0579554 20240402214920.620231215235959.9 85183571431 10.1016/j.procs.2023.10.285 10 s. P cav_un_epca*05795531877-05092251 (2023)2922-2931 COVID-19 Recursive forecasting model Machine learning method Prediction Anti-pandemic measures cav_un_auth*0101239 Žid Pavel UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101100 Havlíček Vojtěch UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/RO/zid-0579554.pdf https://www.sciencedirect.com/science/article/pii/S1877050923014436?via%3Dihub GA19-12340S GA ČR CZ cav_un_auth*0376011 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. cav_un_auth*0459925 International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2023 (KES 2023) /27./ 20230906 20230908 Athens GR SCOPUS BD 20000 20200 20205 2024 3 RVO:67985556 https://hdl.handle.net/11104/0348913 S A 152 0.569 21.93 2.39 16825 Q3 2023 85183571431 SCOPUS PUBMED WOS cav_un_epca*0579553 Procedia Computer Science Roč. 225 č. 1 2023 2922 2931 1877-0509