Redefining thermal suitability for urban malaria transmission in the context of humidity

项目摘要/摘要 由于城市环境是地球上增长最快的景观，媒介传播的疾病 在过去的几十年里，城市适应蚊子的数量显著增加。城市向量包括 斯氏按蚊是南亚城市疟疾的罪魁祸首。年消灭疟疾 南亚以及为其进一步向非洲扩张做好准备，取决于采取有效行动打击 城市中的疾病。我们知道温度对疟疾传播有强烈的、非线性的影响。虽然 相对湿度对疟疾流行病学也有重要影响，它对传播的量化影响 被广泛地研究得很少，并且经常被视为独立于温度。与蚊子有关的小气候 进一步受到人口密度和景观特征显著空间变化的影响 城市环境导致了疾病传播的强烈时间和空间模式。因为这些 关系目前还没有被很好地理解，我们预测出现、传播和 在城市环境中控制疟疾。我们最重要的假设是湿度影响城市疟疾 通过修改温度-传输关系进行传输。此外，结合了 湿度将改善对疟疾传播和疟疾风险热点的预测，在时间和 传播的空间模型。我们建议的研究将通过以下方式解决这一知识差距 明确的目标。目标1将调查湿度对温度-疟疾传播的影响 两性关系。将进行全面的实验来表征两种相对湿度的影响 温度对蚊子和疟疾生活史特征的影响与传播有关。这些机械式的 然后，这些关系将被纳入目标2和目标3中疟疾流行病学的时间和空间模型。 目标2将制定和参数化一个时间耦合的人-蚊子传播模型，用于预测 疟疾发病率和媒介丰度的季节和年际变化。AIM 3将实施一个 利用气象资料预测跨城市环境传播风险和发病率的空间模型 利用城市土地覆盖数据进行观测，以绘制疟疾传播的环境适宜性地图。适宜性 然后，地图将叠加人口密度和社会经济因素，以预测热点 变速箱。印度的两个城市，苏拉特和艾哈迈达巴德，经历了显著的年平均差异 在过去的二十年里，它一直保持着广泛的疟疾监测项目。 这两个城市将提供截然不同的机会来测试气候-特征关系的能力 目的1改进城市疟疾传播模式。主要成果包括改善了概念 基于蚊虫生物学的城市疟疾环境流行病学框架和新模型 应用这一知识来预测疾病传播的方法。对未来的预测 异常季节与热点识别相结合，将加强有针对性的公共卫生干预。

PROJECT SUMMARY / ABSTRACT With urban environments the fastest growing landscapes on the planet, transmission of vector-borne diseases by urban adapted mosquitoes has increased markedly over the past several decades. Urban vectors include Anopheles stephensi, the mosquito responsible for urban malaria across South Asia. Elimination of malaria in South Asia, and preparedness against its further expansion into Africa, hinges on effective action against the disease in cities. We know temperature has strong, non-linear effects on malaria transmission. Although relative humidity also has important effects on malaria epidemiology, its quantitative effects on transmission are vastly understudied and often treated as independent from temperature. Mosquito-relevant microclimates are further affected by pronounced spatial variation in human population densities and landscape features of urban environments resulting in strong temporal and spatial patterns of disease transmission. Because these relationships are currently not well understood, we have limited capacity to predict the emergence, spread, and control of malaria in urban environments. Our overarching hypothesis is that humidity affects urban malaria transmission by modifying the temperature-transmission relationship. Further, incorporating the effect of humidity will improve predictions of malaria transmission and hot spots of malaria risk in both temporal and spatial models of transmission. Our proposed research will address this knowledge gap through the following specific aims. Aim 1 will investigate the effects of humidity on the temperature-malaria transmission relationship. Comprehensive experiments will be conducted to characterize the effects of both relative humidity and temperature on mosquito and malaria life history traits relevant for transmission. These mechanistic relationships will then be integrated into temporal and spatial models of malaria epidemiology in Aims 2 and 3. Aim 2 will formulate and parameterize a temporal coupled human-mosquito transmission model used to predict the seasonal and interannual variation in malaria incidence and vector abundance. Aim 3 will implement a spatial model to predict transmission risk and incidence across urban environments by using meteorological observations with urban land cover data to map environmental suitability for malaria transmission. Suitability maps will then be overlaid with population density and socio-economic factors to predict hotspots for transmission. Two cities in India, Surat and Ahmedabad, experience notable differences in mean annual relative humidity and have maintained extensive surveillance malaria programs over the last two decades. These two cities will provide contrasting opportunities to test the ability of the climate-trait relationships from Aim 1 to improve transmission models of urban malaria. Major outcomes include an improved conceptual framework for the environmental epidemiology of urban malaria based on mosquito biology, and new modeling approaches that apply this knowledge to make predictions of disease transmission. Prediction of upcoming anomalous seasons combined with identification of hotspots will enhance targeted public health intervention.