Modeling the spread of COVID-19 in New York City

Olmo, J. ; Sanso-Navarro, M. (Universidad de Zaragoza)

Resumen: 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. © 2021 The Authors. Papers in Regional Science published by John Wiley & Sons Ltd on behalf of Regional Science Association International.

Idioma: Inglés

DOI: 10.1111/pirs.12615

Año: 2021

Publicado en: Papers in Regional Science 100, 5 (2021), 1209-1229
ISSN: 1056-8190

Factor impacto JCR: 2.186 (2021)

Categ. JCR: ECONOMICS rank: 182 / 381 = 0.478 (2021) - Q2 - T2
Categ. JCR: GEOGRAPHY rank: 52 / 86 = 0.605 (2021) - Q3 - T2
Categ. JCR: REGIONAL & URBAN PLANNING rank: 32 / 40 = 0.8 (2021) - Q4 - T3
Categ. JCR: ENVIRONMENTAL STUDIES rank: 101 / 128 = 0.789 (2021) - Q4 - T3

Factor impacto CITESCORE: 4.7 - Social Sciences (Q1) - Environmental Science (Q2)

Factor impacto SCIMAGO: 0.873 - Geography, Planning and Development (Q1) - Environmental Science (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/S16-ADETRE
Financiación: info:eu-repo/grantAgreement/ES/DGA/S39-20R

Tipo y forma: Article (Published version)

Área (Departamento): Área Fund. Análisis Económico (Dpto. Análisis Económico)