  The study proposes a modeling framework for investigating the disease
dynamics with adaptive human behavior during a disease outbreak, considering
the impacts of both local observations and global information. One important
application scenario is that commuters may adjust their behavior upon observing
the symptoms and countermeasures from their physical contacts during travel,
thus altering the trajectories of a disease outbreak. We introduce the
heterogeneous mean-field (HMF) approach in a multiplex network setting to
jointly model the spreading dynamics of the infectious disease in the contact
network and the dissemination dynamics of information in the observation
network. The disease spreading is captured using the classic
susceptible-infectious-susceptible (SIS) process, while an SIS-alike process
models the spread of awareness termed as unaware-aware-unaware (UAU). And the
use of multiplex network helps capture the interplay between disease spreading
and information dissemination, and how the dynamics of one may affect the
other. Theoretical analyses suggest that there are three potential equilibrium
states, depending on the percolation strength of diseases and information. The
dissemination of information may help shape herd immunity among the population,
thus suppressing and eradicating the disease outbreak. Finally, numerical
experiments using the contact networks among metro travelers are provided to
shed light on the disease and information dynamics in the real-world scenarios
and gain insights on the resilience of transportation system against the risk
of infectious diseases.
