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Abstract:
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Influenza is one of the main causes of death, not only in the US but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for detection of any upcoming influenza epidemic periods. Most currently available methods for influenza prediction are based on parametric time series and regression models that impose restrictive and often unverifiable assumptions on the data. In turn, more flexible machine learning models and, particularly, deep learning tools whose utility is proven in a wide range of disciplines, remain largely under-explored in epidemiological forecasting. We study the seasonal influenza in Dallas County by evaluating the forecasting ability of deep learning with feedforward neural networks as well as performance of more conventional statistical models, such as beta regression, autoregressive integrated moving average (ARIMA), least absolute shrinkage and selection operators (LASSO), and a non-parametric multivariate adaptive regression splines (MARS) models for one week and two weeks ahead forecasting. Finally, we develop probabilistic forecasting of influenza in Dallas County by Bayesian model average
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