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In this paper, we build a mechanistic system to understand the relationbetween a reduction in human mobility and Covid-19 spread dynamics within NewYork City. To this end, we propose a multivariate compartmental model thatjointly models smartphone mobility data and case counts during the first 90days of the epidemic. Parameter calibration is achieved through the formulationof a general Bayesian hierarchical model to provide uncertainty quantificationof resulting estimates. The open-source probabilistic programming language Stanis used for the requisite computation. Through sensitivity analysis andout-of-sample forecasting, we find our simple and interpretable model providesevidence that reductions in human mobility altered case dynamics.

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