Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India

Bhalchandra S Pujari; Snehal M Shekatkar

Here we introduce a new technique to main-tain the desired population of a city via selfconsistent parametrisation. In this approach, disease dynamics inside individual cities are treated with SIR model that accounts for the intra-city mixing. On the other hand, intercity migration is modeled by spatially realistic networks of airports and train stations. Put differently, the "fully-mixed" approximation is assumed to be valid inside cities but not between the cities. Another notable feature of the scheme proposed here is the introduction of distancedependent delay inherent in the transportation. Additionally, the model is also capable of simultaneously handling cities with different populations. This is achieved by scaling the product terms inside the SIR equations by respective populations as discussed in the next section. As an application to ongoing COVID-19 outbreak, we run our model on aviation and rail networks of India where links connect multiple cities of varying population sizes.