Bayesian nonparametric inference for heterogeneously mixing infectious disease models.
Proc Natl Acad Sci U S A
; 119(10): e2118425119, 2022 03 08.
Article
em En
| MEDLINE
| ID: mdl-35238628
ABSTRACT
SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Transmissíveis
/
Teorema de Bayes
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2022
Tipo de documento:
Article