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The COVID-19 infection cases have surged globally, causing devastations toboth the society and economy. A key factor contributing to the sustainedspreading is the presence of a large number of asymptomatic or hiddenspreaders, who mix among the susceptible population without being detected orquarantined. Here we propose an effective non-pharmacological interventionmethod of detecting the asymptomatic spreaders in contact-tracing networks, andvalidated it on the empirical COVID-19 spreading network in Singapore. We findthat using pure physical spreading equations, the hidden spreaders of COVID-19can be identified with remarkable accuracy. Specifically, based on the uniquecharacteristics of COVID-19 spreading dynamics, we propose a computationalframework capturing the transition probabilities among different infectiousstates in a network, and extend it to an efficient algorithm to identifyasymptotic individuals. Our simulation results indicate that a screening methodusing our prediction outperforms machine learning algorithms, e.g. graph neuralnetworks, that are designed as baselines in this work, as well as randomscreening of infection's closest contacts widely used by China in its earlyoutbreak. Furthermore, our method provides high precision even with incompleteinformation of the contract-tracing networks. Our work can be of criticalimportance to the non-pharmacological interventions of COVID-19, especiallywith increasing adoptions of contact tracing measures using various newtechnologies. Beyond COVID-19, our framework can be useful for other epidemicdiseases that also feature asymptomatic spreading

Identify Hidden Spreaders of Pandemic over Contact Tracing Networks