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

arXiv: https://arxiv.org/abs/2010.14004 · HTML: https://mlsanity.com/paper/2010.14004/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory](https://mlsanity.com/paper/2208.01749/) (2022)
- [SARS-Cov-2 RNA Sequence Classification Based on Territory Information](https://mlsanity.com/paper/2101.03323/) (2021)

## Abstract (arXiv)

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.