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

Abstract

The ongoing pandemic of 2019-nCov (COVID-19) coronavirus has made reliable epidemiological modeling an urgent necessity. Unfortunately, most of the existing models are either too fine-grained to be efficient or too coarse-grained to be reliable. Here we propose a computationally efficient hybrid approach that uses SIR model for individual cities which are in turn coupled via empirical transportation networks that facilitate migration among them. The treatment presented here differs from existing models in two crucial ways: first, self-consistent determination of coupling parameters so as to maintain the populations of individual cities, and second, the incorporation of distance dependent temporal delays in migration. We apply our model to Indian aviation as well as railway networks taking into account populations of more than 300 cities. Our results project that through the domestic transportation, the significant population is poised to be exposed within 90 days of the onset of epidemic. Thus, serious supervision of domestic transport networks is warranted even after restricting international migration.