Czech Anti-Covid Rules Evaluation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00619027" target="_blank" >RIV/67985556:_____/25:00619027 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-981-96-3863-5_49" target="_blank" >http://dx.doi.org/10.1007/978-981-96-3863-5_49</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-96-3863-5_49" target="_blank" >10.1007/978-981-96-3863-5_49</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Czech Anti-Covid Rules Evaluation
Popis výsledku v původním jazyce
We present a retrospective analysis of the efficaciousness of Czech anti-COVID state rules during the first quarter of 2022. This analysis focuses on a specific time window from our four-year evaluation of various restrictive measures implemented by the Czech government, examining long-term data from the first three COVID-19 cases detected in early March 2020 through to September 2024. It traces the evolution from the initial intense response to the virus to the eventual normalization of COVID-19 as a common issue. Our study utilizes an adaptive recursive Bayesian stochastic multidimensional model to predict key COVID-19 metrics from nine essential data series. This model distinguishes between effective measures and those merely disruptive or mistimed. Additionally, it predicts crucial statistics such as hospitalizations, deaths, and symptomatic cases, offering valuable insights for the daily management of anti-COVID measures, necessary precautions, and future pandemic recommendations.
Název v anglickém jazyce
Czech Anti-Covid Rules Evaluation
Popis výsledku anglicky
We present a retrospective analysis of the efficaciousness of Czech anti-COVID state rules during the first quarter of 2022. This analysis focuses on a specific time window from our four-year evaluation of various restrictive measures implemented by the Czech government, examining long-term data from the first three COVID-19 cases detected in early March 2020 through to September 2024. It traces the evolution from the initial intense response to the virus to the eventual normalization of COVID-19 as a common issue. Our study utilizes an adaptive recursive Bayesian stochastic multidimensional model to predict key COVID-19 metrics from nine essential data series. This model distinguishes between effective measures and those merely disruptive or mistimed. Additionally, it predicts crucial statistics such as hospitalizations, deaths, and symptomatic cases, offering valuable insights for the daily management of anti-COVID measures, necessary precautions, and future pandemic recommendations.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)
ISBN
978-981-96-3862-8
ISSN
1876-1100
e-ISSN
1876-1119
Počet stran výsledku
10
Strana od-do
537-546
Název nakladatele
Springer Nature Singapore
Místo vydání
Singapore
Místo konání akce
The University of Manchester
Datum konání akce
19. 11. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001491664600049