ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the worldwide spread of coronavirus disease-2019 (COVID-19) since its emergence in 2019. Virus replication and infection dynamics after its deposition on the respiratory tissues require detailed studies for infection control. This study focused primarily on SARS-CoV-2 dynamics in the mucus layer of the nasal cavity and nasopharynx, based on coupled computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) analyses. Considering the mucus milieu, we coupled the target-cell limited model with the convection-diffusion term to develop an improved HCD model. The infection dynamics in the mucus layer were predicted by a combination of the mucus flow field, droplet deposition distribution, and HCD. The effect of infection rate, β, was investigated as the main parameter of HCD. The results showed that the time series of SARS-CoV-2 concentration distribution in the mucus layer strongly depended on diffusion, convection, and virus production. β affected the viral load peak, its arrival time, and duration. Although the SARS-CoV-2 dynamics in the mucus layer obtained in this study have not been verified by appropriate clinical data, it can serve as a preliminary study on the virus transmission mode in the upper respiratory tract. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)
ABSTRACT
Many epidemic modeling studies rely on the common assumption that the disease transmission rate between individuals is constant. However, in reality, transmission rates depend on the time-varying viral load of the infected individual. The time-dependent transmission rate has the potential to affect the spread of an epidemic. In this study, the influenza and SARS-CoV-2 transmission rate profiles were developed based on the viral load of infected individuals and dose-response curves. In addition, a new epidemic model, the multi-infectious stage edge-based compartment model, was proposed to apply the transmission rate profile to epidemic dynamics in both static and temporal networks. It was determined that in terms of the final epidemic size there is no discrepancy between the constant and time-dependent transmission rates in the static network. However, the time at which the infected fraction peaks, and the peak infection fraction are dependent on the transmission rate profile. However, in temporal networks, the final epidemic size for the constant transmission rate is higher than that for the time-dependent transmission rate. In conclusion, the time-dependent transmission rate strongly affects the epidemic dynamics.