新型冠状病毒疾病的相互作用Neyman-Scott点过程模型
An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19
摘要：
2019冠状病毒病（COVID-19）传播迅速，已导致全球超过200万人死亡，构成重大社会挑战。了解患者就诊的空间模式和检测局部传播事件对于控制疾病暴发至关重要。我们分析了从首尔收集的非常详细的COVID-19接触者追踪数据，这为了解患者就诊发生的机制提供了独特的机会。分析接触者追踪数据具有挑战性，因为患者访问显示出强烈的聚类模式，而事件聚类可能具有复杂的交互行为。为了解释这种行为，我们开发了一种新的交互Neyman-Scott过程，该过程将观察到的患者访问事件视为从父传播事件生成的后代。这种模型的推理是复杂的，因为可能性涉及棘手的归一化函数。为了解决这个问题，我们嵌入一个辅助变量算法到我们的马尔可夫链蒙特卡罗。我们将我们的模型拟合到不同爆发场景下的几个模拟和真实数据示例中，并表明我们的方法可以很好地描述患者就诊的空间模式。我们还提供可视化工具，可以为传染病的公共卫生干预提供信息，例如社交距离。
英文摘要：
With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to over 2 million deaths worldwide, posing significant societal challenges. Understanding the spatial patterns of patient visits and detecting the local spreading events are crucial to controlling disease outbreaks. We analyze highly detailed COVID-19 contact tracing data collected from Seoul, which provides a unique opportunity to understand the mechanism of patient visit occurrence. Analyzing contact tracing data is challenging because patient visits show strong clustering patterns while clusters of events may have complex interaction behavior. To account for such behaviors, we develop a novel interaction Neyman-Scott process that regards the observed patient visit events as offsprings generated from a parent spreading event. Inference for such models is complicated since the likelihood involves intractable normalizing functions. To address this issue, we embed an auxiliary variable algorithm into our Markov chain Monte Carlo. We fit our model to several simulated and real data examples under different outbreak scenarios and show that our method can describe spatial patterns of patient visits well. We also provide visualization tools that can inform public health interventions for infectious diseases such as social distancing.
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