Predicting Malaria Outbreak Using Indigenous Knowledge and Fuzzy Cognitive Maps: A Case Study of Vhembe District in South Africa
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63999-9_9
Reference44 articles.
1. World Health Organization: World malaria report (2022). https://www.who.int/teams/global-malaria-programme. Accessed 31 August 2023
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3. Kim, Y., et al.: Malaria predictions based on seasonal climate forecasts in South Africa: a time series distributed lag nonlinear model. Sci. Rep. 9(1), 1 (2019). https://doi.org/10.1038/s41598-019-53838-3
4. Macherera, M., Chimbari, M.J., Mukaratirwa, S.: Indigenous environmental indicators for malaria: a district study in Zimbabwe. Acta Trop. 175, 50–59 (2017). https://doi.org/10.1016/j.actatropica.2016.08.021
5. Phoobane, P., Masinde, M., Botai, J.: Prediction model for Malaria: an ensemble of machine learning and hydrological drought indices. In: Yang, X.S., Sherratt, S., Dey, N., Joshi, A. (eds.) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol. 216. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-1781-2_51
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