学习潜在扩散剂存在时的激活概率
Learning the Probability of Activation in the Presence of Latent Spreaders
摘要：
当传染病在社区传播时，一个人被感染的可能性取决于她的易感性和通过与他人接触而接触到传染病的程度。虽然人们通常了解个体的易感性，但在许多情况下，个体的接触者是否具有传染性是未知的。我们研究的问题，预测如果一个人将采取的传染病在存在多种模式的感染（暴露/易感性）和潜在的邻居的影响。我们提出了一个生成的概率模型和变分推理方法来学习我们的模型的参数。通过对合成数据的一系列实验，我们测量了所提出的模型识别潜在传播者的能力，并预测了感染的风险。应用于20，000名医院患者的真实数据集，我们证明了我们的模型在使用病房共享和护士共享网络预测医疗相关感染的发生方面的实用性。我们的模型优于现有的基准，并为设计和实施有针对性的干预措施以遏制感染传播提供了可操作的见解。
英文摘要：
When an infection spreads in a community, an individual's probability of becoming infected depends on both her susceptibility and exposure to the contagion through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious is unknown. We study the problem of predicting if an individual will adopt a contagion in the presence of multiple modes of infection (exposure/susceptibility) and latent neighbor influence. We present a generative probabilistic model and a variational inference method to learn the parameters of our model. Through a series of experiments on synthetic data, we measure the ability of the proposed model to identify latent spreaders, and predict the risk of infection. Applied to a real dataset of 20,000 hospital patients, we demonstrate the utility of our model in predicting the onset of a healthcare associated infection using patient room-sharing and nurse-sharing networks. Our model outperforms existing benchmarks and provides actionable insights for the design and implementation of targeted interventions to curb the spread of infection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
