基于反向感染的复合种群网络传播溯源算法.

The spread of epidemics poses a significant threat to the entire human community. Therefore, it is critical to identify the sources of transmission quickly and take timely control measures. However, the diversity of epidemic transmission processes and information uncertainty makes it challenging to identify the sources of transmission quickly and accurately. This paper proposed a new algorithm for identifying transmission sources in a metapopulation network by combining the reverse infection algorithm and Markov chain theory. The algorithm first uses a Markov chain to initially estimate the time when a subpopulation is infected, and the infected subpopulation obtains its own identity information based on the infection time. Then, it traverses all subpopulations that obtain the identity information of the infected subpopulation and spreads the collected identity information of the infected subpopulation to all its neighbors. Finally, the spreading source of the metapopulation network can be inferred based on the temporal order in which all the identity information of the infected subpopulation is obtained. Simulation experiments conducted on real airports networks and artificial networks show that the accuracy of this algorithm is significantly improved compared to other algorithms, regardless of whether all or partial of the infection snapshots are known. This algorithm is well-suited for metapopulation networks such as aviation networks and is also useful for real-world epidemic transmission tracing and control. [ABSTRACT FROM AUTHOR]