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Article: Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009

TitlePros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009
Authors
KeywordsReferences (46) View In Table Layout
Issue Date2010
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.tbiomed.com/home/
Citation
Theoretical Biology And Medical Modelling, 2010, v. 7 n. 1 How to Cite?
AbstractBackground. In many parts of the world, the exponential growth rate of infections during the initial epidemic phase has been used to make statistical inferences on the reproduction number, R, a summary measure of the transmission potential for the novel influenza A (H1N1) 2009. The growth rate at the initial stage of the epidemic in Japan led to estimates for R in the range 2.0 to 2.6, capturing the intensity of the initial outbreak among school-age children in May 2009. Methods. An updated estimate of R that takes into account the epidemic data from 29 May to 14 July is provided. An age-structured renewal process is employed to capture the age-dependent transmission dynamics, jointly estimating the reproduction number, the age-dependent susceptibility and the relative contribution of imported cases to secondary transmission. Pitfalls in estimating epidemic growth rates are identified and used for scrutinizing and re-assessing the results of our earlier estimate of R. Results. Maximum likelihood estimates of R using the data from 29 May to 14 July ranged from 1.21 to 1.35. The next-generation matrix, based on our age-structured model, predicts that only 17.5% of the population will experience infection by the end of the first pandemic wave. Our earlier estimate of R did not fully capture the population-wide epidemic in quantifying the next-generation matrix from the estimated growth rate during the initial stage of the pandemic in Japan. Conclusions. In order to quantify R from the growth rate of cases, it is essential that the selected model captures the underlying transmission dynamics embedded in the data. Exploring additional epidemiological information will be useful for assessing the temporal dynamics. Although the simple concept of R is more easily grasped by the general public than that of the next-generation matrix, the matrix incorporating detailed information (e.g., age-specificity) is essential for reducing the levels of uncertainty in predictions and for assisting public health policymaking. Model-based prediction and policymaking are best described by sharing fundamental notions of heterogeneous risks of infection and death with non-experts to avoid potential confusion and/or possible misuse of modelling results. © 2010 Nishiura et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/134200
ISSN
2015 Impact Factor: 1.033
2015 SCImago Journal Rankings: 0.444
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
JST PRESTO program
Funding Information:

The work of H Nishiura was supported by the JST PRESTO program. The authors are grateful to the editor and the three reviewers for their useful comments, advices and expediting publication of this article.

References

 

DC FieldValueLanguage
dc.contributor.authorNishiura, Hen_HK
dc.contributor.authorChowell, Gen_HK
dc.contributor.authorSafan, Men_HK
dc.contributor.authorCastilloChavez, Cen_HK
dc.date.accessioned2011-06-13T07:20:47Z-
dc.date.available2011-06-13T07:20:47Z-
dc.date.issued2010en_HK
dc.identifier.citationTheoretical Biology And Medical Modelling, 2010, v. 7 n. 1en_HK
dc.identifier.issn1742-4682en_HK
dc.identifier.urihttp://hdl.handle.net/10722/134200-
dc.description.abstractBackground. In many parts of the world, the exponential growth rate of infections during the initial epidemic phase has been used to make statistical inferences on the reproduction number, R, a summary measure of the transmission potential for the novel influenza A (H1N1) 2009. The growth rate at the initial stage of the epidemic in Japan led to estimates for R in the range 2.0 to 2.6, capturing the intensity of the initial outbreak among school-age children in May 2009. Methods. An updated estimate of R that takes into account the epidemic data from 29 May to 14 July is provided. An age-structured renewal process is employed to capture the age-dependent transmission dynamics, jointly estimating the reproduction number, the age-dependent susceptibility and the relative contribution of imported cases to secondary transmission. Pitfalls in estimating epidemic growth rates are identified and used for scrutinizing and re-assessing the results of our earlier estimate of R. Results. Maximum likelihood estimates of R using the data from 29 May to 14 July ranged from 1.21 to 1.35. The next-generation matrix, based on our age-structured model, predicts that only 17.5% of the population will experience infection by the end of the first pandemic wave. Our earlier estimate of R did not fully capture the population-wide epidemic in quantifying the next-generation matrix from the estimated growth rate during the initial stage of the pandemic in Japan. Conclusions. In order to quantify R from the growth rate of cases, it is essential that the selected model captures the underlying transmission dynamics embedded in the data. Exploring additional epidemiological information will be useful for assessing the temporal dynamics. Although the simple concept of R is more easily grasped by the general public than that of the next-generation matrix, the matrix incorporating detailed information (e.g., age-specificity) is essential for reducing the levels of uncertainty in predictions and for assisting public health policymaking. Model-based prediction and policymaking are best described by sharing fundamental notions of heterogeneous risks of infection and death with non-experts to avoid potential confusion and/or possible misuse of modelling results. © 2010 Nishiura et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.tbiomed.com/home/en_HK
dc.relation.ispartofTheoretical Biology and Medical Modellingen_HK
dc.subjectReferences (46) View In Table Layouten_US
dc.titlePros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009en_HK
dc.typeArticleen_HK
dc.identifier.emailNishiura, H:nishiura@hku.hken_HK
dc.identifier.authorityNishiura, H=rp01488en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1186/1742-4682-7-1en_HK
dc.identifier.pmid20056004-
dc.identifier.pmcidPMC2821365-
dc.identifier.scopuseid_2-s2.0-77149155310en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77149155310&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue1en_HK
dc.identifier.isiWOS:000274486400001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridNishiura, H=7005501836en_HK
dc.identifier.scopusauthoridChowell, G=9845935500en_HK
dc.identifier.scopusauthoridSafan, M=14421859900en_HK
dc.identifier.scopusauthoridCastilloChavez, C=7003725806en_HK
dc.identifier.citeulike6501368-

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