Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy.
Department of Veterinary Science, University of Turin, Torino, Italy.
PLoS Comput Biol. 2014 Jul 10;10(7):e1003716. doi: 10.1371/journal.pcbi.1003716. eCollection 2014 Jul.
Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.
人口流动是传染病大规模空间传播模型的关键组成部分。正确地对人口流动进行建模和量化对于改善疫情控制至关重要，但可能会受到数据不完整或不可用的阻碍。在这里，我们探索了使用个体流动代理来描述通勤流并预测流感样疾病流行扩散的机会。我们考虑了三个欧洲国家和不同分辨率尺度的相应通勤网络，这些网络来自(i)官方人口普查调查、(ii)从移动电话通话记录中提取的代理移动数据，以及(iii)用人口普查数据校准的辐射模型。在这些国家定义的元种群模型，并整合不同的流动层，根据传染病的可观察结果进行比较。我们表明，来自移动电话数据的通勤网络很好地捕捉了经验性的通勤模式，占总通量的 87%以上。移动电话和人口普查来源的每条链路的通勤通量分布相似且高度相关，但观察到移动电话数据中通勤流量存在系统性高估。一旦在传染病模型中考虑到移动电话通勤网络，这将导致流行病传播速度比人口普查通勤网络更快，但是在很大程度上保留了新受感染地点的感染顺序。代理的校准会影响不同模型之间到达时间的一致性，并且在移动性来源之间观察到的拓扑和流量差异会改变由此产生的流行病入侵模式。结果还表明，代理在不同分辨率尺度下用于模拟疾病传播的通勤模式的性能不同，辐射模型在网络中心的种子时比移动电话数据具有更高的准确性，而对于外围位置则相反。因此，应该根据所研究的疫情情况的所需准确性来选择代理。
