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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

Here we provide the coupled case with α = β = 0.0008.One would expect that if we set α = β, the disease and fear epidemic S-curves should coincide, but this is not the case. Fear (the green curve) precedes disease (the red curve).
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pone-0003955-g002: Here we provide the coupled case with α = β = 0.0008.One would expect that if we set α = β, the disease and fear epidemic S-curves should coincide, but this is not the case. Fear (the green curve) precedes disease (the red curve).

Mentions: These results seem reasonably predictable, and are symmetrical to one another: at and , we get S-curves of disease. Reverse these settings and we get the strictly analogous pair of S-curves for fear. Surely one would expect that if we set , the disease and fear epidemic S-curves should coincide. Is this what happens? Not always, as shown in Figure 2, with .


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

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

Here we provide the coupled case with α = β = 0.0008.One would expect that if we set α = β, the disease and fear epidemic S-curves should coincide, but this is not the case. Fear (the green curve) precedes disease (the red curve).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0003955-g002: Here we provide the coupled case with α = β = 0.0008.One would expect that if we set α = β, the disease and fear epidemic S-curves should coincide, but this is not the case. Fear (the green curve) precedes disease (the red curve).
Mentions: These results seem reasonably predictable, and are symmetrical to one another: at and , we get S-curves of disease. Reverse these settings and we get the strictly analogous pair of S-curves for fear. Surely one would expect that if we set , the disease and fear epidemic S-curves should coincide. Is this what happens? Not always, as shown in Figure 2, with .

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