利用非均匀抽样接触数据估计流行病风险
Estimating the epidemic risk using non-uniformly sampled contact data
摘要：
由于人口抽样和接触者报告不足，许多描述人口接触者的数据集都不完整。使用这种不完整的数据对传播过程进行数据驱动的模拟会导致对流行病风险的低估，因此必须制定纠正这种偏差的方法。在这里，我们专注于一个非均匀的采样个人之间的接触，旨在模仿日记或调查的结果，并考虑作为案例研究在不同的背景下收集的两个数据集。我们表明，使用代理数据建立使用的方法开发的情况下，均匀的人口抽样产生的改进，相对于使用的采样数据，但在采样网络中的链接密度被低估的强烈限制。我们提出了第二种方法来建立代理数据，假设知识的密度的链接在一个组形成的人口。我们表明，它给出了很好的结果时，人口是强结构，并讨论其局限性的情况下，人口与较弱的群体结构。这些限制突出了使用可穿戴传感器进行测量的兴趣，这些传感器能够产生关于接触的结构和持续时间的准确信息。
英文摘要：
Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.
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