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Preserving privacy whilst maintaining robust epidemiological predictions.

Marleen Werkman1, Michael J Tildesley2, Ellen Brooks-Pollock3

  • 1WIDER Centre, Mathematics Institute and School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK; Current address: Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Marys Campus, Imperial College London, London, UK.

Epidemics
|October 30, 2016
PubMed
Summary

Mathematical models can predict epidemics using aggregated data, but accounting for farm size heterogeneity improves accuracy. This is crucial for effective disease control when detailed farm data is unavailable.

Keywords:
MetapopulationSimulationsSpatial aggregationStochastic model

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Veterinary Public Health

Background:

  • Mathematical models are essential for epidemic quantification and control strategy development.
  • Data limitations, such as lack of individual farm data due to privacy or capacity, often necessitate using aggregated data.

Purpose of the Study:

  • To systematically investigate the accuracy of mathematical model predictions using varying levels of data aggregation.
  • To assess the impact of incorporating farm size and composition heterogeneity into metapopulation models for disease spread prediction.

Main Methods:

  • Developed a metapopulation model for disease transmission using aggregated spatial grid cell data.
  • Adapted the metapopulation model to include farm census data, capturing heterogeneity in farm size and composition.
  • Utilized the UK 2001 Foot-and-Mouth Disease Epidemic as a case study to validate model predictions.

Main Results:

  • Homogeneous models using aggregated data tended to overestimate the final epidemic size but showed good performance in predicting spatial spread.
  • Incorporating heterogeneity in farm sizes significantly improved predictions of the final epidemic size.
  • Recognizing farm heterogeneity enhanced the identification of high-risk areas, epidemic take-off likelihood, and optimal control strategies.

Conclusions:

  • Mathematical models can provide meaningful predictions even with aggregated data, though careful interpretation is required.
  • Accounting for farm-level heterogeneity is critical for improving the accuracy of epidemic size predictions and optimizing control strategies.
  • These findings are vital for disease management planning when granular data is inaccessible.