论文标题
基于开放数据
Multiplex mobility network and metapopulation epidemic simulations of Italy based on Open Data
论文作者
论文摘要
人类流动性的模式在传染病的传播中起关键作用，因此代表了流行病建模和预测的关键要素。不幸的是，正如19日的大流行所强调的那样，对于绝大多数国家来说，没有颗粒状流动性数据。这阻碍了开发计算框架以监测疾病的演变并及时有足够的预防政策的可能性。在这里，我们展示了在意大利案例研究中如何解决这个问题。我们仅基于开放数据构建多重移动性网络，并实现SIR跨吞噬模型，该模型允许通过数据驱动的随机模拟进行方案分析。我们估计的移动性流与智能手机的实时专有数据一致。因此，在没有高分辨率移动性数据的上下文中，我们的建模方法可以很有用。
The patterns of human mobility play a key role in the spreading of infectious diseases and thus represent a key ingredient of epidemic modeling and forecasting. Unfortunately, as the Covid-19 pandemic has dramatically highlighted, for the vast majority of countries there is no availability of granular mobility data. This hinders the possibility of developing computational frameworks to monitor the evolution of the disease and to adopt timely and adequate prevention policies. Here we show how this problem can be addressed in the case study of Italy. We build a multiplex mobility network based solely on open data, and implement a SIR metapopulation model that allows scenario analysis through data-driven stochastic simulations. The mobility flows that we estimate are in agreement with real-time proprietary data from smartphones. Our modeling approach can thus be useful in contexts where high-resolution mobility data is not available.
