چکیده (انگلیسی):

Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal con-trol strategies in case of an outbreak. State-of-the-art models increasingly require detailed individualfarm-based and sensitive data, which may not be available due to either lack of capacity for data collec-tion or privacy concerns. However, in many situations, aggregated data are available for use. In thisstudy, we systematically investigate the accuracy of predictions made by mathematical models ini-tialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a casestudy. We consider the scenario when the only data available are aggregated into spatial grid cells, anddevelop a metapopulation model where individual farms in a single subpopulation are assumed to behaveuniformly and transmit randomly. We also adapt this standard metapopulation model to capture het-erogeneity in farm size and composition, using farm census data. Our results show that homogeneousmodels based on aggregated data overestimate final epidemic size but can perform well for predictingspatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size,identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal con-trol strategy. In conclusion, in cases where individual farm-based data are not available, models can stillgenerate meaningful predictions, although care must be taken in their interpretation and use.© 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).