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Governmental Anti-Covid Measures Effectiveness Detection
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SYSNO ASEP 0579554 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve SCOPUS Title Governmental Anti-Covid Measures Effectiveness Detection Author(s) Žid, Pavel (UTIA-B) RID, ORCID
Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RIDNumber of authors 3 Source Title Procedia Computer Science - ISSN 1877-0509
Roč. 225, č. 1 (2023), s. 2922-2931Number of pages 10 s. Publication form Print - P Action International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2023 (KES 2023) /27./ Event date 06.09.2023 - 08.09.2023 VEvent location Athens Country GR - Greece Event type WRD Language eng - English Country NL - Netherlands Keywords COVID-19 ; Recursive forecasting model ; Machine learning method ; Prediction ; Anti-pandemic measures Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UTIA-B - RVO:67985556 EID SCOPUS 85183571431 DOI 10.1016/j.procs.2023.10.285 Annotation We present a retrospective analysis of Czech anti-covid governmental measures' effectiveness for an unusually long three years of observation. Numerous Czech government restrictive measures illustrate this analysis applied to three years of COVID-19 data from the first three COVID-19 cases detected on 1st March 2020 till March 2023. It illustrates the course from the dramatic combat of unknown illness to resignation to country-wide measures and placing COVID-19 into a category of common nuisances. Our analysis uses the derived adaptive recursive Bayesian stochastic multidimensional Covid model-based prediction of nine essential publicly available COVID-19 data series. The COVID-19 model enables us to differentiate between effective measures and solely nuisance or antagonistic provisions and their correct or wrong timing. Our COVID model allows us to predict vital covid statistics such as the number of hospitalized, deaths, or symptomatic individuals, which can serve for daily control of anti-covid measures and the necessary precautions and formulate recommendations to control future pandemics. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2024 Electronic address https://www.sciencedirect.com/science/article/pii/S1877050923014436?via%3Dihub
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