Epidemic predictions in an imperfect world: modelling disease spread with partial data

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 the world, 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.

DOI: 10.1098/rspb.2015.0205

Extracted Key Phrases

3 Figures and Tables

0204020162017
Citations per Year

Citation Velocity: 10

Averaging 10 citations per year over the last 2 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@inproceedings{Dawson2015EpidemicPI, title={Epidemic predictions in an imperfect world: modelling disease spread with partial data}, author={Peter M. Dawson and Marleen Werkman and Ellen Brooks-Pollock and Michael J. Tildesley}, booktitle={Proceedings. Biological sciences}, year={2015} }