Harnessing publicly available geospatial data to guide vector-borne disease interventions in sub-Saharan Africa

气候和环境因素是影响撒哈拉以南非洲病媒传播疾病时空分布的主要驱动因素。例如，疟疾热点经常位于农村地区，那里有按蚊媒介喜欢的栖息地。因此，有可能利用气候、环境和疾病传播风险之间的关系来确定最需要控制干预措施的地区，并随后更有效地确定资源的目标。我们现在生活的世界中，大量关于气候和环境的信息正在通过遥感方法，例如以越来越高的空间和时间分辨率的卫星持续收集，并且越来越多地被公开。国家管制方案也在改进其数据收集和报告系统。例如，许多国家现在使用数字卫生管理信息系统定期收集卫生信息，例如疟疾确诊病例的数量。虽然大比例尺大陆一级风险静态地图越来越常见，使用地质统计模型或物种分布模型等方法估计疾病负担并突出传播风险高的地区，但在指导国家控制方案活动方面，这些数据来源中包含的大量公共卫生信息尚未得到充分利用。该项目的目的是开发方法，通过这些方法，可以产生和利用空间和时间上量身定做的疾病风险产出，以优化病媒传播的疾病控制活动。该项目将侧重于疟疾和非传染性疾病在撒哈拉以南非洲的传播，并将利用谷歌地球引擎和R SHINY等免费可用的资源来获取数据、分析和展示所产生的成果。

Climate and environmental factors are key drivers to the spatial and temporal distributions of vector-borne diseases affecting sub-Saharan Africa. For example, malaria hotspots are frequently located in rural areas which contain habitat favoured by the Anopheles mosquito vector. It is therefore possible to leverage the relationship between climate, environment and disease transmission risk to identify areas with the greatest need for control interventions and subsequently target resources more effectively.We now live in a world where vast amount of information on climate and environment is being continuously collected by remote sensing methods e.g. satellites at ever higher spatial and temporal resolutions and increasingly being made publicly available. National control programmes are also improving their data collection and reporting systems. For example, many countries now routinely collect health information e.g. number of confirmed malaria cases using digital health management information systems. While large-scale static maps of continent-level risk are increasingly commonplace, using methods such as geostatistical modelling or species distribution modelling to estimate disease burden and highlight areas of high transmission risk, the vast amount of public health information contained in these data sources has yet to be fully harnessed with respect to guiding national control programme activities. The aim of this project is to develop methods by which spatially and temporally tailored outputs of disease risk can be generated and utilised to optimise vector-borne disease control activities. The project will focus on malaria and NTD transmission in sub-Saharan Africa, and will make use of freely available resources such as Google Earth Engine and R Shiny to access data, analyse and present resulting outputs.