Governmental Anti-Covid Measures Effectiveness Detection

0579554 - ÚTIA 2024 RIV NL eng J - Článek v odborném periodiku
Žid, Pavel - Haindl, Michal - Havlíček, Vojtěch
Governmental Anti-Covid Measures Effectiveness Detection.
Procedia Computer Science. Roč. 225, č. 1 (2023), s. 2922-2931. ISSN 1877-0509.
[International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2023 (KES 2023) /27./. Athens, 06.09.2023-08.09.2023]
Grant CEP: GA ČR(CZ) GA19-12340S
Institucionální podpora: RVO:67985556
Klíčová slova: COVID-19 * Recursive forecasting model * Machine learning method * Prediction * Anti-pandemic measures
Obor OECD: Automation and control systems
Způsob publikování: Open access
http://library.utia.cas.cz/separaty/2023/RO/zid-0579554.pdf https://www.sciencedirect.com/science/article/pii/S1877050923014436?via%3Dihub

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.
Trvalý link: https://hdl.handle.net/11104/0348913