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Behavioral Responses to Epidemics in an Online Experiment: Using Virtual Diseases to Study Human Behavior

Overview of attention for article published in PLOS ONE, January 2013
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Title
Behavioral Responses to Epidemics in an Online Experiment: Using Virtual Diseases to Study Human Behavior
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0052814
Pubmed ID
Authors

Frederick Chen, Amanda Griffith, Allin Cottrell, Yue-Ling Wong

Abstract

We report the results of a study we conducted using a simple multiplayer online game that simulates the spread of an infectious disease through a population composed of the players. We use our virtual epidemics game to examine how people respond to epidemics. The analysis shows that people's behavior is responsive to the cost of self-protection, the reported prevalence of disease, and their experiences earlier in the epidemic. Specifically, decreasing the cost of self-protection increases the rate of safe behavior. Higher reported prevalence also raises the likelihood that individuals would engage in self-protection, where the magnitude of this effect depends on how much time has elapsed in the epidemic. Individuals' experiences in terms of how often an infection was acquired when they did not engage in self-protection are another factor that determines whether they will invest in preventive measures later on. All else being equal, individuals who were infected at a higher rate are more likely to engage in self-protective behavior compared to those with a lower rate of infection. Lastly, fixing everything else, people's willingness to engage in safe behavior waxes or wanes over time, depending on the severity of an epidemic: when prevalence is high, people are more likely to adopt self-protective measures as time goes by; when prevalence is low, a 'self-protection fatigue' effect sets in whereby individuals are less willing to engage in safe behavior over time.

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Geographical breakdown

Country Count As %
Sweden 1 1%
United Kingdom 1 1%
Taiwan 1 1%
Mexico 1 1%
Japan 1 1%
Unknown 80 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 16%
Researcher 13 15%
Student > Master 12 14%
Student > Bachelor 10 12%
Student > Doctoral Student 5 6%
Other 12 14%
Unknown 19 22%
Readers by discipline Count As %
Medicine and Dentistry 10 12%
Social Sciences 9 11%
Economics, Econometrics and Finance 8 9%
Psychology 8 9%
Computer Science 7 8%
Other 21 25%
Unknown 22 26%