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

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  • 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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  • Frederick Chen & Amanda Griffith & Allin Cottrell & Yue-Ling Wong, 2013. "Behavioral Responses to Epidemics in an Online Experiment: Using Virtual Diseases to Study Human Behavior," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-10, January.
  • Handle: RePEc:plo:pone00:0052814
    DOI: 10.1371/journal.pone.0052814
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    References listed on IDEAS

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    Cited by:

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    2. Konstantin Matthies & Flavio Toxvaerd, 2023. "Rather doomed than uncertain: risk attitudes and transmissive behavior under asymptomatic infection," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(1), pages 1-44, July.
    3. Zhu, Xuzhen & Liu, Yuxin & Wang, Shengfeng & Wang, Ruijie & Chen, Xiaolong & Wang, Wei, 2021. "Allocating resources for epidemic spreading on metapopulation networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    4. Krystal Lau & Katharina Hauck & Marisa Miraldo, 2019. "Excess influenza hospital admissions and costs due to the 2009 H1N1 pandemic in England," Health Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 175-188, February.
    5. Huo, Liang’an & Ding, Fan & Cheng, Yingying, 2019. "Dynamic analysis of a SIbInIu, rumor spreading model in complex social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 924-932.
    6. Xia, Ling-Ling & Jiang, Guo-Ping & Song, Bo & Song, Yu-Rong, 2015. "Rumor spreading model considering hesitating mechanism in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 295-303.

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