Modeling the spread of COVID‐19 in New York City
Modeling the spread of COVID‐19 in New York City
This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-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 in-sample and out-of-sample.
Bayesian model averaging, COVID-19, Poisson regression, prediction models, spatial effects
1209-1229
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Sanso‐navarro, Marcos
39ed49fd-2d29-4763-898b-9117bf977956
11 May 2021
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Sanso‐navarro, Marcos
39ed49fd-2d29-4763-898b-9117bf977956
Olmo, Jose and Sanso‐navarro, Marcos
(2021)
Modeling the spread of COVID‐19 in New York City.
Papers in Regional Science, 100 (5), .
(doi:10.1111/pirs.12615).
Abstract
This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-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 in-sample and out-of-sample.
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OlmoSansoPiRS_final
- Accepted Manuscript
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OnlineAppendixPIRS_final
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More information
Accepted/In Press date: 6 May 2021
e-pub ahead of print date: 11 May 2021
Published date: 11 May 2021
Additional Information:
Funding Information:
The authors acknowledge financial support from Gobierno de Aragón (ADETRE Research Group, Grant S39‐20R). Jose Olmo acknowledges financial support from Fundación Agencia Aragonesa para la Investigación y el Desarrollo (ARAID).
Funding Information:
The authors acknowledge financial support from Gobierno de Arag?n (ADETRE Research Group, Grant S39-20R). Jose Olmo acknowledges financial support from Fundaci?n Agencia Aragonesa para la Investigaci?n y el Desarrollo (ARAID).
Publisher Copyright:
© 2021 The Authors. Papers in Regional Science published by John Wiley & Sons Ltd on behalf of Regional Science Association International.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
Bayesian model averaging, COVID-19, Poisson regression, prediction models, spatial effects
Identifiers
Local EPrints ID: 449453
URI: http://eprints.soton.ac.uk/id/eprint/449453
ISSN: 1056-8190
PURE UUID: 10b4bc51-a442-466e-9041-9b7db94fd9d9
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Date deposited: 01 Jun 2021 16:31
Last modified: 17 Mar 2024 06:36
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Author:
Marcos Sanso‐navarro
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