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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10101 - Pure mathematics

Projekt

<a href="/cs/project/GC18-01953J" target="_blank" >GC18-01953J: Geometrické metody ve statistické teorie učení a aplikace</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BIOLOGY-BASEL

ISSN

2079-7737

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

21

Strana od-do

477

Kód UT WoS článku

000601813500001

EID výsledku v databázi Scopus

2-s2.0-85098171869