A Flexible Compartment Model for Simulation Specific to COVID-19
Received Date: Jul 08, 2022 / Published Date: Aug 09, 2022
Abstract
The dynamic relation among ‘Susceptible’, ‘Infected’, ‘Removed (Recovered)’, ‘Death’ and others for COVID-19 disease is a kind of multibody problem. It has been simulated mainly by compartment models, of which the representative is the SIR model. For the SIR model, ‘Infected’ infects ‘Susceptible’ through the recovery period, and ‘Infected’ is removed as ‘Removed’ not only from the disease but also from the community after the recovery period is ended. For COVID-19, however, the infected individuals should be isolated from the community when they become symptomatic after the latent period is ended. Thus, the infected individuals do not infect susceptible individuals in the community after the latent period, even during the recovery period. Additionally, the infection has occurred in the community even during the latent period before the infected individuals are isolated due to being symptomatic. These two facts for COVID-19 suggest that the simulation by the SIR model would be less accurate in calculating the number of infected individuals and that the results might mislead political and medical interventions. For the model proposed here, the infected individuals are isolated from the community when they become symptomatic after the latent period is ended, but the recovered individuals who have medically recovered and have immunity return to the community, and the infection occurs even during the latent period. The model shows remarkably different results from those simulated by the SIR model. The model also provides the processes evaluating the political and social countermeasures against COVID-19.
Keywords: Breakthrough infection; COVID-19; Herd immunity; Infection during latent period; SIR model; Vaccination
Citation: Ohmori H (2022) A Flexible Compartment Model for Simulation Specific to COVID-19. J Infect Dis Ther S5:001.
Copyright: © 2022 Ohmori H. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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