0619027 20260226075436.420250416235959.9 105003185570 001491664600049 10.1007/978-981-96-3863-5_49 10 s. P cav_un_epca*0619026978-981-96-3862-81876-11001372537-546SingaporeSpringer Nature Singapore2025SuRuidanFrangiAlejandro F.ZhangYudong COVID-19 Recursive forecasting model Bayesian learning Adaptive multistep predictor 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. https://library.utia.cas.cz/separaty/2025/RO/haindl-0619027.pdf 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. cav_un_auth*0486529 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024) 20241119 20241121 The University of Manchester GB IN 20000 20200 20205 2026 3 PR RVO:67985556 https://hdl.handle.net/11104/0365900 S 49 0.147 15.85 0.4 9115 Q4 2025 105003185570 SCOPUS PUBMED 001491664600049 WOS cav_un_epca*0619026 Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024) Springer Nature Singapore 2025 Singapore 537 546 978-981-96-3862-8 Lecture Notes in Electrical Engineering 1372 1876-1100 1876-1119 Su Ruidan 340 Frangi Alejandro F. 340 Zhang Yudong 340