Epidemic dynamics via wavelet theory and machine learning with applications to Covid-19

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985840%3A_____%2F20%3A00536558" target="_blank" >RIV/67985840:_____/20:00536558 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/biology9120477" target="_blank" >https://doi.org/10.3390/biology9120477</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/biology9120477" target="_blank" >10.3390/biology9120477</a>

Result language
angličtina
Original language name
Epidemic dynamics via wavelet theory and machine learning with applications to Covid-19
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10101 - Pure mathematics

Project
<a href="/en/project/GC18-01953J" target="_blank" >GC18-01953J: Geometric methods in statistical learning theory and applications</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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