Prediction of asymptomatic COVID-19 infections based on complex network.

Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.

A coalitional game-based joint monitoring mechanism for combating COVID-19.

In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability. In this work, we formulate the epidemiological monitoring process as a coalitional game. Then, we propose a Shapley value-based payoffs distribution scheme for the coalitional game. A comprehensive analytical framework is developed to evaluate the advantages and sustainability of the cooperation between communities. Experimental results show that the proposed mechanism performs much better than the conventional non-cooperative monitoring design and can greatly increase each community's payoffs.