Author: Olmo, Jose; Sansoâ€Navarro, Marcos
Title: Modeling the spread of COVIDâ€19 in New York City Cord-id: 8a1bdqoh Document date: 2021_6_28
ID: 8a1bdqoh
Snippet: This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVIDâ€19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of n
Document: This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVIDâ€19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability inâ€sample and outâ€ofâ€sample.
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