Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0001941
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dc.titleMeasles on the edge: Coastal heterogeneities and infection dynamics
dc.contributor.authorBharti, N.
dc.contributor.authorXia, Y.
dc.contributor.authorBjornstad, O.N.
dc.contributor.authorGrenfell, B.T.
dc.date.accessioned2014-10-28T05:13:05Z
dc.date.available2014-10-28T05:13:05Z
dc.date.issued2008-04-09
dc.identifier.citationBharti, N., Xia, Y., Bjornstad, O.N., Grenfell, B.T. (2008-04-09). Measles on the edge: Coastal heterogeneities and infection dynamics. PLoS ONE 3 (4) : -. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0001941
dc.identifier.issn19326203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105217
dc.description.abstractMathematical models can help elucidate the spatio-temporal dynamics of epidemics as well as the impact of control measures. The gravity model for directly transmitted diseases is currently one of the most parsimonious models for spatial epidermic spread. This model uses distance-weighted, population size-dependent coupling to estimate host movement and disease incidence in metapopulations. The model captures overall measles dynamics in terms of underlying human movement in pre-vaccination England and Wales (previously established). In spatial models, edges often present a special challenge. Therefore, to test the model's robustness, we analyzed gravity model incidence predictions for coast cities in England and Wales. Results show that, although predictions are accurae for inland towns, they significantly underestimate coastal presistence. We examine incidence, outbreak seasonality, and public transportation records, to show that the model's inaccuracies stern from an underestimation of total contacts per individual along the coast. We rescue this predicted 'edge effect' by increasing coastal contacts to approximate the number of per capita inland contacts. These result illustrate the impact of 'edge effects' on epidemic metapopulations in general and illustrate directions for the refinement of spatiotemporal epidemic models. © 2008 Bharti et al.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1371/journal.pone.0001941
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1371/journal.pone.0001941
dc.description.sourcetitlePLoS ONE
dc.description.volume3
dc.description.issue4
dc.description.page-
dc.identifier.isiut000260795500018
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