Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76176
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dc.contributorDepartment of Applied Mathematics-
dc.creatorYu, D-
dc.creatorLin, QY-
dc.creatorChiu, APY-
dc.creatorHe, DH-
dc.date.accessioned2018-05-10T02:55:30Z-
dc.date.available2018-05-10T02:55:30Z-
dc.identifier.urihttp://hdl.handle.net/10397/76176-
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.rights© 2017 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rightsThe following publication: Yu D, Lin Q, Chiu AP, He D (2017) Effects of reactive social distancing on the 1918 influenza pandemic. PLoS ONE 12(7): e0180545 is available at https://doi.org/10.1371/journal.pone.0180545en_US
dc.titleEffects of reactive social distancing on the 1918 influenza pandemicen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume12en_US
dc.identifier.issue7en_US
dc.identifier.doi10.1371/journal.pone.0180545en_US
dcterms.abstractThe 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson's correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationPLoS one, 2017, v. 12, no. 7, e0180545-
dcterms.isPartOfPLoS one-
dcterms.issued2017-
dc.identifier.isiWOS:000405649600033-
dc.identifier.pmid28704460-
dc.identifier.eissn1932-6203en_US
dc.identifier.artne0180545en_US
dc.identifier.rosgroupid2017002191-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201810_a bcma; 201805 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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