COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness
AffiliationNational and Kapodistrian University of Athens, Athens, Greece
Kotelnikov’s Institute of Radioengineering and Electronics, Fryazino Branch, Russian Academy of Sciences, Vvedensky 1, Fryazino, Moscow Region 141190, Russian Federation
University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China
University of Derby
MetadataShow full item record
AbstractThe year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario.
CitationVarotsos, C.A., Krapivin, V.F., Xue, Y., Soldatov, V. and Voronova, T., (2021). 'COVID-19 Pandemic Decision Support System for an Appropriate Population Defense Strategy and Vaccination Effectiveness'. Safety Science, pp. 1-7.
The following license files are associated with this item:
- Creative Commons