Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19
Abstract
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number of infectious individuals at time 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.
Keywords
Cite
@article{arxiv.2010.14004,
title = {Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19},
author = {Tô Tat Dat and Protin Frédéric and Nguyen T. T. Hang and Martel Jules and Nguyen Duc Thang and Charles Piffault and Rodríguez Willy and Figueroa Susely and Hông Vân Lê and Wilderich Tuschmann and Nguyen Tien Zung},
journal= {arXiv preprint arXiv:2010.14004},
year = {2020}
}
Comments
References added, typos fixed, projections updated, minor mistakes corrected