Visual Risk Literacy in “Flatten the Curve” COVID-19 Visualizations

Amidon, Timothy R.; Nielsen, Alex Christian Yde; Pflugfelder, Ehren Helmut; Richards, Daniel P.; Stephens, Sonia H.

doi:10.1177/1050651920963439

Cited by 28 publications

(19 citation statements)

References 7 publications

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“…The outbreak of the pandemic was followed by the rapid spread of information about the threat of the coronavirus and how to cope with the virus via protective behaviours (Amidon, Nielsen, Pugfelder, Richards, & Stephens, 2021). The framework of protection motivation theory is particularly useful for understanding behaviour and motivations in such situations as individual responses to these two components of risk communication constitutes the theory's core focus.…”

Section: Introductionmentioning

confidence: 99%

Compliance without fear: Individual‐level protective behaviour during the first wave of the COVID‐19 pandemic

Jørgensen

Bor

Petersen

2021

British J Health Psychol

161

153

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Objectives. The outbreak of the COVID-19 pandemic required rapid public compliance with advice from health authorities. Here, we ask who was most likely to do so during the first wave of the pandemic.Design. Quota-sampled cross-sectional and panel data from eight Western democracies (Denmark, France, Germany, Hungary, Italy, Sweden, the United Kingdom, and the United States).Methods. We fielded online public opinion surveys to 26,508 citizens between 19 March and 16 May. The surveys included questions about protective behaviour, perceptions of the pandemic (threat and self-efficacy), as well as broader attitudes towards society (institutional and interpersonal trust). We employ multilevel and fixedeffects regression models to analyse the relationship between these variables.Results. Consistent with prior research on epidemics, perceptions of threat turn out as culturally uniform determinants of both avoidant and preventive forms of protective behaviour. On this basis, authorities could foster compliance by appealing to fear of COVID-19, but there may be normative and practical limits to such a strategy. Instead, we find that another major source of compliance is a sense of self-efficacy. Using individual-level panel data, we find evidence that self-efficacy is amendable to change and exerts an effect on protective behaviour. Furthermore, the effects of fear are small among those who feel efficacious, creating a path to compliance without fear. In contrast, two other major candidates for facilitating compliance from the social sciences, interpersonal trust and institutional trust, have surprisingly little motivational power during the first wave of the COVID-19 pandemic.Conclusions. To address future waves of the pandemic, health authorities should thus focus on facilitating self-efficacy in the public. Statement of contributionWhat is already known on this subject?Health-related fear correlates with protective behaviour during epidemics including the COVID-19 pandemic.

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Section: Introductionmentioning

confidence: 99%

Compliance without fear: Individual‐level protective behaviour during the first wave of the COVID‐19 pandemic

Jørgensen

Bor

Petersen

2021

British J Health Psychol

161

153

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“…This means that the effectiveness of a current forecasting method is likely to be impacted as new preventative measures are put into place that alter spread dynamics. Conveying this understanding to the public advances knowledge of health statistics and statistical literacy in public health [ 21 , 27 - 29 ].…”

Section: Introductionmentioning

confidence: 99%

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

Lynch¹,

Gore²

2021

J Med Internet Res

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Background Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Objective COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior–day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia’s 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. Results The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days’ counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. Conclusions For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

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“…Nonetheless, Harris' chart and its replications did not correct the visual misrepresentation noted earlier and somewhat conflicted with the degree of scientific rigor in the original graph published by CDC (Amidon et al, 2020). What is more, the original FTC chart and its derivatives obscured the nature of both curves as mathematical projections and omit uncertainty involved in statistical modeling (Amidon et al, 2020).…”

Section: The "Flatten the Curve" Chart: From A Scientific Graph To A "Meme"mentioning

confidence: 92%

“…What is more, the original FTC chart and its derivatives obscured the nature of both curves as mathematical projections and omit uncertainty involved in statistical modeling (Amidon et al, 2020). They also presented rhetorical limitations by metaphorizing the pandemic risk into a "single-peak curve" instead of multiple-peak "waves," which was more akin to the reality (Amidon et al, 2020).…”

Section: The "Flatten the Curve" Chart: From A Scientific Graph To A "Meme"mentioning

confidence: 99%

Can scientists use simple infographics to convince? Effects of the “flatten the curve” charts on perceptions of and behavioral intentions toward social distancing measures during the COVID-19 pandemic

Molder

2021

Public Underst Sci

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Infographics of modest complexity are commonly used to convey knowledge to non-experts. However, little is known regarding how the use of infographics may convince the public and lead to massive behavioral changes in response to an acute cause. In March 2020, scientists and journalists revamped a scholarly published graph into the “flatten the curve” (FTC) mantra that defined the United States’ initial response to the COVID-19 pandemic. This study examined how Americans’ awareness of the flatten the curve charts relates to their perceived effectiveness of social distancing measures, perceived controllability of the pandemic, and behavioral intentions toward social distancing measures. Implications on visual communication of science are discussed.

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Visual Risk Literacy in “Flatten the Curve” COVID-19 Visualizations

Cited by 28 publications

References 7 publications

Compliance without fear: Individual‐level protective behaviour during the first wave of the COVID‐19 pandemic

Compliance without fear: Individual‐level protective behaviour during the first wave of the COVID‐19 pandemic

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

Can scientists use simple infographics to convince? Effects of the “flatten the curve” charts on perceptions of and behavioral intentions toward social distancing measures during the COVID-19 pandemic

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