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dc:abstract "In 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." ;
dc:creator "['Lau, Max S. Y.', 'Gibson, Gavin J.', 'Adrakey, Hola', 'McClelland, Amanda', 'Riley, Steven', 'Zelner, Jon', 'Streftaris, George', 'Funk, Sebastian', 'Metcalf, Jessica', 'Dalziel, Benjamin D.', 'Grenfell, Bryan T.']" ;
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dc:issued "2017-01-01"^^xsd:date ;
dc:license "CC BY" ;
dc:title "A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak" ;
sso:has_full_text "False" ;
sso:journal "PLoS Comput Biol" ;
sso:sha "900e41d6d6ed985556b74bdf32ff59f782a53eaa" ;
sso:source_x "PMC" .