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Article: Real-time forecasting of an epidemic using a discrete time stochastic model: A case study of pandemic influenza (H1N1-2009)

TitleReal-time forecasting of an epidemic using a discrete time stochastic model: A case study of pandemic influenza (H1N1-2009)
Authors
Issue Date2011
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedical-engineering-online.com
Citation
Biomedical Engineering Online, 2011, v. 10 How to Cite?
AbstractBackground: Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting.Methods: A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.Results: The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.Conclusions: Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance. © 2011 Nishiura; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/134184
ISSN
2015 Impact Factor: 1.382
2015 SCImago Journal Rankings: 0.531
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Japan Science and Technology Agency PRESTO
Funding Information:

HN is supported by the Japan Science and Technology Agency PRESTO program.

References

 

DC FieldValueLanguage
dc.contributor.authorNishiura, Hen_HK
dc.date.accessioned2011-06-13T07:20:43Z-
dc.date.available2011-06-13T07:20:43Z-
dc.date.issued2011en_HK
dc.identifier.citationBiomedical Engineering Online, 2011, v. 10en_HK
dc.identifier.issn1475-925Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/134184-
dc.description.abstractBackground: Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting.Methods: A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.Results: The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.Conclusions: Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance. © 2011 Nishiura; licensee BioMed Central Ltd.en_HK
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedical-engineering-online.comen_HK
dc.relation.ispartofBioMedical Engineering Onlineen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsBioMedical Engineering OnLine. Copyright © BioMed Central Ltd.-
dc.subject.meshForecasting - methods-
dc.subject.meshInfluenza A Virus, H1N1 Subtype - physiology-
dc.subject.meshInfluenza, Human - epidemiology - transmission - virology-
dc.subject.meshPandemics-
dc.subject.meshStochastic Processes-
dc.titleReal-time forecasting of an epidemic using a discrete time stochastic model: A case study of pandemic influenza (H1N1-2009)en_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1475-925X&volume=10 article no. 15&spage=article 15&epage=&date=2011&atitle=Real-time+forecasting+of+an+epidemic+using+a+discrete+time+stochastic+model:+a+case+study+of+pandemic+influenza+(H1N1-2009)-
dc.identifier.emailNishiura, H:nishiura@hku.hken_HK
dc.identifier.authorityNishiura, H=rp01488en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1186/1475-925X-10-15en_HK
dc.identifier.pmid21324153en_HK
dc.identifier.pmcidPMC3045989-
dc.identifier.scopuseid_2-s2.0-79951557043en_HK
dc.identifier.hkuros185315-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79951557043&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.isiWOS:000287976200001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridNishiura, H=7005501836en_HK
dc.identifier.citeulike8829207-

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