Capturing the COVID-19 pandemic characteristics with DSMC method.

The SARS-COV-2 virus had infected over 600 million people, and this global pandemic seems endless. The individual interaction affects the virus transmission but is ignored in most reported prediction models. In this paper, we build a Susceptible-Exposed-Infected-Recovered (SEIR) model to include the individual interactions with the direct simulation Monte Carlo (DSMC) method. The classical pandemic model is a series of ordinary differential equations with constant parameters. In the real world, as viruses proliferate in the human body, the critical parameter of the pandemic is time-varying. In this state, the result from the present DSMC method shows the time to reach the peak number of patients is linearly related to the infectious period, which is beyond the capability of the traditional deterministic model. This study provides several valuable results which are potentially useful to the centers for disease control and prevention (CDC) in the future. [ABSTRACT FROM AUTHOR]