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The study proposes a modeling framework for investigating the diseasedynamics with adaptive human behavior during a disease outbreak, consideringthe impacts of both local observations and global information. One importantapplication scenario is that commuters may adjust their behavior upon observingthe symptoms and countermeasures from their physical contacts during travel,thus altering the trajectories of a disease outbreak. We introduce theheterogeneous mean-field (HMF) approach in a multiplex network setting tojointly model the spreading dynamics of the infectious disease in the contactnetwork and the dissemination dynamics of information in the observationnetwork. The disease spreading is captured using the classicsusceptible-infectious-susceptible (SIS) process, while an SIS-alike processmodels the spread of awareness termed as unaware-aware-unaware (UAU). And theuse of multiplex network helps capture the interplay between disease spreadingand information dissemination, and how the dynamics of one may affect theother. Theoretical analyses suggest that there are three potential equilibriumstates, depending on the percolation strength of diseases and information. Thedissemination of information may help shape herd immunity among the population,thus suppressing and eradicating the disease outbreak. Finally, numericalexperiments using the contact networks among metro travelers are provided toshed light on the disease and information dynamics in the real-world scenariosand gain insights on the resilience of transportation system against the riskof infectious diseases.

Modeling disease spreading with adaptive behavior considering local and global information dissemination