Abstract:

A functional relationship was constructed between the probability of inhaling viruses and social distance to characterize the viral transmission of subway passengers at the microscopic level. Formulas for calculating the increase and decrease of viral load were constructed based on establishing the viral load evolution equation. Normalized parameters were used within this equation to describe the effect of pandemic prevention measures. The viral load of each passenger was programmed through the Anylogic software’s secondary development interface to characterize the viral load change at the pre- and post-infection phases. In the initial simulation settings, 10% of the passengers were infected with the virus, including ordinary carriers and supercarriers. The evolution of the virus under different passenger number conditions within subway carriages was simulated, which was categorized into with-control and without-control scenarios. The simulation results showed the following: as the number of passengers increases, the passenger density increases, the virus transmission increases, and the individual viral load increases rapidly. Isolating passengers with a viral load greater than a threshold of 1 000 and prohibiting them from taking the subway can reduce the viral load of all passengers by an order of magnitude.

Key words: urban transportation, subway passengers, viral load, pandemic control, evolutionary model, Anylogic simulation