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Article Dans Une Revue Biology Année : 2020

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

Protin Frédéric
  • Fonction : Auteur
Nguyen Hang
  • Fonction : Auteur
Martel Jules
  • Fonction : Auteur
Nguyen Duc Thang
  • Fonction : Auteur
Charles Piffault
  • Fonction : Auteur
Rodríguez Willy
  • Fonction : Auteur
Figueroa Susely
  • Fonction : Auteur
Hông Vân Lê
  • Fonction : Auteur
Wilderich Tuschmann
  • Fonction : Auteur
Nguyen Tien Zung
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-03273245 , version 1 (29-06-2021)

Identifiants

Citer

Frédéric Protin, Tô Tat Dat, Protin Frédéric, Nguyen Hang, Martel Jules, et al.. Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19. Biology, 2020, 9 (12), pp.477. ⟨10.3390/biology9120477⟩. ⟨hal-03273245⟩
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