Exponential-growth prediction bias and compliance with safety measures related to COVID-19

Ritwik Banerjee, Joydeep Bhattacharya and Priyama Majumdar

Social Science & Medicine, 2021, vol. 268, issue C

Abstract: We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB.

Keywords: COVID; Exponential growth bias; WHO safety Measures; Health communication; Graphical communication; Nudges (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:socmed:v:268:y:2021:i:c:s0277953620306924

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DOI: 10.1016/j.socscimed.2020.113473

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