A modelling approach for correcting reporting delays in disease surveillance data.
Bayesian hierarchical model
INLA
SARI
dengue
reporting delay
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 09 2019
30 09 2019
Historique:
received:
15
05
2018
revised:
13
05
2019
accepted:
03
06
2019
pubmed:
12
7
2019
medline:
16
1
2021
entrez:
12
7
2019
Statut:
ppublish
Résumé
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
Identifiants
pubmed: 31292995
doi: 10.1002/sim.8303
pmc: PMC6900153
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4363-4377Informations de copyright
© 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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