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• W3127076166 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." @default.
• W3127076166 created "2021-02-15" @default.
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• W3127076166 date "2021-01-01" @default.
• W3127076166 modified "2023-10-14" @default.
• W3127076166 title "A Compartment Model of Human Mobility and Early Covid-19 Dynamics in NYC" @default.
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• W3127076166 doi "https://doi.org/10.51387/21-nejsds2" @default.
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