Impact of asymptomatic infected individuals on epidemic transmission dynamics in multiplex networks with partial coupling

Abstract The theory of network science has attracted the great interest of many researchers in the realm of biomathematics and public health, and numerous valuable epidemic models have been developed. In previous studies, it is common to set up a one-to-one correspondence between the nodes of a multi-layer network, ignoring the more complex situations in reality. In the present work, we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on epidemics. We propose a self-discovery mechanism for asymptomatic infected individuals, taking into account situations such as nucleic acid testing in the community and individuals performing selfantigen testing during the epidemic. Considering these factors together, through the microscopic Markov chain approach (MMCA) and extensive Monte Carlo (MC) numerical simulations, we find that the greater the coupling between the networks, the more information dissemination is facilitated. In order to control the epidemics, more asymptomatic infected individuals should be made aware of their infection. Massive adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic outbreaks. Meanwhile, the epidemic threshold of the proposed model is derived, and then miscellaneous factors affecting the epidemic threshold are also discussed. Current results are conducive to devising the prevention and control policies of pandemics.