Evaluating transfer learning for forecasting chikungunya cases
Abstract
In the last decade, we have seen a spread of chikungunya cases all over the country. Each year, new cities are reporting cases. To minimize the impact of this new disease on public health is necessary to understand how the disease spreads over time. It can be done using mathematical models[1]. Another approach is through statistical models, which can learn from historical data and can help us forecast the number of cases in the short to medium term. When it comes to forecasting, another class of model that is growing more common is deep learning models (DL)[2], which are quite robust at capturing the nonlinear associations between predictor variables and the response variable. [...]
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References
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