| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0619027 |
| utime |
20250423144545.4 |
| mtime |
20250416235959.9 |
| DOI |
10.1007/978-981-96-3863-5_49 |
| title
(primary) (eng) |
Czech Anti-Covid Rules Evaluation |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0619026 |
| ISBN |
978-981-96-3862-8 |
| ISSN |
1876-1100 |
| title
|
Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024) |
| part_title |
1372 |
| page_num |
537-546 |
| publisher |
| place |
Singapore |
| name |
Springer Nature Singapore |
| year |
2025 |
|
| editor |
|
| editor |
| name1 |
Frangi |
| name2 |
Alejandro F. |
|
| editor |
|
|
| keyword |
COVID-19 |
| keyword |
Recursive forecasting model |
| keyword |
Bayesian learning |
| keyword |
Adaptive multistep predictor |
| keyword |
Anti-pandemic measures |
| author
(primary) |
| ARLID |
cav_un_auth*0101239 |
| name1 |
Žid |
| name2 |
Pavel |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101100 |
| name1 |
Havlíček |
| name2 |
Vojtěch |
| institution |
UTIA-B |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0486529 |
| name |
International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024) |
| dates |
20241119 |
| mrcbC20-s |
20241121 |
| place |
The University of Manchester |
| country |
GB |
|
| RIV |
IN |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2026 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0365900 |
| confidential |
S |
| arlyear |
2025 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
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 |
| mrcbU67 |
Su Ruidan 340 |
| mrcbU67 |
Frangi Alejandro F. 340 |
| mrcbU67 |
Zhang Yudong 340 |
|