ABSTRACT
Objective Many mutant strains of SARS-CoV-2 have stronger infectivity and immune escape ability. The situation of epidemic evaluation, prevention and control is serious. The aim of the present paper is to track and predict the infectious transmission of COVID-19 through a theoretical model. Methods Based on the grid epidemic model, this paper discussed the relationship between the duration of infection and the effect of group immunity, and on this basis, established the theoretical model of infection transmission of COVID-19. The infectivity parameter A and the immune effect parameter B are introduced to predict the daily variation curve of infection. The parameter (Equation presented)can be used to quantitatively compare the comprehensive infectivity of each mutant, and we also test the conjecture that the infection parameters A and B are not related to regional factors. Results Through the theoretical model of infection transmission of COVID-19, the infectious time was accurately predicted. By analyzing the infectivity and electrical changes of mutant strains, the internal relationship between the infectivity of mutant strains and the electrical changes of mutant residues was pointed out. The parameter changes of mutants were analyzed, and the comprehensive infectivity of each mutant was quantitatively compared. We also verified the conjecture that parameters A and B are only related to the nature of the virus itself and the coexistence of the virus and the human body, but not related to the region where the disease occurs and evaluated and compared the epidemic prevention level of each outbreak region. Conclusion This paper established a theoretical model of infection transmission of COVID-19, which can predict the duration of the epidemic, the number of new infections per day, and evaluate the infectivity of the virus, immune escape ability, comprehensive infectivity, and regional epidemic prevention level. It can also give some suggestions on epidemic prevention countermeasures according to the possible parameter changes caused by virus variation. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.
ABSTRACT
Objective Many mutant strains of SARS-CoV-2 have stronger infectivity and immune escape ability. The situation of epidemic evaluation, prevention and control is serious. The aim of the present paper is to track and predict the infectious transmission of COVID-19 through a theoretical model. Methods Based on the grid epidemic model, this paper discussed the relationship between the duration of infection and the effect of group immunity, and on this basis, established the theoretical model of infection transmission of COVID-19. The infectivity parameter A and the immune effect parameter B are introduced to predict the daily variation curve of infection. The parameter A/B23 can be used to quantitatively compare the comprehensive infectivity of each mutant, and we also test the conjecture that the infection parameters A and B are not related to regional factors. Results Through the theoretical model of infection transmission of COVID-19, the infectious time was accurately predicted. By analyzing the infectivity and electrical changes of mutant strains, the internal relationship between the infectivity of mutant strains and the electrical changes of mutant residues was pointed out. The parameter changes of mutants were analyzed, and the comprehensive infectivity of each mutant was quantitatively compared. We also verified the conjecture that parameters A and B are only related to the nature of the virus itself and the coexistence of the virus and the human body, but not related to the region where the disease occurs and evaluated and compared the epidemic prevention level of each outbreak region. Conclusion This paper established a theoretical model of infection transmission of COVID-19, which can predict the duration of the epidemic, the number of new infections per day, and evaluate the infectivity of the virus, immune escape ability, comprehensive infectivity, and regional epidemic prevention level. It can also give some suggestions on epidemic prevention countermeasures according to the possible parameter changes caused by virus variation.