A Compartment Model of Human Mobility and Early Covid-19 Dynamics in NYC
Volume 1, Issue 1 (2023), pp. 110–121
Pub. online: 4 January 2022
Type: Statistical Methodology
Open Access
Open Access
Accepted
20 June 2021
20 June 2021
Published
4 January 2022
4 January 2022
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 system 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 statistical-mechanistic Bayesian hierarchical model. 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 quantifiable evidence for how reductions in human mobility altered early case dynamics in New York City.
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