Version 1
: Received: 11 July 2023 / Approved: 11 July 2023 / Online: 12 July 2023 (11:31:07 CEST)
How to cite:
Vyklyuk, Y.; Nevinskyi, D.; Chopyak, V.; Golubovska, O.; Hazdiuk, K.; Škoda, M. Modeling the Spatial Distribution of Different Strains of the COVID-19 Virus Based on the GeoSER(D) Model. Preprints2023, 2023070775. https://doi.org/10.20944/preprints202307.0775.v1
Vyklyuk, Y.; Nevinskyi, D.; Chopyak, V.; Golubovska, O.; Hazdiuk, K.; Škoda, M. Modeling the Spatial Distribution of Different Strains of the COVID-19 Virus Based on the GeoSER(D) Model. Preprints 2023, 2023070775. https://doi.org/10.20944/preprints202307.0775.v1
Vyklyuk, Y.; Nevinskyi, D.; Chopyak, V.; Golubovska, O.; Hazdiuk, K.; Škoda, M. Modeling the Spatial Distribution of Different Strains of the COVID-19 Virus Based on the GeoSER(D) Model. Preprints2023, 2023070775. https://doi.org/10.20944/preprints202307.0775.v1
APA Style
Vyklyuk, Y., Nevinskyi, D., Chopyak, V., Golubovska, O., Hazdiuk, K., & Škoda, M. (2023). Modeling the Spatial Distribution of Different Strains of the COVID-19 Virus Based on the GeoSER(D) Model. Preprints. https://doi.org/10.20944/preprints202307.0775.v1
Chicago/Turabian Style
Vyklyuk, Y., Kateryna Hazdiuk and Miroslav Škoda. 2023 "Modeling the Spatial Distribution of Different Strains of the COVID-19 Virus Based on the GeoSER(D) Model" Preprints. https://doi.org/10.20944/preprints202307.0775.v1
Abstract
The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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