Mastrandrea Rossana, Barrat Alain
Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France.
IMT Institute of Advanced Studies, Lucca, Lucca, Italy.
PLoS Comput Biol. 2016 Jun 24;12(6):e1005002. doi: 10.1371/journal.pcbi.1005002. eCollection 2016 Jun.
Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.
社会互动塑造了人群中传播过程的模式。日记或接近传感器等技术能够收集有关接触情况的数据，并构建个体之间的接触网络。然而，从这些不同技术获得的接触网络在数量上存在差异。在此，我们首先展示当将这些数据输入到疫情传播数值模型中时，这些差异如何影响对疫情风险的预测：与传感器数据相比，接触日记中参与率低、接触情况报告不足以及接触持续时间估计过高，确实会导致相应模拟结果出现重要差异，例如对初始条件的敏感性增强。最重要的是，我们研究从接触日记收集的信息是否以及如何能用于此类模拟，以准确描述疫情风险，假设传感器数据代表真实情况。由接触传感器和日记构建的接触网络确实存在一些结构相似性：这表明仅利用接触日记网络信息构建替代接触网络的可能性，使得使用该替代网络的模拟给出的疫情风险估计与使用接触传感器网络的模拟相同。我们提出并比较了几种构建此类替代数据的方法，并表明只要接触日记信息由描述人际接触持续时间异质性的公开可用数据补充，确实有可能在使用替代数据和传感器数据的模拟结果之间取得良好的一致性。
