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
Max S Y LauBryan T Grenfell

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 ...Continue Reading

References

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Feb 15, 2017·Proceedings of the National Academy of Sciences of the United States of America·Max S Y LauBryan T Grenfell
Aug 24, 2017·The Journal of Infectious Diseases·Gerardo ChowellCecile Viboud

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Citations

Nov 22, 2019·PLoS Neglected Tropical Diseases·Gerardo ChowellMaria Kiskowski
Jun 10, 2018·Nature Communications·Simon DellicourPhilippe Lemey
Aug 21, 2020·Proceedings of the National Academy of Sciences of the United States of America·Max S Y LauBen Lopman

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Software Mentioned

R package spatstat

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