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We study the class of SIS epidemics on temporal networks and propose a newactivity-driven and adaptive epidemic model that captures the impact ofasymptomatic and infectious individuals in the network. In the proposed model,referred to as the A-SIYS epidemic, each node can be in three possible states:susceptible, infected without symptoms or asymptomatic and infected withsymptoms or symptomatic. Both asymptomatic and symptomatic individuals areinfectious. We show that the proposed A-SIYS epidemic captures severalwell-established epidemic models as special cases and obtain sufficientconditions under which the disease gets eradicated by resorting to mean-fieldapproximations.  In addition, we highlight a potential inaccuracy in the derivation of theupper bound on the decay ratio in the activity-driven adaptive SIS (A-SIS)model in (Ogura et. al., 2019) and present a more general version of theirresult. We numerically illustrate the evolution of the fraction of infectednodes in the A-SIS epidemic model and show that the bound in (Ogura et. al.,2019) often fails to capture the behavior of the epidemic in contrast with ourresults.

A Generalized SIS Epidemic Model on Temporal Networks with Asymptomatic Carriers and Comments on Decay Ratio