  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

Physics and Society;Populations and Evolution