Modeling Malaria Outbreaks Utilizing Weather FactorsDownload PDF


02 Feb 2022 (modified: 05 May 2023)Submitted to ProjectX2021Readers: Everyone
Keywords: machine learning, transfer learning, epidemiology, malaria, dengue, weather, disease predication, recurrent neural networks, long short term memory network
TL;DR: A look into modelling mosquito-borne disease outbreaks based on weather conditions
Abstract: Using meteorological data, time series forecasts of disease outbreaks can better capture the true epidemiological profile of tropical diseases such as malaria. In this study, several methods of time series analysis are employed to study the disease patterns of malaria in the Indian state of Odisha. Weather information, including temperature and precipitation data, is incorporated alongside monthly case numbers in SARIMA and LSTM models. The viability of transferring the model trained on malaria in Odisha to dengue in Bangkok, Thailand, is also explored. The methods outlined in this paper can serve as the basis for forecasting mosquito-borne disease outbreaks in settings with a poor data-collection infrastructure.
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