Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number \(I(t)\) of infectious individuals at time \(t\) in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using...

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Bibliographic Details
Published in:arXiv.org 2020-11
Main Authors: Tô Tat Dat, Protin Frédéric, Hang, Nguyen T T, Martel, Jules, Nguyen, Duc Thang, Piffault, Charles, Rodríguez Willy, Figueroa Susely, Lê, Hông Vân, Tuschmann, Wilderich, Nguyen, Tien Zung
Format: Article
Language:English
Subjects:
Coronaviruses
COVID-19
Curve fitting
Epidemics
Machine learning
Modelling
Severe acute respiratory syndrome coronavirus 2
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Summary:We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number \(I(t)\) of infectious individuals at time \(t\) in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the John Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
ISSN:2331-8422
DOI:10.48550/arxiv.2010.14004