Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures

Abstract Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic...

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Autores principales:	Jason L. Harman, Justin M. Weinhardt, James W. Beck, Ivy Mai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/162c37565c864d5bb418849a930adc93
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spelling	oai:doaj.org-article:162c37565c864d5bb418849a930adc932021-12-02T16:35:31ZInterpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures10.1038/s41598-021-95134-z2045-2322https://doaj.org/article/162c37565c864d5bb418849a930adc932021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95134-zhttps://doaj.org/toc/2045-2322Abstract Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic reasoning bias that stems from intuitive misunderstandings of accumulation. Previous work in system dynamics shows that even highly educated individuals with training in science and math misunderstand basic concepts of accumulation. In the context of COVID-19, relying on the single cue of daily new cases can lead to relaxed attitudes about the risk of COVID-19 when daily new cases begin to decline. This situation is at the very point when risk is highest because even though daily new cases have declined, the active number of cases are highest because they have been accumulating over time. In an experiment with young adults from the USA and Canada (N = 551), we confirm that individuals fail to understand accumulation regarding COVID-19, have less concern regarding COVID-19, and decrease endorsement for public health measures as new cases decline but when active cases are at the highest point. Moreover, we experimentally manipulate different dynamic data visualizations and show that presenting data highlighting active cases and minimizing new cases led to increased concern and increased endorsement for COVID-19 health measures compared to a control condition highlighting daily cases. These results hold regardless of country, political affiliation, and individual differences in decision making. This study has implications for communicating the risks of contracting COVID-19 and future public health issues.Jason L. HarmanJustin M. WeinhardtJames W. BeckIvy MaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jason L. Harman Justin M. Weinhardt James W. Beck Ivy Mai Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
description	Abstract Effective risk communication during the COVID-19 pandemic is critical for encouraging appropriate public health behaviors. One way that the public is informed about COVID-19 numbers is through reports of daily new cases. However, presenting daily cases has the potential to lead to a dynamic reasoning bias that stems from intuitive misunderstandings of accumulation. Previous work in system dynamics shows that even highly educated individuals with training in science and math misunderstand basic concepts of accumulation. In the context of COVID-19, relying on the single cue of daily new cases can lead to relaxed attitudes about the risk of COVID-19 when daily new cases begin to decline. This situation is at the very point when risk is highest because even though daily new cases have declined, the active number of cases are highest because they have been accumulating over time. In an experiment with young adults from the USA and Canada (N = 551), we confirm that individuals fail to understand accumulation regarding COVID-19, have less concern regarding COVID-19, and decrease endorsement for public health measures as new cases decline but when active cases are at the highest point. Moreover, we experimentally manipulate different dynamic data visualizations and show that presenting data highlighting active cases and minimizing new cases led to increased concern and increased endorsement for COVID-19 health measures compared to a control condition highlighting daily cases. These results hold regardless of country, political affiliation, and individual differences in decision making. This study has implications for communicating the risks of contracting COVID-19 and future public health issues.
format	article
author	Jason L. Harman Justin M. Weinhardt James W. Beck Ivy Mai
author_facet	Jason L. Harman Justin M. Weinhardt James W. Beck Ivy Mai
author_sort	Jason L. Harman
title	Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
title_short	Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
title_full	Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
title_fullStr	Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
title_full_unstemmed	Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures
title_sort	interpreting time-series covid data: reasoning biases, risk perception, and support for public health measures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/162c37565c864d5bb418849a930adc93
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Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures

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