Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19
This provides a new modeling approach for epidemic dynamics, specifically applied to Covid-19 forecasting, which is incremental as it builds on existing SIR models and wavelet theory.
The authors tackled epidemic modeling by introducing epidemic-fitted wavelets and a novel method combining wavelet theory and machine learning for curve fitting, applying it to forecast Covid-19 spread in multiple countries and US states with results based on the John Hopkins University dataset.
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.