Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 495899
Title Epidemic predictions in an imperfect world : Modelling disease spread with partial data
Author(s) Dawson, P.M.; Werkman, Marleen; Brooks-Pollock, Ellen; Tildesley, M.J.
Source Proceedings of the Royal Society. B: Biological Sciences 282 (2015)1808. - ISSN 0962-8452 - 9 p.
DOI http://dx.doi.org/10.1098/rspb.2015.0205
Department(s) CVI Diagnostics and Crisis
CVI Bacteriology and Epidemiology
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) Epidemics - Livestock networks - Partial datav
Abstract

‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around theworld, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

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