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
in English
| MEDLINE | ID: covidwho-1992461
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'.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Communicable Diseases
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Philos Trans A Math Phys Eng Sci
Journal subject:
Biophysics
/
Biomedical Engineering
Year:
2022
Document Type:
Article
Affiliation country:
Rsta.2021.0303
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