A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US.
Shasha Gao, Mingwang Shen, Xueying Wang, Jin Wang, Maia Martcheva, Libin Rong
Author Information
Shasha Gao: School of Mathematics and Statistics, Jiangxi Normal University, Nanchang, 330000, China; Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America.
Mingwang Shen: China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
Xueying Wang: Department of Mathematics and Statistics, Washington State University, Pullman, WA 99163, United States of America.
Jin Wang: Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States of America.
Maia Martcheva: Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America.
Libin Rong: Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America. Electronic address: libinrong@ufl.edu.
COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.
