We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics—which we call 'meta-metrics'—that may better predict vulnerability. We find that the GP solutions—the best of which combine both global and node-level metrics—are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.

中文翻译：

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网络元度量标准：使用进化计算来识别畜牧生产系统模型中流行病学脆弱性的有效指标

我们开发了基于代理的易感性/传染性模型，该模型可模拟美国三个州的生猪生产链网络中的疾病入侵。每次运行模型后，都会输出座席参数，联系网络数据和流行病学传播模式。然后计算关键网络指标，其中一些指标与整个网络结构有关，其他指标与网络中每个节点的位置有关。我们进行统计测试以评估每个网络指标在多大程度上预测流行病学脆弱性，在某些情况下发现显着的相关性，但没有单独的指标可作为可靠的风险指标。为了研究网络结构和节点位置之间的复杂相互作用，我们使用遗传编程（GP）算法搜索描述各个指标组合（称为“元指标”）的数学方程式，以更好地预测脆弱性。我们发现，GP解决方案（最好的方案结合了全局和节点级别的指标）是疾病风险指标，远比任何单个指标都好，而元指标可解释这三者中多达91％的代理脆弱性变异性学习区。我们建议，该方法可用于帮助牲畜流行病学家确定生物安全干预措施的目标，而且元度量方法可能对研究各种复杂的网络现象可能有用。我们发现，GP解决方案（最好的方案结合了全局和节点级别的指标）是疾病风险指标，远比任何单个指标都好，而元指标可解释这三者中多达91％的代理脆弱性变异性学习区。我们建议，该方法可用于帮助牲畜流行病学家确定生物安全干预措施的目标，而且元度量方法可能对研究各种复杂的网络现象可能有用。我们发现，GP解决方案（最好的方案结合了全局和节点级别的指标）是疾病风险指标，远比任何单个指标都好，而元指标可解释这三者中多达91％的代理脆弱性变异性学习区。我们建议，该方法可用于帮助牲畜流行病学家确定生物安全干预措施的目标，而且元度量方法可能对研究各种复杂的网络现象可能有用。