Sun Lijun, Axhausen Kay W, Lee Der-Horng, Cebrian Manuel
1] Future Cities Laboratory, Singapore-ETH Centre for Global Environmental Sustainability (SEC), 138602, Singapore [2] Department of Civil & Environmental Engineering, National University of Singapore, 117576, Singapore [3] Media Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
1] Future Cities Laboratory, Singapore-ETH Centre for Global Environmental Sustainability (SEC), 138602, Singapore [2] Institute for Transport Planning and Systems (IVT), Swiss Federal Institute of Technology, Zürich, 8093, Switzerland.
Sci Rep. 2014 Jun 6;4:5099. doi: 10.1038/srep05099.
Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme--a simple, but universal strategy requiring only local information--and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.
在大型都市网络中，身体接触仍然难以追踪，尽管它是传染性疫情传播的关键媒介。日常出行过程中的共现接触为我们提供了一个城市规模的时间分辨身体接触网络，该网络由300万出行者之间的10亿次接触组成。在此，我们研究了这种共现结构知识对于传染性疫情早期检测可能具有的优势。我们首先考察“朋友传感器”方案——一种简单但通用的策略，只需要局部信息——并证明它能对模拟疫情进行显著的早期检测。利用完整的网络结构，我们随后识别出先进的“全局传感器集”，比朋友传感器方案节省了大量的早期预警时间。接触次数最多的个体是最有效的传感器，其性能与出行频率最高、探索行为和结构中心性最高的个体相当。在测试对传感器规模的依赖性并评估传感器可靠性时，出现了一种效率平衡；我们发现，仅监测0.01%度数最高的人群就能获得可观且可靠的提前时间。
