Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations

By Joshua M. Epstein, Jon Parker, Derek A. T. Cummings, Ross A. Hammond. PLoS ONE, 2008

373 citations recorded by the source corpus.

Background In classical mathematical epidemiology, individuals do not adapt their contact behavior during epidemics. They do not endogenously engage, for example, in social distancing based on fear. Yet, adaptive behavior is well-documented in true epidemics. We explore the effect of including such behavior in models of epidemic dynamics. Methodology/Principal Findings Using both nonlinear dynamical systems and agent-based computation, we model two interacting contagion processes: one of disease and one of fear of the disease. Individuals can “contract” fear through contact with individuals who are infected with the disease (the sick), infected with fear only (the scared), and infected with both fear and disease (the sick and scared). Scared individuals–whether sick or not–may remove themselves from circulation with some probability, which affects the contact dynamic, and thus the disease epidemic proper. If we allow individuals to recover from fear and return to circulation, the coupled dynamics become quite rich, and can include multiple waves of infection. We also study flight as a behavioral response. Conclusions/Significance In a spatially extended setting, even relatively small levels of fear-inspired flight can have a dramatic impact on spatio-temporal epidemic dynamics. Self-isolation and spatial flight are only two of many possible actions that fear-infected individuals may take. Our main point is that behavioral adaptation of some sort must be considered.

Subfields: Biomedical Engineering, Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/aac62b9c27245680e40c99876b6e5f8c79221fa5

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Citations in the corpus

Modeling human mobility responses to the large-scale spreading of infectious diseases (2011, background): “In the recent literature, a few attempts to model behavioral changes have been done [8, 9, 10, 11, 12, 13, 14] but any of these tackle the effect of social disruption inside a metapopulation framework.”
Delay-induced chaos and its possible control in a seasonally forced eco-epidemiological model with fear effect and predator switching (2021, background): “[19] investigated the importance of spatial flight in an SIR model with fear and concluded that small amount of fear-inspired flight can dramatically impact the size and speedof the epidemic.”
Systems Science Methods in Public Health (2012, positive): “Agentbased models of epidemics have helped move epidemiology beyond the traditional S-I-R model and have demonstrated the importance of examining the role of social networks, transportation systems, local geography, and diverse behavioral responses to changing contexts on the spread of disease (45, ”
Risk perceptions of COVID-19 around the world (2020, background): “In fact, collectively, people’s behavior can fundamentally influence and alter the spread of a pandemic (Epstein et al. 2008; Funk et al. 2009; Reluga 2010; Van Bavel et al. 2020).”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “However, due to an early decrease in new infections, populations may see a subsequent decrease in NPI use causing multiple waves of infection [69,75,76,71].”
Separating Fact from Fear: Tracking Flu Infections on Twitter (2013, background): “Identifying awareness tweets may be of use in-and-of itself, such as for characterizing fear of illness (Epstein et al., 2008; Epstein, 2009), public perception, and discerning sentiment (e.g. flu is negative, flu shots may be positive.)”
Modelling the influence of human behaviour on the spread of infectious diseases: a review (2010, positive): “If people remove themselves from the circulation of a disease completely when they are affected by fear from the epidemic, subsequent return into circulation as the fear subsides can lead to multiple waves of infection (Epstein et al., 2008).”
Game Theory of Social Distancing in Response to an Epidemic (2010, background): “Related work by Epstein et al.[10] explicitly considers the spatial and information dynamics associated in response to an ongoing epidemic.”