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Coupled contagion dynamics of fear and disease: mathematical and computational explorations.

Epstein JM, Parker J, Cummings D, Hammond RA - PLoS ONE (2008)

Bottom Line: 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.Our main point is that behavioral adaptation of some sort must be considered.

View Article: PubMed Central - PubMed

Affiliation: Center on Social and Economic Dynamics, The Brookings Institution, Washington DC, United States of America. jepstein@brookings.edu

ABSTRACT

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.

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Related in: MedlinePlus

(A&B): A comparison of epidemic duration and total incidence with 10% “fleers” versus 10% “ignorers.”As before, each bar in the chart represents an average across 30 simulation runs for a given parameter setting, with standard error range. The runs with 10% “ignorers” have similar incidence to runs with 100% “hiders,” and similar duration to runs with 100% “ignorers.” By contrast, the runs with 10% “fleers” have much higher incidence and lower duration.
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pone-0003955-g006: (A&B): A comparison of epidemic duration and total incidence with 10% “fleers” versus 10% “ignorers.”As before, each bar in the chart represents an average across 30 simulation runs for a given parameter setting, with standard error range. The runs with 10% “ignorers” have similar incidence to runs with 100% “hiders,” and similar duration to runs with 100% “ignorers.” By contrast, the runs with 10% “fleers” have much higher incidence and lower duration.

Mentions: Of course, the 10% of agents who are fleeing are also not hiding. By remaining in circulation, even ignoring (neither fleeing nor hiding) agents should have an inflammatory effect on the epidemic. So, is it the flight or simply the increased circulation from non-hiding which is driving the previous result? To answer this question, we ran the simulation with 90% “hiders” and 10% “ignorers.” As Figure 6 illustrates, the results from these runs differ noticeably from the runs with actual flight: we observed an average incidence of 32% and an average epidemic duration of 640 time periods. Flight has a substantial impact above and beyond increasing the number of “non-hiders”.


Coupled contagion dynamics of fear and disease: mathematical and computational explorations.

Epstein JM, Parker J, Cummings D, Hammond RA - PLoS ONE (2008)

(A&B): A comparison of epidemic duration and total incidence with 10% “fleers” versus 10% “ignorers.”As before, each bar in the chart represents an average across 30 simulation runs for a given parameter setting, with standard error range. The runs with 10% “ignorers” have similar incidence to runs with 100% “hiders,” and similar duration to runs with 100% “ignorers.” By contrast, the runs with 10% “fleers” have much higher incidence and lower duration.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2596968&req=5

pone-0003955-g006: (A&B): A comparison of epidemic duration and total incidence with 10% “fleers” versus 10% “ignorers.”As before, each bar in the chart represents an average across 30 simulation runs for a given parameter setting, with standard error range. The runs with 10% “ignorers” have similar incidence to runs with 100% “hiders,” and similar duration to runs with 100% “ignorers.” By contrast, the runs with 10% “fleers” have much higher incidence and lower duration.
Mentions: Of course, the 10% of agents who are fleeing are also not hiding. By remaining in circulation, even ignoring (neither fleeing nor hiding) agents should have an inflammatory effect on the epidemic. So, is it the flight or simply the increased circulation from non-hiding which is driving the previous result? To answer this question, we ran the simulation with 90% “hiders” and 10% “ignorers.” As Figure 6 illustrates, the results from these runs differ noticeably from the runs with actual flight: we observed an average incidence of 32% and an average epidemic duration of 640 time periods. Flight has a substantial impact above and beyond increasing the number of “non-hiders”.

Bottom Line: 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.Our main point is that behavioral adaptation of some sort must be considered.

View Article: PubMed Central - PubMed

Affiliation: Center on Social and Economic Dynamics, The Brookings Institution, Washington DC, United States of America. jepstein@brookings.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus