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Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency

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  • Sharov, Konstantin S.

Abstract

We propose a Susceptible–Infected–Recovered (SIR) modified model for Coronavirus disease – 2019 (COVID-19) spread to estimate the efficacy of lockdown measures introduced during the pandemic. As input data, we used COVID-19 epidemiological information collected in fifteen European countries either in private surveys or using official statistics. Thirteen countries implemented lockdown measures, two countries (Sweden, Iceland) not. As output parameters, we studied herd immunity level and time of formation. Comparison of these parameters was used as an indicator of effectiveness / ineffectiveness of lockdown measures. In the absence of a medical vaccine, herd immunity may be regarded as a factor of population adaptation to severe acute respiratory syndrome-related coronavirus-2, the viral pathogen causing COVID-19 disease (SARS-CoV-2), and hence COVID-19 spreading stop. We demonstrated that there is no significant difference between lockdown and no-lockdown modes of COVID-19 containment, in terms of both herd immunity level and the time of achieving its maximum. The rationale for personal and business lockdowns may be found in the avoidance of healthcare system overburdening. However, lockdowns do not prevent any virus with droplet transmission (including SARS-CoV-2) from spreading. Therefore, in case of a future viral pathogen emergence, lockdown measures efficiency should not be overestimated, as it was done almost universally in the world during COVID-19 pandemic.

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  • Sharov, Konstantin S., 2020. "Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:chsofr:v:141:y:2020:i:c:s0960077920306913
    DOI: 10.1016/j.chaos.2020.110295
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    6. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Wu, Yucui & Zhang, Zhipeng & Song, Limei & Xia, Chengyi, 2024. "Global stability analysis of two strains epidemic model with imperfect vaccination and immunity waning in a complex network," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    2. RabieiMotlagh, Omid & Soleimani, Leila, 2023. "Effect of mutations on stochastic dynamics of infectious diseases, a probability approach," Applied Mathematics and Computation, Elsevier, vol. 451(C).
    3. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    4. Kathya Lorena Cordova-Pozo & Hubert P. L. M. Korzilius & Etiënne A. J. A. Rouwette & Gabriela Píriz & Rolando Herrera-Gutierrez & Graciela Cordova-Pozo & Miguel Orozco, 2021. "Using Systems Dynamics for Capturing the Multicausality of Factors Affecting Health System Capacity in Latin America while Responding to the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    5. Dmitry V. Boguslavsky & Natalia P. Sharova & Konstantin S. Sharov, 2021. "Cryptocurrency as Epidemiologically Safe Means of Transactions: Diminishing Risk of SARS-CoV-2 Spread," Mathematics, MDPI, vol. 9(24), pages 1-19, December.

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