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

Tat Dat, Tô; Frédéric, Protin; Hang, Nguyen T. T.; Jules, Martel; Duc Thang, Nguyen; Piffault, Charles; Willy, Rodríguez; Susely, Figueroa; Lê, Hông Vân; Tuschmann, Wilderich 1; Tien Zung, Nguyen
1 Karlsruher Institut für Technologie (KIT)


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

Verlagsausgabe §
DOI: 10.5445/IR/1000129660
Veröffentlicht am 12.02.2021
DOI: 10.3390/biology9120477
Zitationen: 10
Web of Science
Zitationen: 7
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Mathematik (MATH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2079-7737
KITopen-ID: 1000129660
Erschienen in Biology
Verlag MDPI
Band 9
Heft 12
Seiten Art. Nr.: 477
Vorab online veröffentlicht am 18.12.2020
Schlagwörter Covid-19; SARS-CoV-2; epidemic-fitted wavelet; epidemic dynamics; model selection; curve fitting; Covid-19 spread predicting
Nachgewiesen in Web of Science
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