Estimation of local time-varying reproduction numbers in noisy surveillance data.
Philos Trans A Math Phys Eng Sci
; 380(2233): 20210303, 2022 Oct 03.
Article
en En
| MEDLINE
| ID: mdl-35965456
ABSTRACT
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedades Transmisibles
/
COVID-19
Tipo de estudio:
Prognostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Philos Trans A Math Phys Eng Sci
Asunto de la revista:
BIOFISICA
/
ENGENHARIA BIOMEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos