期刊
PLOS COMPUTATIONAL BIOLOGY
卷 13, 期 10, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005798
关键词
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资金
- Bill & Melinda Gates Foundation [OPP1091919]
- RAPIDD programme of the Science and Technology Directorate Department of Homeland Security
- Fogarty International Centre, National Institutes of Health (NIH)
- UK Medical Research Council (MRC)
- European Food Safety Authority [OC/EFSA/AHAW/2013/01 - CT01]
- MRC [MR/K021680/1]
- Medical Research Council [MR/K010174/1B, MR/K021680/1] Funding Source: researchfish
- National Institute for Health Research [HPRU-2012-10080] Funding Source: researchfish
- MRC [MR/K021680/1] Funding Source: UKRI
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.
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