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Article: Forecasting Influenza Epidemics in Hong Kong

TitleForecasting Influenza Epidemics in Hong Kong
Authors
Issue Date2015
Citation
PLoS Computational Biology, 2015, v. 11 n. 7, article no. e1004383 How to Cite?
AbstractRecent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A (H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.
Persistent Identifierhttp://hdl.handle.net/10722/216033
ISSN
2015 Impact Factor: 4.587
2015 SCImago Journal Rankings: 3.405
ISI Accession Number ID
Grants

 

DC FieldValueLanguage
dc.contributor.authorYang, W-
dc.contributor.authorCowling, BJ-
dc.contributor.authorLau, EHY-
dc.contributor.authorShaman, J-
dc.date.accessioned2015-08-21T13:50:04Z-
dc.date.available2015-08-21T13:50:04Z-
dc.date.issued2015-
dc.identifier.citationPLoS Computational Biology, 2015, v. 11 n. 7, article no. e1004383-
dc.identifier.issn1553-734X-
dc.identifier.urihttp://hdl.handle.net/10722/216033-
dc.description.abstractRecent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A (H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.-
dc.languageeng-
dc.relation.ispartofPLoS Computational Biology-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleForecasting Influenza Epidemics in Hong Kong-
dc.typeArticle-
dc.identifier.emailCowling, BJ: bcowling@hku.hk-
dc.identifier.emailLau, EHY: ehylau@hku.hk-
dc.identifier.authorityCowling, BJ=rp01326-
dc.identifier.authorityLau, EHY=rp01349-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pcbi.1004383-
dc.identifier.pmid26226185-
dc.identifier.hkuros248984-
dc.identifier.volume11-
dc.identifier.issue7-
dc.identifier.isiWOS:000360620100032-
dc.relation.projectControl of Pandemic and Inter-pandemic Influenza-

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