ABSTRACT
In the age of digitalization, predicting outcomes of a viral infection is a major goal to policymakers. Traditional methods are statistical analysis of prior epidemic events tied into on-the-fly analysis of an ongoing epidemic. An alternative way is simulating the present conditions using physical parameters that represent different scenarios and using those to predict the outcomes with and without external intervention. We propose to modify a well-known particle diffusion model used for modeling viral spread to include different mobility within one city, travel between cities, as well as health interventions to contain the COVID-19 epidemic. Preliminary results of including different mobility within a city have shown that under extreme cases of high vs low-mobility, the low-mobility side of the city will have the largest number of cases, although counter-intuitive one can explain it as in the low-mobility case the individuals will spend more time together, enhancing the probability of infection. In this chapter, we will modify the original model, determine if known interventions had the desired effects, and use the results to establish new policies.
