EconPapers    
Economics at your fingertips  
 

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0277953620306924
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Exponential-growth prediction bias and compliance with safety measures related to COVID-19 (2021) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

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

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
http://www.elsevier. ... _01_ooc_1&version=01

DOI: 10.1016/j.socscimed.2020.113473

Access Statistics for this article

Social Science & Medicine is currently edited by Ichiro (I.) Kawachi and S.V. (S.V.) Subramanian

More articles in Social Science & Medicine from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:socmed:v:268:y:2021:i:c:s0277953620306924