key: cord-0758933-2ud0h0dv
authors: Varotsos, Costas A.; Krapivin, Vladimir F.; Xue, Yong; Soldatov, Vladimir; Voronova, Tanya
title: COVID-19 Pandemic Decision Support System for an Appropriate Population Defense Strategy and Vaccination Effectiveness
date: 2021-06-05
journal: Saf Sci
DOI: 10.1016/j.ssci.2021.105370
sha: add8830079d3b159d95aae54571a21b47ede1fba
doc_id: 758933
cord_uid: 2ud0h0dv

The 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.

countries and regions, take into account the big data clouds for important geophysical and of which is based on epidemiologically verified monitoring data whose architecture is 45 represented as a multi-scale stochastic process with a detailed structure. All officially 46 recorded data cover potential COVID-19 results as statistical series, the analysis of which helps to test and predict a trend in each pandemic. Unfortunately, many efforts made by many scenarios what is most likely to happen. 86 The main contribution of this paper is how to use official COVID-19 pandemic data to 87 predict trends in virus spreading. The method is designed to create a decision-making support 88 system (DMSS) based on the sequential analysis procedure to assess the corona virus 89 transition probability between sites and the mathematical model. 

The block function DMCPO Dynamic model for COVID-19 pandemic outcomes. AABMP Algorithm for assessing basic management parameters. RSDMP Realization of the sequential decision-making procedure. SABDC Synchronization algorithm for big data clouds. CIISD Calculation of instability indicator for statistical data fluxes. ASPPO Assessment of statistical parameters for the pandemic outcomes using the randomized approximation. 

where [0,1] quantifies the immunization coefficient, p is the probability of the human-to- 

where c is the recovery rate at time t (c=R /I), J is instability indicator reflecting the 138 statistical character of official COVID-19 pandemic data. 

where q(t) indicates the medical support indicator at time t. 145 Dead individuals (D) determine the death rates of the COVID-19 pandemic per 146 country  reflecting the effectiveness of socio-economic and management solutions for the 147 defense of the population through appropriate decisions concerning the probability of 148 reducing p. Finally, the following equation describes the total number of dead individuals:

Official statistics series Z= (S, I, R, D) represent typical data with deterministic and 151 stochastic components that need to introduce the instability factor J for country  to correct 152 the model results. To formalize this procedure the algorithm for evaluating these instability 153 data is proposed: PEP scenario. Figure 3 shows that in this case the changes in the pandemic outcomes stabilize depending on the vaccination level. This uncertainty also arises from the data in Table 4 273 which show the existence of countries where the effects of the pandemic are minimal. where many restrictions are considered unjustified. A confirmation of this conclusion is given 292 in Figure 6 and Table 4 . In conclusion, the results of this study demonstrate that DMSS helps to understand and 

TLCoV-An automated Covid-19 screening 326 model using Transfer Learning from chest X-ray images

Covid-19 vaccines and community 372 immunity

A new fractal pattern feature generation function-based 374 emotion recognition method using EEG

A new big data approach based on geoecological 376 information-modeling system

Global ecoinformatics: Theory and applications

A new model for the spread of COVID-19 and the 380 improvement of safety

Diagnostic model for the society safety under 382 COVID-19 pandemic conditions

COVID-19 vaccination during the COVID-19 pandemic in China. Vaccines