Vote-processing rules for combining control recommendations from multiple models.
Vote-processing rules for combining control recommendations from multiple models.
复制标题
投票处理规则，用于结合来自多个模型的控制建议。
DOI：
10.1098/rsta.2021.0314
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发表时间：
2022-10-03
期刊：
影响因子：
5
通讯作者：
Runge, Michael C.
中科院分区：
文献类型：
--
作者：
Probert, William J. M.;Nicol, Sam;Ferrari, Matthew J.;Li, Shou-Li;Shea, Katriona;Tildesley, Michael J.;Runge, Michael C.
在疾病暴发期间，使用数学模型来比较控制干预措施。使用多个模型时，联合收割机推荐的最佳方法尚不清楚。现有的方法加权模型预测，然后使用组合预测对控制干预措施进行排名，假设模型输出直接可比。然而，每个模型表示流行病学系统的方式将有所不同。我们将选举投票处理规则应用于联合收割机模型生成的干预措施排名。将干预措施的排序合并，而不是将模型预测合并，避免了假设预测具有可比性，因为预测的所有比较都是在每个模型内进行的。我们研究了四个规则：最多者当选，替代投票（AV），库姆斯方法和Borda计数。我们调查规则的敏感性，包括模型，有利于只有一个动作，包括那些排名干预措施随机。我们调查了两个案例研究：2014年西非埃博拉疫情（37个房室模型）和假设的英国口蹄疫疫情（4个基于个体的模型）。Coombs方法最不容易添加有利于单一行动的模型，Borda计数和AV最容易添加随机排名干预措施的模型。每个规则选择相同的干预措施时，排名干预措施的平均预测，这表明，结合排名提供了类似的建议，更少的假设模型的可比性。这篇文章是“模拟现实生活中的流行病的技术挑战和克服这些挑战的例子”主题的一部分。
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’.
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影响因子：
34.3
作者：
Eaton, Jeffrey W.;Bacaer, Nicolas;Hallett, Timothy B.
通讯作者：
Hallett, Timothy B.
影响因子：
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DOI：
10.1073/pnas.1617482114
发表时间：
2017-05-30
影响因子：
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作者：
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