A Compartment Model of Human Mobility and Early Covid-19 Dynamics in NYC

In this paper, we build a mechanistic system to understand the relation between a reduction in human mobility and Covid-19 spread dynamics within New York City. To this end, we propose a multivariate compartmental model that jointly models smartphone mobility data and case counts during the first 90 days of the epidemic. Parameter calibration is achieved through the formulation of a general Bayesian hierarchical model to provide uncertainty quantification of resulting estimates. The open-source probabilistic programming language Stan is used for the requisite computation. With the additional benefit of modeling a mechanism by which human mobility and infection counts relate, we find our simple and interpretable model is able to recover epidemiological parameters that are consistent with current literature.