论文标题
用非参数自兴奋的点过程估算过量的199年感染
Estimating Excess COVID-19 Infections with Nonparametric Self-Exciting Point Processes
论文作者
论文摘要
COVID-19的大流行导致统计模型和方法的特征是疾病暴发特征的大量增长。在这方面揭示的一类模型是使用自我引起的点过程，其中感染既是随机的”，也出现在人与人之间的传播中。除了整体Covid-19爆发的建模外，大流行还激发了评估各种政策决策和事件结果的研究。此处介绍的一个这样的研究领域涉及的方法是衡量大型事件或聚会在举行事件的地方的影响的方法。我们为传统的因果推理方法制定了另一种方法，然后将我们的方法应用于评估当时唐纳德·特朗普总统的连任竞选活动的影响，在举办集会的地区，对唐纳德·特朗普的连任集会对19日的感染。通过对非参数自激发点过程模型进行几种适应，我们估计了集会带来的共同感染的过量，以及这些过度感染持续存在的时间持续时间。
The COVID-19 pandemic has led to a vast amount of growth for statistical models and methods which characterize features of disease outbreaks. One class of models that came to light in this regard has been the use of self-exciting point processes, wherein infections occur both "at random" and also more systematically from person-to-person transmission. Beyond the modeling of the overall COVID-19 outbreak, the pandemic has also motivated research assessing various policy decisions and event outcomes. One such area of study, addressed here, relates to the formulation of methods which measure the impact that large events or gatherings of people had in the local areas where the events were held. We formulate an alternative approach to traditional causal inference methods and then apply our method to assessing the impact that then President Donald Trump's re-election campaign rallies had on COVID-19 infections in areas where the rallies were hosted. By incorporating several adaptions to nonparametric self-exciting point process models, we estimate both the excess number of COVID-19 infections brought on by the rallies and the duration of time in which these excess infections persisted.
