Vote-processing rules for combining control recommendations from multiple models. (3rd October 2022)

• Record Type: Journal Article
• Authors: Probert, William J. M.; Nicol, Sam; Ferrari, Matthew J.; Li, Shou-Li; Shea, Katriona; Tildesley, Michael J.; Runge, Michael C.
• Is Part Of: Philosophical transactions. Volume 380:Number 2233(2022)
• Publication Date: 2022-10-03
• Subjects: epidemiology -- voting -- management -- decision-making -- multiple models; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rsta.2021.0314
• Languages: English
• ISSNs: 1364-503X

Abstract

Abstract : Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.