Dutta Ritabrata, Mira Antonietta, Onnela Jukka-Pekka
Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Department of Science and High Technology, Università degli Studi dell'Insubria, Varese, Italy.
Proc Math Phys Eng Sci. 2018 Jul;474(2215):20180129. doi: 10.1098/rspa.2018.0129. Epub 2018 Jul 18.
Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with few other individuals, and the structure of these interactions influence spreading processes, the pairwise relationships between individuals can be usefully represented by a network. Although the underlying transmission processes are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumours in a social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social/contact network in an Indian village and an online social network. Our goal is to learn simultaneously the spreading process parameters and the first infected node, given a fixed network structure and the observed state of nodes at several time points. Our inference scheme is based on approximate Bayesian computation, a likelihood-free inference technique. Our method is agnostic about the network topology and the spreading process. It generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies.
研究传染病是为了了解其传播机制、评估控制策略并预测未来疫情爆发的风险和过程。由于人们只与少数其他人互动，且这些互动的结构会影响传播过程，个体之间的两两关系可以通过网络有效地表示出来。尽管潜在的传播过程不同，但网络方法可用于研究病原体在接触网络中的传播或谣言在社交网络中的传播。我们在合成网络以及两个实证网络（一个印度村庄的社会/接触网络和一个在线社交网络）上研究模拟的简单和复杂流行病。我们的目标是在给定固定网络结构以及在几个时间点观察到的节点状态的情况下，同时了解传播过程参数和首个被感染节点。我们的推理方案基于近似贝叶斯计算，这是一种无似然推理技术。我们的方法对网络拓扑和传播过程不做假设。它通常表现良好，而且有点违反直觉的是，在更具异质性的网络拓扑上推理问题似乎更容易，这增强了其未来在很少有网络具有同质性拓扑的现实世界场景中的适用性。
