Malaria prediction using Machine learning models

JAMSTEC and Nagasaki University along with counterparts from South Africa recently completed the “Infectious Diseases Early Warning System (iDEWS)” project. The project was funded under the SATREPS (Science and Technology Research Partnership for Sustainable Development) project by the Japan International Cooperation Agency (JICA), the Japan Agency for Medical Research and Development (AMED) and The Department of Science and Technology, South Africa. Under the project an early warning system was developed for forecasting the number of malaria cases over Limpopo province of South Africa three months ahead. The early warning system developed is based on an ensemble of machine learning models.

JAMSTEC is funding a pilot project, under its 4DVE framework, for maintaining the models and to continue generating malaria forecasts and providing the forecasts to registered users. Registered users can login to view the forecasts.

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Publication

P. Martineau, S. K. Behera, M. Nonaka, R. Jayanthi, T. Ikeda, N. Minakawa, P. Kruger, and Q. E. Mabunda (2022): Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning. Front. Public Heal. 10 ., https://doi.org/10.3389/fpubh.2022.962377