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

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.