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.

一种新的基于水文的疟疾传播模型及其现场应用 项目摘要 疟疾是非洲的一项重大公共卫生挑战。扩大驱虫蚊帐和 在过去的20年里，室内残留喷雾(IRS)使非洲的疟疾负担减少了一半， 然而，在许多非洲国家，疟疾控制的进展因有限而停滞不前 LLIN和IRS的有效性。世界卫生组织推荐幼虫来源 作为辅助媒介控制工具的管理(LSM)。然而，LSM到目前为止还没有广泛应用 用于非洲疟疾病媒控制，部分原因是无法预测栖息地位置和稳定性 在许多生态流行病学环境中。如果幼虫栖息地分布能够 先验地预测，以便能够识别最适合或不适合LSM的区域。此外，如果 景观改变和灌溉等环境变化对疟疾风险的影响可能 可以预测，可以开发出基于最小二乘法的最优矢量控制程序。过去的研究表明 试图使用现场调查或遥感数据来识别幼虫栖息地或 环境因素与疟疾风险相关，但这些研究侧重于统计 环境因素与疟疾发病率之间的关系，他们没有考虑 媒介幼虫生态的物理过程与环境调控。此外，动态的 多种环境因素之间相互作用的性质，这些因素可能是高度动态的和 没有对疟疾风险进行研究。并行计算、水文模拟和应用领域的最新进展 遥感为将水文过程纳入疟疾风险提供了极好的机会 建模，并随后提高预测精度。此R21的中心目标是 应用是将基于物理的水文模型与遥感和 昆虫学数据，对疟疾风险进行建模，并应用该模型确定最佳幼虫栖息地 水管理战略和疟疾热点。西区各具特色的研究地点 肯尼亚将使用详细的昆虫学和流行病学信息来校准和验证 模特。该项目的一个独特方面是使用多层数据，如水文、 气象、地形、昆虫学和历史流行病学参数，以加强 疟疾风险预测。这个项目的发现将提高我们对影响的理解 水文和其他环境条件对病媒生态和疟疾风险的影响；以及 通过高空间传播热点的先验预测加强疟疾控制 解决和确定满足以下条件的最优农业水管理战略 粮食生产需要，但减少疟疾传播。