Effective reproduction numbers are commonly overestimated early in a disease outbreak
Identifieur interne : 000509 ( Istex/Corpus ); précédent : 000508; suivant : 000510Effective reproduction numbers are commonly overestimated early in a disease outbreak
Auteurs : G. N. Mercer ; K. Glass ; N. G. BeckerSource :
- Statistics in Medicine [ 0277-6715 ] ; 2011-04-30.
English descriptors
- Teeft :
- American journal, Average number, Becker, Case ascertainment, Control measures, Copyright, Daily case numbers, Data sets, Disease incidence data, Disease outbreak, Early estimates, Early stages, Effective reproduction number, Effective reproduction numbers, Estimation, Estimation methods, Euro, Euro surveillance, Exponential growth rate, Greater overestimation, Importation, Importation status, Individual infects, Infectious disease, Infectious diseases, Infectious period, Infectious periods, Initial cases, Intervention measures, John wiley sons, June, Latent period, Local cases, Local transmission, Major outbreak, Many factors, National centre, Nishiura, Other factors, Outbreak, Overestimation, Pandemic, Pandemic outbreak, Policy decisions, Population health, Population size, Posterior probability distribution, Process model, Real data, Real time, Reproduction, Reproduction number, Reproduction numbers, Royal society interface, Secondary cases, Serial interval, Serial interval distribution, Simulation, Simulation data, Small number, Solid line, Statist, Stochastic version, Subpopulation, Successful outbreak, Time period, Transmission dynamics, Western australia.
Abstract
Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data. Copyright © 2011 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/sim.4174
Links to Exploration step
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<p>Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data. Copyright © 2011 John Wiley & Sons, Ltd.</p>
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<p>Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data. Copyright © 2011 John Wiley & Sons, Ltd.</p>
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<abstract lang="en">Reproduction numbers estimated from disease incidence data can give public health authorities valuable information about the progression and likely size of a disease outbreak. Here, we show that methods for estimating effective reproduction numbers commonly give overestimates early in an outbreak. This is due to many factors including the nature of outbreaks that are used for estimation, incorrectly accounting for imported cases and outbreaks arising in subpopulations with higher transmission rates. Awareness of this bias is necessary to correctly interpret estimates from early disease outbreak data. Copyright © 2011 John Wiley & Sons, Ltd.</abstract>
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