Identify Hidden Spreaders of Pandemic over Contact Tracing Networks

Huang, Shuhong; Sun, Jiachen; Feng, Ling; Xie, Jiarong; Wang, Dashun; Neuroscience, Yanqing Hu Institute of; Munich, Technical University of; Munich,; Germany,; Intelligence, MIT Center for Collective; Cambridge,; MA,; USA,; Computing, Institute of High Performance; ASTAR,; Singapore,; Physics, Department of; Singapore, National University of; Data, School of; Science, Computer; University, Sun Yat-sen; Guangzhou,; China,; Management, Kellogg School of; University, Northwestern; Evanston,; IL,

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Here we propose an effective non-pharmacological intervention method of detecting the asymptomatic spreaders in contact-tracing networks, and validated it on the empirical COVID-19 spreading network in Singapore. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy. Specifically, based on the unique characteristics of COVID-19 spreading dynamics, we propose a computational framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. Our simulation results indicate that a screening method using our prediction outperforms machine learning algorithms, e.g. graph neural networks, that are designed as baselines in this work, as well as random screening of infection's closest contacts widely used by China in its early outbreak. Furthermore, our method provides high precision even with incomplete information of the contract-tracing networks. Our work can be of critical importance to the non-pharmacological interventions of COVID-19, especially with increasing adoptions of contact tracing measures using various new technologies. Beyond COVID-19, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading