Machine-learning techniques for predicting the evolution of an epidemic

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Various methods have been developed to improve the forecasting of case spread using mathematical and time-series models, but these rely on a mechanistic understanding of how the contagion spreads and the efficacy of mitigation measures such as masks and isolation. Such methods become increasingly accurate as our understanding of a particular contagion improves, but this can lead to erroneous assumptions that might unknowingly affect the accuracy of the modeling results.

A new KAUST study has shown that machine learning techniques can extract relevant information from the data with flexibility and without any assumptions regarding the underlying data distribution. GPR is very attractive for handling different kinds of data that follow different Gaussian or nonGaussian distributions, and the integration of lagged data contributes significantly to improved prediction quality.

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