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

Medientyp: E-Artikel; Sonstige Veröffentlichung

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

Beteiligte: Tat Dat, Tô [VerfasserIn]; Frédéric, Protin [VerfasserIn]; Hang, Nguyen T. T. [VerfasserIn]; Jules, Martel [VerfasserIn]; Duc Thang, Nguyen [VerfasserIn]; Piffault, Charles [VerfasserIn]; Willy, Rodríguez [VerfasserIn]; Susely, Figueroa [VerfasserIn]; Lê, Hông Vân [VerfasserIn]; Tuschmann, Wilderich [VerfasserIn]; Tien Zung, Nguyen [VerfasserIn]

Erschienen: MDPI, 2021-02-12

Sprache: Englisch

DOI: https://doi.org/10.5445/IR/1000129660; https://doi.org/10.3390/biology9120477

ISSN: 2079-7737

Schlagwörter: SARS-CoV-2 ; epidemic-fitted wavelet ; curve fitting ; Covid-19 spread predicting ; Covid-19 ; model selection ; Mathematics ; epidemic dynamics

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Beschreibung: 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

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