|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Reconstructing disease transmission dynamics from animal movements and test data|
|Author(s):||Enright, Jessica A|
|Citation:||O'Hare A & Enright J (2017) Reconstructing disease transmission dynamics from animal movements and test data, Stochastic Environmental Research and Risk Assessment, 31 (2), pp. 369-377. https://doi.org/10.1007/s00477-016-1354-z.|
|Abstract:||Disease outbreaks are often accompanied by a wealth of data, usually in the form of movements, locations and tests. This data is a valuable resource in which data scientists and epidemiologists can reconstruct the transmission pathways and parameters and thus devise control strategies. However, the spatiotemporal data gathered can be both vast whilst at the same time incomplete or contain errors frustrating the effort to accurately model the transmission processes. Fortunately, several techniques exist that can be used to infer the relevant information to help explain these processes. The aim of this article is to provide the reader with a user friendly introduction to the techniques used in dealing with the large datasets that exists in epidemiological and ecological science and the common pitfalls that are to be avoided as well as an introduction to inference techniques for estimating parameter values for mathematical models from spatiotemporal datasets.|
|Rights:||This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.|
|Enright_OHare_SERRA_2017.pdf||Fulltext - Published Version||1.22 MB||Adobe PDF||View/Open|
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