Advances in machine learning (ML) for healthcare applications have the potential to be an alternative and best solution to solve the problems of climate-sensitive diseases in Africa and low-income countries like Tanzania. The main objective of this project is to strengthen the health system in the East African region, specifically Tanzania, by creating a dataset that aids in the prediction and characterization of climate-sensitive waterborne diseases. This will be Tanzania’s first machine learning dataset for forecasting climate-sensitive waterborne diseases. The dataset will include three climate-sensitive waterborne diseases, which are typhoid fever, diarrhea, and amoebiasis.
Five different kinds of datasets will be collected and used to characterize the disease hotspots in five selected areas of Tanzania: Morogoro Municipal Council (MC), Singida MC, Dodoma City Council (CC), and Dar es Salaam CC (Temeke MC, Ilala MC). Datasets will be collected in the following categories: (i) demographic characteristics of the waterborne diseases, (ii) locations of the toilets and quality of the toilets, (iii) management of solid wastes and dump sites, (iv) meteorological information of the hotspots, and (v) location of the water sources used by local people for daily household activities.
The combination of all these datasets in tabular form will be used to train powerful machine learning algorithms to predict and characterize the outbreaks of waterborne diseases in the study areas. Furthermore, the predictive models can be embedded into early warning systems to support council managers and healthcare providers to make informed decisions to control and eliminate the outbreak of waterborne diseases.
