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首页> 外文会议>International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation >Prospective Detection of Foodborne Illness Outbreaks Using Machine Learning Approaches
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Prospective Detection of Foodborne Illness Outbreaks Using Machine Learning Approaches

机译：使用机器学习方法对食源性疾病暴发进行前瞻性检测

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Despite advances in food safety regulations, food-borne illness imposes a heavy health burden, with nearly 50 million estimated incident cases of illness each year. Having a prospective foodborne illness outbreak detection mechanism for more accurate and timely triggering of outbreak control measures would offer notable public health dividends, but is challenging due to the subclinical character of most foodborne illnesses. Within this work, collected synthetic datasets of incident illness cases and vendor contamination records from a previously contributed and empirically grounded model of foodborne illness, are used to study the efficacy of Hidden Markov Models (HMMs) for syndromic surveillance monitoring and disease outbreak detection under two data collection regimes, one involving a sentinel population using smartphone-based app for tracing location of food consumption and subclinical reporting. A support vector machine (SVM) approach was applied to compare the results to the HMM. Findings suggest that while reliance on clinical data offers poor potential for automatic outbreak detection, the use of HMMs offer excellent potential for detecting foodborne illness outbreak when informed by subclinical reporting by even a very small (4% of population) sentinel group. By contrast, SVM offers relatively poor prospects for detection. Furthermore, experiments with an empirically grounded agent-based model suggest that use of an HMM may be advantageous for triggering outbreak investigations among public health inspectors.
机译：尽管食品安全法规有所改进，但食源性疾病却给健康带来了沉重负担，每年估计有近5000万例疾病突发事件。拥有潜在的食源性疾病爆发检测机制以更准确，及时地触发疾病爆发控制措施，将为公共卫生带来可观的收益，但由于大多数食源性疾病的亚临床特征，这具有挑战性。在这项工作中，从先前贡献的和以经验为基础的食源性疾病模型中收集了突发事件病例的综合数据集和供应商污染记录，用于研究隐马尔可夫模型（HMM）在两种情况下进行症状监测和疾病暴发检测的功效。数据收集制度，其中一项涉及使用基于智能手机的应用来跟踪哨兵人群，以追踪食物消费和亚临床报告的位置。应用支持向量机（SVM）方法将结果与HMM进行比较。研究结果表明，尽管依靠临床数据提供的自动暴发检测潜力不大，但即使只有极少（占人口的4％）的前哨小组报告亚临床报告，使用HMM仍具有极好的发现食源性疾病暴发的潜力。相比之下，SVM提供相对较差的检测前景。此外，基于经验的基于代理的模型的实验表明，使用HMM可能会有利于触发公共卫生检查员之间的暴发调查。

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