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A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak

Author(s): Lau, Max S.Y.; Gibson, Gavin J.; Adrakey, Hola; McClelland, Amanda; Riley, Steven; et al

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dc.contributor.authorLau, Max S.Y.-
dc.contributor.authorGibson, Gavin J.-
dc.contributor.authorAdrakey, Hola-
dc.contributor.authorMcClelland, Amanda-
dc.contributor.authorRiley, Steven-
dc.contributor.authorZelner, Jon-
dc.contributor.authorStreftaris, George-
dc.contributor.authorFunk, Sebastian-
dc.contributor.authorMetcalf, C. Jessica E.-
dc.contributor.authorDalziel, Benjamin D.-
dc.contributor.authorGrenfell, Bryan T.-
dc.date.accessioned2019-12-16T17:46:44Z-
dc.date.available2019-12-16T17:46:44Z-
dc.date.issued2017-10-30en_US
dc.identifier.citationLau, Max SY, Gibson, Gavin J, Adrakey, Hola, McClelland, Amanda, Riley, Steven, Zelner, Jon, Streftaris, George, Funk, Sebastian, Metcalf, Jessica, Dalziel, Benjamin D, Grenfell, Bryan T. (2017). A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak. PLOS Computational Biology, 13 (10), e1005798 - e1005798. doi:10.1371/journal.pcbi.1005798en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr14x92-
dc.description.abstractIn recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.en_US
dc.format.extente1005798 - e1005798en_US
dc.language.isoenen_US
dc.relation.ispartofPLOS Computational Biologyen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleA mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreaken_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1371/journal.pcbi.1005798-
dc.date.eissued2017-10-30en_US
dc.identifier.eissn1553-7358-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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