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

TL;DR

This study develops a compartmental model linking human mobility reductions to Covid-19 spread in NYC, using smartphone data and Bayesian calibration to quantify uncertainty and assess impact.

Contribution

It introduces a multivariate compartmental model integrating mobility data with case counts, employing Bayesian methods for parameter estimation and uncertainty quantification.

Findings

Mobility reductions significantly impacted Covid-19 case dynamics.

The model accurately captures early epidemic trends in NYC.

Uncertainty quantification enhances understanding of mobility's role.

Abstract

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. Through sensitivity analysis and out-of-sample forecasting, we find our simple and interpretable model provides evidence that reductions in human mobility altered case dynamics.