Czech Anti-Covid Rules Evaluation
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Czech Anti-Covid Rules Evaluation
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
10
Pages from-to
537-546
Publisher name
Springer Nature Singapore
Place of publication
Singapore
Event location
The University of Manchester
Event date
Nov 19, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001491664600049