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Vote-processing rules for combining control recommendations from multiple models.

William J M Probert1, Sam Nicol2, Matthew J Ferrari3,4

  • 1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

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PubMed
Summary
This summary is machine-generated.

Combining disease outbreak models is challenging. This study uses electoral vote-processing rules to rank interventions, offering a more robust method than combining projections, especially for comparing control strategies.

Keywords:
decision-makingepidemiologymanagementmultiple modelsvoting

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Mathematical models are crucial for comparing disease outbreak control interventions.
  • Current methods for combining multiple model outputs often assume direct comparability of projections, which may not hold true due to differing model structures.
  • The optimal approach for synthesizing recommendations from diverse epidemiological models remains unclear.

Purpose of the Study:

  • To investigate the application of electoral vote-processing rules for combining rankings of control interventions from multiple disease outbreak models.
  • To compare the performance of different voting rules (First-past-the-post, Alternative Vote, Coombs Method, Borda Count) in synthesizing model-based recommendations.
  • To assess the robustness of these methods when faced with models exhibiting biased or random rankings.

Main Methods:

  • Applied four electoral vote-processing rules to combine intervention rankings generated by individual epidemiological models.
  • Evaluated rule sensitivity using simulated models that favored single actions or ranked interventions randomly.
  • Tested the methodology on two case studies: the 2014 Ebola outbreak in West Africa and a hypothetical foot-and-mouth disease outbreak in the UK.

Main Results:

  • The Coombs Method demonstrated the least susceptibility to models favoring a single intervention.
  • Borda Count and Alternative Vote (AV) were most sensitive to the inclusion of randomly ranking models.
  • All tested voting rules selected the same top intervention as traditional mean projection ranking, but with fewer assumptions.

Conclusions:

  • Combining model-generated intervention rankings using electoral vote-processing rules offers a viable alternative to combining projections.
  • This ranking-based approach reduces assumptions about the direct comparability of model outputs.
  • The Coombs Method appears to be a robust choice for synthesizing intervention recommendations from diverse epidemiological models.