How risky is it to visit a supermarket during the pandemic?

Virus transmission risk of college students in railway station during Post-COVID-19 era: Combining the social force model and the virus transmission model

In the post-epidemic era, people's lives are gradually returning to normal, and travel is gradually resuming. The safe evacuation of cross-regional travelers in railway station has also attracted more and more attention, especially the evacuation behavior of college students in railway station. In this paper, considering the pedestrian dynamics mechanism in the emergency evacuation process during the COVID-19 normalized epidemic prevention and control, an Agent-based social force model was established to simulate the activities of college students in railway station. Combined with the virus infection transmission model, Monte Carlo simulation was used to calculate the total exposure time and the number of high-risk exposed people in the railway station evacuation process. In addition, sensitivity analysis was conducted on the total exposure time and the number of high-risk exposed people under 180 combinations of the number of initial infections, social distance, and the proportion of people wearing masks incorrectly. The results show that with the increase of social distances, the total exposure time and the number of high-risk exposures do not always decrease, but increase in some cases. The presence or absence of obstacles in the evacuation scene has no significant difference in the effects on total exposure time and the number of high-risk exposures. During the evacuation behavior of college students in railway station, choosing the appropriate number of lines can effectively reduce the total exposure time and the number of high-risk exposures. Finally, some policy suggestions are proposed to reduce the risk of virus transmission in the railway station evacuation process, such as choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles.

An agent-based study on the airborne transmission risk of infectious disease in a fever clinic during COVID-19 pandemic

Prevention of nosocomial infections is particularly important for the control of COVID-19 pandemic. We conducted a field study and performed extensive numerical simulations of infection transmission in a fever clinic during pandemic through an agent-based model with pedestrian dynamic and an infection transmission model. Furthermore, we evaluated the cross-infection risk of the patients influenced by the patient inject flow, medical service capability and plane layout. The service capability of fever clinic is determined by the least efficient medical session. When patient inject flow exceeded the service capability, the average dwell time, contact time, exposure dose, and risk of infection of patients all increased dramatically. With the patient inject flow exceeding the service capability, the growth rate of the contact time between patients and the cross-infection risk increased by 11.5-fold and 29.5-fold, respectively. The plane layout of the fever clinic affected the exposure dose and risk of infection. The waiting areas in the fever clinic had the highest risk, where the cumulative exposure dose of virus occupied up to 66.5% of the total. Our research will help to evaluate the biosafety of hospital buildings used for the diagnosis and treatment of infectious diseases.