Bayesian time-varying autoregressive models of COVID-19 epidemics.
Biom J
; 65(1): e2200054, 2023 01.
Article
em En
| MEDLINE
| ID: mdl-35876399
ABSTRACT
The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
COVID-19
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Biom J
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Itália