  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.
