| bibtype |
J -
Journal Article
|
| ARLID |
0579554 |
| utime |
20240402214920.6 |
| mtime |
20231215235959.9 |
| SCOPUS |
85183571431 |
| DOI |
10.1016/j.procs.2023.10.285 |
| title
(primary) (eng) |
Governmental Anti-Covid Measures Effectiveness Detection |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0579553 |
| ISSN |
1877-0509 |
| title
|
Procedia Computer Science |
| volume_id |
225 |
| volume |
1 (2023) |
| page_num |
2922-2931 |
|
| keyword |
COVID-19 |
| keyword |
Recursive forecasting model |
| keyword |
Machine learning method |
| keyword |
Prediction |
| 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 |
| project |
| project_id |
GA19-12340S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0376011 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0459925 |
| name |
International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2023 (KES 2023) /27./ |
| dates |
20230906 |
| mrcbC20-s |
20230908 |
| place |
Athens |
| country |
GR |
|
| result_subspec |
SCOPUS |
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2024 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0348913 |
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-s |
0.569 |
| mrcbT16-E |
Q3 |
| arlyear |
2023 |
| mrcbU14 |
85183571431 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0579553 Procedia Computer Science Roč. 225 č. 1 2023 2922 2931 1877-0509 |
|