A random walk model of social distancing to mitigate COVID-19 spread

We introduce a modified SIRD (Susceptible-Infected-Recovered-Deceased) model and study the impact of social distancing for interacting random walkers in two dimensional continuum using Monte Carlo methods. We use both fixed step length walkers as well as variable step length walkers drawn from a Gaussian distribution for different vulnerability factors, population density, inter-city/country traffic, and a variety of possible scenario based on COVID-19 data at various locations, and study to what extent social distancing would mitigate the spread of the disease. Our model can be used to understand how COVID-19 would have impacted at a reduced degree if social distancing were maintained more rigidly, and suggests measures to be taken to resists spread of such disease before it becomes a pandemic.