论文标题
通过子采样来估算人群中基于接触的传播的传播
Estimating Spread of Contact-Based Contagions in a Population Through Sub-Sampling
论文作者
论文摘要
物理接触导致各种现象的传播，例如病毒，八卦，想法，包装和营销小册子。传播取决于人们如何相互移动和共同设置，或者他们的流动模式。这种现象的传播多远对于政策制定和个人决策都具有重要意义，例如研究Covid-19在不同的干预策略（例如戴口罩）下的传播。实际上，整个人口的移动性模式永远无法获得，我们通常可以访问个人子集的位置数据。在本文中，我们正式化并研究了估计现象在人群中传播的问题，因为我们只能访问某些人群中某些人的位置访问的子示例。我们表明，诸如估算子样本中的扩散并将其缩放到人群中的简单解决方案，或依赖于个体建模的位置访问的更复杂的解决方案在实践中表现不佳，因为它忽略了未观察到的个体和采样的人之间的接触，而后者则是因为它产生了共同分类的不准确的建模。取而代之的是，我们直接对个体之间的共处进行建模。我们介绍了Pollspreader和Collsustipsipsible，这两种新颖的方法，它们使用接触网络对个体之间的共分离进行了建模，并使用子样本使用子样本来推断接触网络的特性，以估计现象在整个人群中的传播。我们表明，我们的估计值在预期的疾病传播方面提供了上限和下限。最后，使用大型的高分辨率现实世界移动性数据集，我们在实验上表明我们的估计值是准确的，而其他无法正确解释个人之间的共同分离的方法会导致错误的观察结果（例如，早产群免疫）。
Physical contacts result in the spread of various phenomena such as viruses, gossips, ideas, packages and marketing pamphlets across a population. The spread depends on how people move and co-locate with each other, or their mobility patterns. How far such phenomena spread has significance for both policy making and personal decision making, e.g., studying the spread of COVID-19 under different intervention strategies such as wearing a mask. In practice, mobility patterns of an entire population is never available, and we usually have access to location data of a subset of individuals. In this paper, we formalize and study the problem of estimating the spread of a phenomena in a population, given that we only have access to sub-samples of location visits of some individuals in the population. We show that simple solutions such as estimating the spread in the sub-sample and scaling it to the population, or more sophisticated solutions that rely on modeling location visits of individuals do not perform well in practice, the former because it ignores contacts between unobserved individuals and sampled ones and the latter because it yields inaccurate modeling of co-locations. Instead, we directly model the co-locations between the individuals. We introduce PollSpreader and PollSusceptible, two novel approaches that model the co-locations between individuals using a contact network, and infer the properties of the contact network using the subsample to estimate the spread of the phenomena in the entire population. We show that our estimates provide an upper bound and a lower bound on the spread of the disease in expectation. Finally, using a large high-resolution real-world mobility dataset, we experimentally show that our estimates are accurate, while other methods that do not correctly account for co-locations between individuals result in wrong observations (e.g, premature herd-immunity).
