Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19
- PMID: 33353045
- PMCID: PMC7767158
- DOI: 10.3390/biology9120477
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 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 Johns 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: Covid-19; Covid-19 spread predicting; SARS-CoV-2; curve fitting; epidemic dynamics; epidemic-fitted wavelet; model selection.
Conflict of interest statement
The authors declare no conflict of interest.
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