Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

中文翻译：

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不同地理背景下疟疾环境危险因素的遥感

尽管进行了全球干预，疟疾仍然是世界许多地区的主要公共卫生问题。了解疟疾模式的地理差异及其环境决定因素可以支持疟疾控制的针对性和消除战略的制定。我们使用遥感环境数据分析了 2014 年至 2017 年埃塞俄比亚两个地理环境中环境风险因素对恶性疟原虫和间日疟原虫引起的疟疾病例的影响。地理空间数据集来自多个来源，并表征了气候、植被、土地利用、地形和地表水。每年在分区 (kebele) 级别对两个研究区域中的每个区域的所有数据进行汇总。我们使用增强回归树 (BRT) 模型分析了环境数据与疟疾病例之间的关联。我们发现疟疾发生的空间差异很大。与土地覆盖绿度 (NDVI) 和湿度 (NDWI) 相关的光谱指数与疟疾呈负相关，因为在雨季之后的几个月内，在植被覆盖度和湿度低的景观中发现的疟疾发病率最高。气候因素，包括降水和地表温度，与疟疾呈正相关。聚落结构也发挥了重要作用，在两个研究区产生了不同的影响。与地表水相关的变量，例如灌溉农业、湿地、季节性洪水泛滥的水体和最近排水系统上方的高度，对疟疾没有很大影响。我们在两个地理上不同的地区发现了疟疾与环境条件之间的不同关系。这些结果强调，疟疾与环境关系的研究和疟疾发生的预测模型应该是特定于背景的，以解释这种差异。