Biology (2020-12-01)

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

  • Tô Tat Dat,
  • Protin Frédéric,
  • Nguyen T. T. Hang,
  • Martel Jules,
  • Nguyen Duc Thang,
  • Charles Piffault,
  • Rodríguez Willy,
  • Figueroa Susely,
  • Hông Vân Lê,
  • Wilderich Tuschmann,
  • Nguyen Tien Zung

Journal volume & issue
Vol. 9, no. 477
p. 477


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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.