-up of insecticide-treated nets (LLINs) and indoor residual spray (IRS) in the past two decades has reduced malaria burden in Africa by half, however the progress of malaria control has been stalled in many African countries due to limited effectiveness of LLINs and IRS. The World Health Organization recommends larval source management (LSM) as a supplementary vector control tool. However, LSM has so far not been widely used for malaria vector control in Africa, partly due to the inability to predict habitat locations and stability in many eco-epidemiological settings. LSM would be greatly facilitated if larval habitat distribution can be predicted a priori so that areas best suitable or unsuitable to LSM can be identified. Further, if the impact of environmental modifications such as landscape alteration and irrigation on malaria risk can be predicted, optimal LSM-based vector control program can be developed. Past studies have attempted to use field-based surveys or remotely sensed data for larval habitat identification or correlated environmental factors with malaria risk, but these studies focused on the statistical association between the environmental factors and malaria incidence, and they did not consider the physical processes and environmental regulation on vector larval ecology. Furthermore, the dynamic nature of the interactions between the multiple environmental factors that may be highly dynamic and malaria risk was not studied. Recent advancements in parallel computing, hydrological modeling and remote sensing present an excellent opportunity to incorporate hydrologic processes in malaria risk modeling, and subsequently enhance the prediction accuracy. The central objective of this R21 application is to integrate a physically-based hydrologic model with remote sensing and entomological data, to model malaria risk and apply the model to identify optimal larval habitat water management strategies and malaria hotspots. Well-characterized study sites in western Kenya with detailed entomological and epidemiological information will be used to calibrate and validate the model. A unique aspect of this project is the use of multi-layer data such as hydrological, meteorological, topographic, entomological and historical epidemiological parameters to enhance malaria risk prediction. The findings of this project will improve our understanding of the impact of hydrology and other environmental conditions on vector ecology and malaria risk, and enhance malaria control through a priori prediction of transmission hotspots at high spatial resolution and identification of optimal agricultural water management strategies that meet the crop production needs but reduce malaria transmission.

一种新型基于水文的疟疾传播模型和现场应用 项目摘要 疟疾是非洲的主要公共卫生挑战。杀虫剂处理的网（LLIN）和 在过去的二十年中 但是，由于有限 LLIN和IRS的有效性。世界卫生组织推荐幼虫来源 管理（LSM）作为补充向量控制工具。但是，LSM到目前为止尚未广泛 用于非洲疟疾媒介的控制，部分原因是无法预测栖息地的位置和稳定 在许多生态流行病学环境中。如果幼虫栖息地分布可以 可以先验预测，以便可以确定最适合或不适合LSM的区域。此外，如果是 环境改变（例如景观改变和灌溉对疟疾风险）的影响可以 可以预测，可以开发基于LSM的最佳矢量控制程序。过去的研究有 试图使用基于现场的调查或远程感知的数据进行幼虫栖息地识别或 将环境因素与疟疾风险相关，但这些研究集中在统计上 环境因素与疟疾发病率之间的关联，他们没有考虑 对载体幼虫生态学的物理过程和环境调节。此外，动态 多个环境因素之间相互作用的性质，可能是高度动态的和 没有研究疟疾风险。并行计算，水文建模和 遥感提供了将水文过程纳入疟疾风险的绝佳机会 建模，然后提高预测准确性。此R21的核心目标 应用是将基于物理的水文模型与遥感和 昆虫学数据，建模疟疾风险并应用模型以识别最佳幼虫栖息地 水管理策略和疟疾热点。西方的特征良好的研究地点 肯尼亚具有详细的昆虫学和流行病学信息将用于校准和验证 模型。该项目的一个独特方面是使用多层数据，例如水文， 气象，地形，昆虫学和历史流行病学参数以增强 疟疾风险预测。该项目的发现将改善我们对影响的理解 关于媒介生态学和疟疾风险的水文和其他环境条件，以及 通过在高空间上对传输热点的先验预测增强疟疾控制 解决与满足最佳农业水管理策略的解决和识别 作物生产需要，但减少了疟疾的传播。