tetano
Editor, Senior Moderator
Biosystems
. 2023 Sep 20;105035.
doi: 10.1016/j.biosystems.2023.105035. Online ahead of print. COVID-19 spatio-temporal forecast in England
Oleg Gaidai[SUP] 1 [/SUP], Vladimir Yakimov[SUP] 2 [/SUP], Fuxi Zhang[SUP] 1 [/SUP]
Affiliations
The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health. This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy.
Keywords: COVID-19; Dynamic system; Epidemic outbreak; Mathematical biology; Public health; Reliability; SARS-CoV-2.
. 2023 Sep 20;105035.
doi: 10.1016/j.biosystems.2023.105035. Online ahead of print. COVID-19 spatio-temporal forecast in England
Oleg Gaidai[SUP] 1 [/SUP], Vladimir Yakimov[SUP] 2 [/SUP], Fuxi Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 37739309
- DOI: 10.1016/j.biosystems.2023.105035
The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health. This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy.
Keywords: COVID-19; Dynamic system; Epidemic outbreak; Mathematical biology; Public health; Reliability; SARS-CoV-2.