Using Particle Diffusion to Study the Spread of Viral Infection

The appearance of the coronavirus (COVID-19) in late 2019 has dominated the news in the last few months as it developed into a pandemic. In many mathematics and physics classrooms, instructors are using the time series of the number of cases to show exponential growth of the
infection. Initially we proposed to use a simple diffusion process as the mode of spreading infections to be implemented as a classroom project in computational physics courses. This model is less sophisticated than other models in the literature, but it can capture the exponential growth and it can explain it in terms of mobility (diffusion constant), population density, and probability of transmission. An account of this pedagogical application was recently published in (Am. J. Phys. 88, 604 (2020)). In this presentation we modify the initial program to include mitigation effects, such as social distancing and use of masks, as well as the effects of inter-city/neighborhood travel on the spread of the virus. Showing that one can change the caseload and explain how pockets that were initially case free can become hotspots.