[Paper Review] Modeling disease spreading with adaptive behavior considering local and global information dissemination

Xinwu Qian, Jiawei Xue+1 more|arXiv (Cornell University)|Aug 25, 2020

Complex Network Analysis Techniques19 references4 citations

TL;DR

This paper proposes a multiplex network model using heterogeneous mean-field (HMF) analysis to study the co-evolution of disease spreading (SIS) and adaptive human behavior driven by local observations and global information (UA-SIS). It finds that information dissemination significantly raises the disease threshold, enhancing network resilience and enabling herd immunity, especially in large-scale networks like metro systems.

ABSTRACT

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.

Motivation & Objective

• To model the co-evolution of disease transmission and information dissemination during infectious disease outbreaks.
• To investigate how adaptive human behavior—shaped by local observations and global information—affects disease dynamics in transportation networks.
• To analyze the impact of information on disease threshold and network resilience using a multiplex network framework.
• To validate the model using real-world metro contact networks and assess its implications for public health and transportation system resilience.

Proposed method

• Employs a multiplex network with two layers: a contact network for disease spreading (SIS model) and an observation network for information dissemination (UA-SIS model).
• Applies the heterogeneous mean-field (HMF) approach to analytically derive equilibrium states and threshold conditions for disease and awareness percolation.
• Models adaptive behavior as a response to local symptoms (observed from contacts) and global alerts (e.g., media, social media).
• Uses numerical experiments on real metro traveler contact networks to simulate disease and information dynamics under varying conditions.
• Analyzes the decay of disease threshold with network size, comparing SIS and UA-SIS models to assess resilience.
• Identifies three stable equilibrium states based on percolation strength of disease and information, determined by threshold analysis.

Experimental results

Research questions

• RQ1 How does the co-evolution of disease spreading and information dissemination affect the stability and threshold of an outbreak?
• RQ2 What role do local observations and global information play in shaping adaptive human behavior during an epidemic?
• RQ3 How does the interplay between disease and awareness dynamics influence the emergence of herd immunity?
• RQ4 What is the impact of network size and topology on disease threshold, especially in scale-free networks like metro systems?
• RQ5 To what extent does information dissemination delay or suppress disease outbreaks in large, connected networks?

Key findings

• The model identifies three stable equilibrium states—disease-free, endemic, and awareness-driven disease-free—depending on the percolation strength of disease and information.
• Information dissemination significantly raises the disease threshold, especially in large-scale networks, delaying or preventing widespread outbreaks.
• In large metro contact networks (MCN), the disease threshold decays to zero under the SIS model due to divergent degree variance, making the system highly vulnerable.
• Under the UA-SIS model, the disease threshold is several magnitudes higher than under SIS, demonstrating that even minimal local awareness improves network resilience.
• Awareness spreads faster than disease, and their growth rates are positively correlated with a time lag, which is shorter under endemic conditions.
• The dynamics show that when awareness is high, the disease is either nearly eradicated or has reached a stable endemic state, consistent with real-world data such as Google Flu trends and patient visits.