COVID-19 RISK ESTIMATION 1

Over-estimation of Covid-19 Risks to Healthy and Non-Elderly Predict Support for

Continuing Restrictions Past Vaccinations

WORKING PAPER

Maja Graso, PhD

Senior Lecturer

University of Otago

[email protected] COVID-19 RISK ESTIMATION 2

Over-estimation of Covid-19 Risks to Healthy and Non-Elderly Predict Support for

Continuing Restrictions Past Vaccinations

I test the possibility that people who provide higher estimates of negative consequences of Covid-19 (e.g., hospitalizations, deaths, and threats to children) will be more likely to support the ‘new normal’; continuation of restrictions for an undefined period of time starting with wide-spread access to vaccines and completed vaccinations of vulnerable people. Results based on N = 1,233 from April, 2021 suggested that people over-estimate

Covid-19 risks, and those over-estimates were consistently related to stronger support for continuing restrictions. This relationship emerged in four different samples, using core and supplementary risk estimations, and persisted after controlling for Covid-19 denialism, political ideology, and personal concern of contracting Covid-19. People were also more likely to support continuing restrictions if they believed there is scientific consensus on

Covid-19 matters, even on issues where there is none (e.g., wearing masks while driving alone). The study concludes with a discussion of the ethical implications of letting both over- and under-estimation of Covid-19 go uncorrected. Just as it is important to combat misinformation that leads people to disregard health mandates, it is crucial to examine the real possibility that people’s support for continuing risk mitigation practices may also not be based on accurate information. COVID-19 RISK ESTIMATION 3

Over-estimation of Covid-19 Risks to Healthy and Non-Elderly Predict Support for

Continuing Restrictions Past Vaccinations

The nearly universal desire to ‘flatten the curve’, prevent hospitals from becoming overwhelmed, and save lives mobilized millions to embrace numerous health-minded practices, and protect themselves and their fellow citizens from Covid-19. Now, the end of the pandemic looks closer than ever thanks to vaccines that are proving to be remarkably effective at reducing transmission, and preventing severe illnesses and deaths from Covid

(CDC, 2021a, 2021e). With the worst of the risks waning, signs of return to pre-pandemic times are rising. Some US states are dropping most of their Covid-19 restrictions (e.g.,

Florida and Texas) and others are adjusting in tandem with local conditions.

Risk, however, is not constant, nor can it be reduced to a binary variable where the outcome is either certain death or insulation from all harm (Sunstein, 2002a, 2002b, 2021).

Covid-19 comes with gradients of risk rooted in the unknown; possibilities of new and more dangerous variants, fears of continuously rising cases across the globe, uneven levels of vaccinations across states and countries, and the uncertainty of long-term effects of Covid-19

(Cohen, 2021). Despite their high effectiveness, Covid-19 vaccines – like any preventative measure – do not eliminate the risk entirely (CDC, 2021a). Some Covid-19 risks will likely remain either until global population immunity is reached through vaccinations (WHO,

2020a), the virus is no longer seen as a threat because people are protected from severe illnesses (Phillips, 2021), or it is eliminated through other strict responses (The Lancet

Infectious, 2021).

Despite vaccines not offering a ‘magic bullet’ to end the pandemic (The Lancet

Infectious, 2021), wide-spread vaccination, particularly of all vulnerable people, represents an important opportunity to re-evaluate the cost/benefit analysis of the severe restrictions, and to re-consider people’s role in this decision-making process. Continuing restrictions through COVID-19 RISK ESTIMATION 4 frequently discussed practices such as shutting borders even with vaccinated population (e.g.,

New Zealand and Australia), isolating people who test positive, implementing vaccine passports, or seeking to eliminate Covid (i.e., ‘zero Covid’) may be beneficial in many ways, but they may be harmful in others (Brown et al., 2021; Habersaat et al., 2020; Oliu-Barton et al.; The Lancet Infectious, 2021). This study does not seek to conduct such cost/benefit analysis, nor does it seek to debate public policy.

Instead, the objective is to identify one potential barrier that may make such a cost/benefit analysis challenging. Whether to continue restrictions after wide-spread vaccinations is not a decision that can be made exclusively by epidemiologists because it involves consideration of consequences in other domains of life (mental health, economics, and education, among others)(Baral et al., 2021; Habersaat et al., 2020). Therefore, this decision becomes a matter of political judgment where citizens decide which risks and collateral costs they are willing to accept once vaccines are shown to be effective at preventing the worst outcomes (hospitalizations and deaths). Best decisions for society are made if decisions are transparent, and citizens are reasonably well-informed of the policies

(Sunstein, 2002b). Covid-19 decision-making process should not be exempt from those practices (Habersaat et al., 2020).

This study builds on recent findings suggesting that people might lack appropriate information when it comes to risks of negative Covid-19 outcomes. That may cause them to severely under-estimate the risk, which may compromise health efforts (Gallotti et al., 2020;

Lammers et al., 2020; Vijaykumar et al., 2021). However, lacking appropriate information may also cause people to over-estimate risks (Rothwell & Desai, 2020). Specifically, I hypothesize that people who over-estimate Covid-19 risks will be more likely to support policies for the colloquial ‘new normal’. For the purposes of this study, the new normal is defined as a continuation of Covid-19 risk-mitigation restrictions for an undefined period of COVID-19 RISK ESTIMATION 5 time starting with completed vaccinations of vulnerable people. This expectation is informed by theory on perceptions of unknown (vs. known) risks (Kuran & Sunstein, 1999), which when left unchecked, can lead to cascades of fear-based information, convictions driven by moralization (Graso et al., 2021; Skitka et al., 2021), and ultimately, censorship. Global policies based on fear, risk over-estimation, and the pursuit of one objective at expense of all others prevent the construction of a stable foundation on which lasting, empirically-informed, and perhaps even more resilient, post-pandemic life can be built.

Theoretical Foundation

Early in 2020, due to the absence of reliable metrics to gauge harm from Covid-19, a balanced risk-assessment was not possible. When a threat cannot be quantified with accuracy, policy-makers may embrace the so-called maximin principle and choose the policy that minimizes the likelihood of a catastrophic worst-case scenario (Sunstein, 2002a). This explains why the flu, despite contributing to thousands of deaths annually (CDC, 2019), does not trigger restrictions; its worst-case scenario is generally known even after taking into account the seasonal fluctuations in severity. In contrast, models predicting numerous deaths and threats to healthcare justified previously unprecedented measures. To be sure, Covid-19 is not a harmless virus nor it is ‘just the flu’ (Piroth et al., 2021). In efforts to prevent the worst case scenario, governments and health institutions sought to educate their citizens on the dangers of Covid-19, highlight the importance of mitigation measures (e.g., physical distancing, hygiene, and masks), and combat the spread of misinformation that threatened to compromise the success of health efforts (Agley & Xiao, 2021; Gallotti et al., 2020; Grimes,

2021; Hornik et al., 2021; Vijaykumar et al., 2021; WHO, 2020b).

While erring on the side of extreme caution is defensible in this case, continuing and unexamined focus on the worst-case scenarios can lead to moralization, a process that links

Covid-19 responses (i.e., efforts to reduce harm from the virus) with matters of moral COVID-19 RISK ESTIMATION 6 imperative (Rozin et al., 1997; Skitka et al., 2021). Matters of moral imperative tend to be rigid and when questioned, they elicit moral outrage (Graso et al., 2021; Skitka et al., 2021).

Of particular relevance to the current point in time (i.e., the transition period moving societies away from restrictions(Habersaat et al., 2020)) is that this fear-based focus can also perpetuate a cascading effect where new information is not used to inform the cost-benefit analysis, but is selectively disseminated and censored. This mechanism is driven by availability heuristic (Kuran & Sunstein, 1999); a mental shortcut where the perceived likelihood of any event is dependent on how easily this event can be brought to mind. For example, information about the numbers of deaths and cases, the dangers of long Covid, or overwhelmed healthcare systems is readily available. In contrast, information about the age or comorbidities of people who died (CDC, 2021h), long-term and severe consequences of flu (Chen et al., 2017; Halle et al., 2020), or pre-Covid-19 reports of hospitals needing ice truck morgues to deal with surge in flu cases (Chen et al., 2017; Greene, 2018; Karlamangla,

2018; Macmillan, 2018; Weise & Eversley, 2013) may not be cognitively available. Such information, however, may be suppressed out of concerns that people might not comply with restrictions if they do not see Covid-19 as a significant threat. Moreover, it may even be seen as taboo, which comports with recent findings that Covid-19 health efforts have become moralized (Graso et al., 2021).

Left unchecked, fear-based availability cascades can perpetuate the over-estimation of risks, censorship, and eventually, rigid support and implementation of practices that are disproportionately deal with one threat, while undervaluing others. Indeed, the Brookings

Institute reported that Americans greatly over-estimate the severity of Covid-19 by many degrees of magnitude. For example, 35% of U.S. adults believe that half or more infected people require hospitalization for Covid-19. However, that number is likely no greater than

5% (Rothwell & Desai, 2020). COVID-19 RISK ESTIMATION 7

The downstream consequences of how people retain and recall information can create misplaced fear. And while legitimate scientific information may provide a more nuanced understanding of the pandemic, information that challenges strict measures is dismissed.

Unlike many policy risks where opinions of the public tend to clash with opinions of the experts, Covid-19 yields diverging views and disagreements between experts. Their disagreements are not driven by questions such as whether Covid-19 is real or how severe it is, but whether the extreme and unprecedented measures applied uniformly for vulnerable and non-vulnerable alike, and for extended periods of time are worth the cost (Alwan et al.,

2020; Great barrington declaration, 2020; Ioannidis, 2020; Lenzer & Brownlee, 2020). Yet, moralization and fear-based availability cascades mean that questioning the magnitude of risks can inflict reputational harms (Sunstein, 2002b). For example, after Dr. Ludvigsson, a pediatrician and epidemiologist, pointed that the risks of Covid-19 to children are extremely low (Ludvigsson et al., 2021), his claims were challenged not only on empirical grounds, but he also received intimidation and personal attacks that ultimately led to him to abandon researching and debating Covid-19 (Torjesen, 2021).

Lay people are similarly inclined to dismiss researchers and findings that go against the fear-based availability cascade. For example Graso and colleagues (Graso et al., 2021) gave participants in New Zealand (NZ) two identical research proposals to investigate human suffering related to Covid-19. Proposals differed in one way: one wanted to examine human costs that result from abandoning elimination, and the other wanted to examine costs from continuing elimination. Despite containing identical references, descriptions, and information about the methodology, the proposal that challenged elimination was seen as less methodologically sound, less reliant on accurate information, and participants showed less trust in the researchers themselves. COVID-19 RISK ESTIMATION 8

The present study reflects on the well-intended collective efforts to combat Covid-19, prevent the spread of misinformation, and increase compliance with health-minded measures.

It casts doubt on the question of whether, as an unintended consequence of Covid-19 moralization and spread of fear-centric information (Stolow et al., 2020), the existing fear- based availability cascades will stop with vaccinations. Instead, people may prefer to apply the maximin principle for the foreseeable future, preferring to let their decision-makers deal with all new and inevitable Covid-19 risks from the perspective of the worst-case scenario.

Results

Detailed methods are available below. Data is available from the author. This study tests the possibility that higher (over-estimation) of Covid-19 risk is associated with stronger support for continuing restrictions even after the most vulnerable populations have been vaccinated and after the threat of overwhelmed hospitals has passed. Maximizing confidence in the findings required multiple complementary indicators, because of the imperfections inherent in any single operationalization of Covid-19 risk perceptions among lay people. The study relied on: 1) core risk indicators or numeric estimation items that align with public surveys on Covid-19 (Rothwell & Desai, 2020), and that represent information that lay people can understand when estimating other, non-Covid risks in life (e.g., chances of surviving a cancer diagnosis), and 2) supplementary indicators (a series of numerical and factual/opinion-based questions about Covid-19 risks).

Core risk indicators were of primary interest to this study. They were given to all

1,200+ participants and all core analyses were performed with those indicators in mind. Sub- sets of supplementary indicators were assigned to participants at random with the goal of maximizing the variety of different ways of asking about Covid-19 risks and therefore increasing the confidence in findings, while minimizing participant fatigue. Due to limited COVID-19 RISK ESTIMATION 9 manuscript space, some supplementary results are reported in Supplementary Online

Materials (SOM) where noted.

The general outcome of interest in this study was the ‘new normal’ support. It was measured with ‘new normal’ practices support (NNP), a 9-item aggregate that refers to practices that should continue to be implemented after all the vulnerable populations have received their vaccinations and after everybody had a chance to get the vaccine (thus accounting for the possibility that some groups might not be able to get the vaccine at this time). The secondary measure of was a 3-item affect-based variable reflecting participants’ fears of returning to normal under same vaccination conditions (labeled as RN-Fear and administered to Samples A and B only).

Participants also reported their current compliance with mandates, mask usage, and their intent to get the vaccine (highest number also indicated that the participant has received the vaccine). Table 1 summarizes all available study materials and content by each sample.

Table 1

Study Materials and Content by Sample COVID-19 RISK ESTIMATION 10

A B C D Study Materials and Content by Sample Mturk Prolific Prolific Aus/NZ Early April Early April Mid April Mid April

Core DVs: Endorsement of Restrictions Past Vaccinations NNP support (9 items) Half* Half All All RN-fear (3 items) Half Half - - Current Behaviour Participating in contact-tracing All All All All Complying with C19 mandates All All All All Intent to get C19 vaccine All All All All Core Covid-19 Risk Assessment Estimation Items All All All All 7 numerical risk indicators Supplementary Indicators of Risk Assessment Perceptions of scientific consensus (10 items) Half** Half Half Half T/F questions about C19 risks (18 items)* Half Half Half Half T/F questions about long Covid risks (6 items)* RA RA RA RA C19 outcomes based on 1,000 (vs. 100; 1 item) RA RA RA RA Perceptions of global death toll (1 item) RA RA RA RA Exploratory Items Right to determine cost-benefit analysis All All All All (Health scientists, non-health scientists, public) Moral elevation in response to restrictions ------All Potential Controls Personal concern over contracting C19 -- -- All All Statistical literacy (3 items) All All All All Belief in conspiracy theories (limited) All All All All Age All All All All Gender All All All All Political ideology All All All All

* RA = random assignment. Half = participants either saw perceptions of scientific consensus or T/F questions. The purpose of supplementary indicators was complementing and extending the generalization of the conclusion documented with core risk indicators, and the purpose of randomly assigning only a select sub-group was minimizing participant burden.

The study relied on different samples (all approximately N = 300; see Table 8) ranging from US (A), general international (Samples B and C), and Australia/New Zealand

(D), thus ameliorating the possibility that the relationship between risk estimation and NNP is influenced by specific government reactions. Confidence in the positive relationship between

Covid-19 risk estimation and new normal support would increase if the relationship manifests independently in different samples, and if it persists after controlling for potentially competing explanations or control variables. COVID-19 RISK ESTIMATION 11

Predicting New Normal Support with Core Risk Indicators

Table 2 shows participants’ responses on core indicators. Table 10 in Methods lists the known risks of Covid-19 and provides relevant recent resources used to inform those estimation benchmarks. Importantly, Methods and SOM provide additional information that can inform the decision about the appropriateness of the label ‘over-estimation’ as opposed to

‘higher estimation’.

Table 2

Descriptive Statistics: Estimation of Negative Covid-19 Consequences per Sample

Estimation All Samples Sample A: Mturk Sample B: Prolific Sample C: Prolific Sample D: ANZ Estimates Benchmarks* Mean SD Mean SD N Mean SD N Mean SD N Mean SD N

What is the average age of a person who died with 1 78 - 81 65.46 12.12 63.97 11.84 275 66.97 11.76 294 64.32 11.91 251 66.11 12.56 384 Covid-19? 2 % of C19 deaths who were children < 1% 8.66 10.20 6.69 7.60 275 8.73 9.66 294 9.96 11.80 254 9.13 10.85 410 3 % of C19 deaths for healthy (18 - 65 years) < 1% 33.68 26.49 30.88 25.51 275 35.35 27.70 294 40.19 28.51 254 30.33 24.08 410 4 % who recover without medical intervention > 90% 64.51 25.70 67.22 26.69 274 60.01 28.06 294 62.66 22.67 254 67.07 24.53 410 5 % that a healthy person < 65 ends up in ICU < 1% 18.78 18.29 19.14 18.93 275 24.35 19.99 294 17.08 16.58 254 15.59 16.64 410 6 % that a healthy person < 65 dies < 1% 10.71 15.83 10.46 15.23 275 14.15 18.64 294 9.11 13.88 254 9.40 14.80 410 % a healthy person < 65 never recovers from Long 7 Low 19.81 21.80 17.89 21.14 275 20.23 21.91 294 19.48 21.56 254 20.99 22.30 410 Covid

Note. Participants were instructed to make estimations for a typical Western country to attenuate the possibility that their responses are driven by global differences in health capabilities. COVID-19 RISK ESTIMATION

Table 3

Descriptive Statistics and Correlation Coefficients Between Estimation of Negative Covid-19 Consequences and ‘New Normal’ Support (All

Samples)

Study 1: Descriptive Statistics and Correlations Contact Compliance Vaccine Core C-19 Estimation Indicators Conspir. Stats. Gender Age Ideology Concern Variables Mean SD N 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Core DVs: Restrictions Past Vaccinations NNP support (9 items) 4.98 1.55 947 0.35 ** 0.63 ** 0.69 ** -0.28 ** 0.27 ** 0.34 ** -0.37 ** 0.34 ** 0.25 ** 0.35 ** -0.40 ** -0.06 -0.09 ** -0.04 -0.37 ** 0.61 ** RN - Fear (3 items) 3.47 1.66 280 0.17 ** 0.29 ** 0.24 ** -0.05 0.21 ** 0.15 * -0.20 ** 0.26 ** 0.26 ** 0.32 ** -0.08 -0.11 -0.19 ** 0.15 * -0.24 ** --

Current Covid-19 Health Behaviour 3 Contact-tracing 3.13 2.42 1219 0.24 ** 0.30 ** -0.08 ** 0.15 ** 0.09 ** -0.09 ** 0.07 * 0.08 ** 0.17 ** -0.15 ** 0.02 -0.24 ** 0.31 ** -0.21 ** 0.28 ** 4 General compliance 6.10 1.52 1218 0.55 ** -0.18 ** 0.12 ** 0.24 ** -0.20 ** 0.17 ** 0.13 ** 0.21 ** -0.33 ** -0.01 -0.13 ** -0.04 -0.25 ** 0.48 ** 5 Vaccine intent 4.21 1.30 1215 -0.14 ** 0.11 ** 0.17 ** -0.20 ** 0.15 ** 0.08 ** 0.22 ** -0.39 ** 0.06 * -0.03 -0.01 -0.34 ** 0.48 **

Core Covid-19 Estimation 6 Average age of C19 death 65.46 12.12 1204 -0.26 ** -0.39 ** 0.28 ** -0.28 ** -0.22 ** -0.25 ** 0.04 0.06 * 0.04 0.06 * 0.06 -0.19 ** 7 % of C19 deaths who were children 8.66 10.20 1233 0.41 ** -0.32 ** 0.43 ** 0.43 ** 0.37 ** -0.01 -0.13 ** -0.12 ** -0.05 -0.07 * 0.17 ** 8 % of C19 deaths 0 healthy people between 18 - 65 33.68 26.49 1233 -0.34 ** 0.42 ** 0.38 ** 0.36 ** -0.09 ** -0.10 ** -0.05 -0.13 ** -0.06 * 0.30 ** 9 % of people who recover without medical intervention 64.51 25.70 1232 -0.46 ** -0.37 ** -0.34 ** 0.11 ** 0.12 ** 0.04 0.04 0.08 ** -0.23 ** 10 % healthy < 65 ends up in ICU 18.78 18.29 1233 0.76 ** 0.57 ** -0.03 -0.21 ** -0.12 ** -0.08 ** -0.05 0.27 ** 11 % healthy < 65 dies 10.71 15.83 1233 0.61 ** 0.03 -0.20 ** -0.13 ** -0.03 -0.01 0.16 ** 12 % healthy < 65 never fully recovers from long Covid 19.81 21.80 1233 -0.08 ** -0.13 ** -0.19 ** 0.00 -0.12 ** 0.32 **

Controls 13 Conspiracy beliefs 2.74 1.80 1229 -0.08 ** 0.02 0.02 0.27 ** -0.24 ** 14 Statistics literacy 1.88 1.13 1233 0.17 ** -0.06 * -0.06 -0.06 15 Gender* 0.50 0.50 1195 -0.27 ** 0.11 ** -0.10 * 16 Age 39.03 16.36 1213 0.03 -0.05 17 Political ideology 4.07 2.06 1138 -0.23 ** 18 Concern over contracting C19 61.59 35.01 658

* = p < .05, ** = p < .01; Gender (1 = male; 0 = female)

Note. ANOVA results presented in SOM show that there are mean differences in core variables of interests between samples. Because the reason behind using four samples was to expand the generalizability of the core relationship (estimation of negative Covid-19 consequences and NNP), between-group comparisons are not of interest. Results in table above are presented with all samples for simplicity and because sample-specific tests reveal largely similar patterns. SOM present results by each individual sample. In summary, while there are differences between means across four samples, the positive relationship between over-estimation of negative consequences and NNP emerges in every sample. SOM also presents results without outliers to show that the relationship persists even after eliminating responses that greatly over- or under-estimate risks. COVID-19 RISK ESTIMATION

Correlations table results suggest that higher estimations of negative outcomes of

Covid-19 (e.g., deaths among children), and lower estimations of positive outcomes

(recovery) are consistently associated with increased desire to continue restrictions, as operationalized as both NNP support (all samples) and fear of returning to normal after vaccinations (RN-Fear administered in Samples A and B only). Estimation of negative

Covid-19 outcomes is not consistently related to Covid-19 behavior (particularly contact- tracing), which may indicate differences in local laws. These results suggest that while higher risk-estimation might not consistently predict current health-minded behavior (tracing, mandates, or vaccination or intent to get vaccinated), it strongly predicts support of the ‘new normal’ (i.e., continuing restrictions after all the vulnerable groups have been vaccinated).

This relationship is particularly robust when assessing the policy-based items (NNP) as opposed to affect-based (RN-Fear). Although both are positively related to higher risk perceptions, assessment of sample-specific tables (SOM) suggest that RN-fear correlations with core indicators do not consistently reach the significance point (p < .05). Finally, results also show that those who are more likely to endorse the new normal are people who identify as women, are liberal, are more personally concerned about contracting Covid-19, who currently report compliance with health-minded behavior, and who are less likely to believe in Covid-19 conspiracy theories.

Finally, regression analysis results presented in Table 4 show that core risk-estimation indicators predict NNP support, even after controlling for gender, political ideology, conspiracy belief, and personal concern over contracting Covid-19.

Table 4

Predicting NNP1 Support with Core Indicators and Controls

1 SOM presents the same analysis with the affect-based RN-Fear as the dependent variable. This variable was only administered to Samples A and B. The core indicators similarly predict RN-Fear, but they are largely driven by perceptions that a healthy person may never fully recover from Covid-19. COVID-19 RISK ESTIMATION

Step 1 Step 2 Predictors B SE p B SE p

1 Gender -.09 .10 .378 .01 .10 .947 3 Ideology -.14 .03 .000 -.15 .03 .000 5 Conspiracy -.19 .03 .000 -.18 .03 .000 6 Concern .02 .00 .000 .02 .00 .000 7 Average age of Covid -.01 .00 .075 8 % of C19 deaths: Children .01 .01 .022 9 % of C19 deaths: Healthy between 18 - 65 .00 .00 .079 10 % recover without intervention -.01 .00 .002 11 % that a healthy person < 65 ends up in ICU .01 .00 .027 12 % that a healthy person < 65 dies .00 .01 .536 13 % that a healthy person < 65 never fully recovers .00 .00 .822

F (df) 111.77 (4, 545) 53.20 (11, 538) p < .001 p < .001

R 2 Δ .07 Model R2 .45 .52 Gender (1 = male ; 0 = female )

Predicting NNP Support with Supplementary Estimation Indicators of Covid-19

Consequences

In efforts to address the possibility that participant responses could be driven by imperfect assessment of negative consequences above, participants saw a subset of supplementary indicators, assigned to them at random (see Table 1). Accordingly, lower Ns reflect this limited and random distribution of supplementary indicators. The results below are based on analyses of all 1,200+ responses (i.e., not dissected by sample) for parsimony and because the same patterns emerge in all samples. Where noted, sample-specific results are available in SOM and study data is available through the link noted below.

Supplementary Indicators: Numerical

First, participants estimated the inevitability of the Covid-19-driven global death toll by indicating the percentage of individuals who would still be alive, if it were not for Covid-

19 (M = 67.90, SD = 24.29, N = 623). Greater perceptions that those individuals would still be alive is related to greater support for NNPs (r = .50, p < .001, N = 468), greater self-report of contact-tracing (r = .15, p < .001, N = 615), greater self-report compliance with health regulations (r = .44, p < .001, N = 614), greater intent to get the vaccine (r = .43, p < .001, N COVID-19 RISK ESTIMATION

= 612), but it is not related to RN-Fear or fear-based reactions to returning to normal (r = .08, p = .31, N = 153).

Second, a sub-set of participants made outcome estimations for people under 65 years of age based on 1,000 people (instead of 100 or 100%). Distribution of risks was forced and participants needed to separate them according to outcomes noted in Table 5.

Table 5

1000-based Forced Estimate

M (for % Correlation with Estimation of Outcomes 1,000) Equivalent SD N NNP Fear Tracing Comp. Vaccine

1 Die 48.49 4.85% 63.65 278 .14 .16 .12 * .06 .05 2 End up in ICU 92.87 9.29% 78.79 278 .23 ** .14 .07 .04 .10 3 Be hospitalized 134.64 13.46% 95.07 278 .24 ** .08 .07 .04 .03 4 Suffer from long Covid 114.01 11.40% 96.33 278 .13 .02 .03 .00 .02 5 Fully recover within 2 months 609.99 61.00% 245.53 278 -.25 *** -.13 -.09 -.04 -.06

* = p < .05, ** = p < .01, *** = p < .001

In line with the core items, over-estimation of negative outcomes (being hospitalized), and under-estimation of positive outcomes (recovering) led to stronger support of NNPs.

However, like with core items, such estimation largely did not impact individuals’ current self-reported behavior (tracing, compliance, and vaccine intent).

Supplementary Indicators: Support for the New Normal and Perceived Scientific

Consensus (PSC)

Participants assessed whether a set of statements about Covid-19 risks and ways to mitigate them receive widespread scientific support. In line with the overarching expectation, if people believe that there is strong and clear empirical evidence used to respond to risks and justify mitigation tactics, they are more likely to support the continuing restrictions past vaccinations. The objective of the study was not to quantify actual scientific conensus; evidence of such consensus and occasional lack of it is evident in other sources (Alwan et al., COVID-19 RISK ESTIMATION

2020; Lenzer & Brownlee, 2020; Phillips, 2021). Instead, the focus here was on people’s perceptions of scientific consensus (PSC).

Accordingly, the study looked at not only empirical practices for which there is little debate and high concensus (e.g., efficacy of vaccines), debate (e.g., mask-wearing and the impact of asymptomatic transmission), but also risk-mitigation practices that are not or cannot be supported with clear concensus, such as the belief that benefits of lockdowns outweigh the risks of Covid-19, elimination (Zero-Covid) being the best strategy, the need for wearing masks while driving or hiking alone, santitizing groceries, and closing parks and beaches. Correlations between PSC, DVs, and core Covid-19 estimators are presented in

Table 6.

Table 6

NNP and Perceptions of Scientific Consensus

10-item 5-item Evi. 5-item No Evi. Variables N r p r p r p

1 NNP support 496 .84 *** .81 *** .78 *** 2 RN-Fear 130 .39 *** .34 *** .32 ***

3 Contact tracing 620 .32 *** .29 *** .28 *** 4 Compliance 622 .58 *** .61 *** .49 *** 5 Vaccine intent 622 .63 *** .70 *** .50 *** 6 What is the average age of a person who died with Covid-19? 611 -.23 *** -.19 *** -.22 *** 7 % of C19 deaths who were children 627 .26 *** .18 *** .27 *** 8 % of C19 deaths who were healthy people between 18 - 65 627 .37 *** .31 *** .37 *** 9 % of people who recover without medical intervention 626 -.36 *** -.30 *** -.35 *** 10 % that a healthy person < 65 ends up in ICU 627 .32 *** .25 *** .34 *** 11 % that a healthy person < 65 dies 627 .23 *** .18 *** .25 *** 12 % that a healthy person < 65 never fully recovers from Long Covid 627 .32 *** .30 *** .29 *** *** = p < .001

Note. The 10-item PSC scale had high internal reliability (Cronbach’s α = .90) and a near-single-factor structure (see Methods and SOM for precise caveats). This suggests that participants did not substantially differentiate between practices that do have high support (e.g., vaccines help prevent serious illness), occasionally mixed empirical support (e.g., wearing masks), from practices that do not (e.g., closing beaches or parks, or sanitizing groceries). Presentation of results with complete and two sub-scales is to further show how participants’ belief in scientific consensus where such consensus is quite low is still related to NNPs. Due to random assignment of PSC items in Samples A and B (and corresponding low N), SOM also presents a correlations table containing analyses using only Samples C and D, and item-specific correlations. Conclusions from those results remain largely unchanged from conclusions presented in the main manuscript. Correlations table that shows relationship between NNP and each PSC item (reported and exploratory) and NNP is available in SOM.

Higher SPC, even with matters that are not supported with clear consensus, emerged as one of the strongest correlates with NNP support and current health-minded behaviors. At COVID-19 RISK ESTIMATION r being just under .85, the relationship between PSC and NNP was so strong that the two are practically indistinguishable from each other (Brown, 2006). Furthermore, regression results reported in SOM show how PSC predicts NNP support over and above all significant control

(ideology, concern over Covid-19, and belief in conspiracy theories), and all the core Covid-

19 indicators.

Supplementary Indicators: NNP and True/False Questions about Covid-19

As the final set of supplementary indicators, participants rated a series of statements about Covid-19. Some statements were factual and taken from public health sources, others were partially accurate (through usage of words such as many, high, some, or majority as discussed in health education outlets), and others were incorrect (see Methods and SOM for additional explanation). Of particular interest were factual items (those endorsed by CDC,

WHO, and other legitimate institutions), which are reported in Table 7. The pupose of this collection of supplementary indicators is to explore broader sources of over- or under- estimation of Covid-19 risk. Results aside from factual items (reported in SOM) should be interpreted with caution, due to rapidly evolving knowledge of Covid-19.

Table 7

NNP and Relationship with T/F Questions (full tables are presented in SOM)

Samples C and D r with r with r with r with Items Mean SD N NNPs contact- compliance vaccine

If a vaccinated person tests positive for Covid-19, it 1 F 2.07 1.34 315 -.36 ** -.27 ** -.39 ** -.53 ** means that the vaccine is not working. Mild asthma, sexually transmitted diseases (non- 2 HIV)/AIDS, and severe acne put people in a high-risk F 2.81 1.40 315 .04 -.21 ** .03 .01 category for Covid-19. In UK, if a person dies within 28 days of a positive Covid- 3 19 test, they are counted as Covid-19 death (even if the T 3.87 1.24 314 -.39 ** -.16 ** -.32 ** -.29 ** person was in terminal stages of another illness). While accounting for a small portion of the population, 4 people over 80 accounted for around half of all Covid-19 T 4.18 1.07 314 -.17 ** -.14 * -.20 ** -.14 * deaths.

5 In 2020, Covid-19 was the main cause of death in the US. F 3.52 1.59 315 .38 ** .22 ** .38 ** .28 **

Note. Means: 1 = definitely not true; 6 = definitely true * = p < .05, ** = p < .01 COVID-19 RISK ESTIMATION

NNP support was related to belief that Covid-19 was the main cause of death in the

US (incorrect), disbelief that UK Covid-19 deaths are reported if they occur within 28 days of a positive test, and disbilief that people over 80 account for half of all Covid-19 deaths (see

Methods for caveats). Incorrectly held belief about vaccines (Item 1) is negatively related to

NNP support and health-minded behaviour. Importantly, this incorrect belief is also strongly related to lower likelihood to get vaccinated against Covid-19.

Discussion

This study offers several conclusions. First, greater estimates of Covid-19 risks were reliably related to stronger support of NNPs. This relationship was robust and emerged when looking at the core Covid-19 estimation items as well as the supplementary indicators, and it emerged independently in geographically distinct samples. Importantly, the relationship emerged even after considering the impact of C19 denialism, political ideology, statistics literacy, and even personal concern of contracting Covid-19, and outliers who greatly over- or under-estimated Covid-19 risk (results without outliers are reported in SOM). While generally in positive direction, greater estimates of Covid-19 risks were not consistently related to participants’ current self-report compliance with health-minded measures.

Second, when interpreting the findings above, it is essential to evaluate whether the results are driven by higher estimations of Covid-19 risks (i.e., estimations that are high, but within the possible bounds), or whether they are driven by over-estimations of risk. In other words, at what threshold does the estimate become over-estimate? This delicate distinction needs to be carefully considered and even challenged empirically using the open data in this study. The current study employed several indicators to reduce the possibility that the findings are driven by imperfect and flawed indicators. Furthermore, comporting with the recent Brookings report (Rothwell & Desai, 2020), the large majority of the individuals in COVID-19 RISK ESTIMATION this study over-estimated the negative outcomes of Covid-19, with the average estimates being far higher than the most conservative estimates. For example, while it is not clear how many people with Covid-19 are hospitalized, that estimate ranges between 1% - 5%

(Rothwell & Desai, 2020), not 35% as suggested in this study (% of recovery without medical intervention), and there is no evidence that a healthy person under 65 years of age has nearly 20% chance of ending up in ICU, as the results would suggest.

This over-estimation of risk, and its corresponding relationship with continuing restrictions even with vaccinations, also emerged when looking at an array of factual variables. For example, NNPs were endorsed more strongly by people who believed incorrectly that Covid-19 was the main cause of death in the US or who did not believe the that UK Covid-19 deaths are designated if the person dies within 28 days of a positive Covid-

19 test (results are reported in SOM due to space).

Under-estimation of risks was rare, but not absent. The most notable type of under- estimation was of risk that Covid-19 poses to the elderly. Specifically, participants under- estimated average age of death. In 2020 elderly care facilities indeed bore the brunt of all

Covid-19 deaths and in some countries more than 70% of all people who died with Covid-19 were in aged care facilities (Cousins, 2020; Petrequin, 2020). However, with greater awareness of Covid-19 dangers to the elderly, fewer deaths occurred in those facilities

(Ioannidis et al., 2021).

Third, perceived scientific consensus and its relationship with NNP support warrants special attention, due to its unexpectedly strong and robust impact. People were more likely to support the NNPs not only if they believed there is scientific evidence on issues that are actually supported with scientific evidence (e.g., efficacy of vaccines to prevent severe illnesses or deaths), but also on issues that have no clear consensus, such as wearing masks while driving alone. Despite this being a supplementary indicator (and thus was given to half COVID-19 RISK ESTIMATION of 1,200+ participants), it unexpectedly emerged as the single strongest predictor of NNPs over and above control variables and core indicators (analyses are reported in SOM).

This finding raises important questions about lay people’s perceptions of science,

Covid-19, and scientists’ and science communicators’ potential role in managing all misinformation. People’s belief in scientific consensus over Covid-19 matters can encourage pro-social behaviours to mitigate the risk of Covid-19 (e. g, contact-tracing, compliance, and vaccination intent). However, if their risk and fear are disproportionally high, it may become deleterious to a cost-benefit analyses essential for Covid-19 and may be misused to encourage compliance based on fear. Lay people have to power to impact public policy through democratic process. which is why their perceptions were of interest in this study. If people greatly over-estimate Covid-19 risks and if they do not differentiate between practices that are based on evidence from those that are based on social contracts or desire to create a feeling of safety (Lederer & Stolow, 2021), responses to Covid-19 may be based on false consensus, and trust in science and leadership might be further undermined.

When interpreting the results of this study, readers should be aware of several limitations. First, while the study relied on multiple indicators of Covid-19 estimation of risk that may be accessible to lay audience, it is by no means a definitive record of all core risk information about Covid-19. Future research should expand these indicators and use the open data from this study to challenge and extend the field’s knowledge of how lay people perceive negative Covid-19 consequences. Similarly, while this study only focused on continuation of restrictions based on high risk aversion, future research should consider multiple manifestations of the ‘new normal’. Through bringing attention to hygiene practices, importance of strong healthcare systems, and preventability of other viral illnesses (including flu), there are numerous positive elements of post-pandemic life that might garner broader support from people. COVID-19 RISK ESTIMATION

Second, despite relying on participants from several different countries, the study did not examine the situational, cultural, or political factors that may explain the difference between, for instance, Australia/NZ (Sample D) and US (Sample A). Therefore, the converging results across those samples should only be used as evidence of generalizability; they should not be used to make inferences about cross-cultural comparisons or population- level attitudes. Follow-up research would benefit from documenting empirically the social conditions, government responses, and perceptions of media that lead to differences between country-level scores.

Third, the study only assesses risk estimation as a predictor of continuing restrictions post-vaccinations. Other reasons could presumably influence participants’ responses. For example, while the study focused on risk to healthy and non-elderly, it could be that participants’ general pro-sociality, their perceptions of greater risk to elderly, and the potential to transmit the virus to them drives them to continue supporting restrictions.

Fourth, findings from itemized true/false indicators should be interpreted with caution. Recall that the main reason for using supplementary indicators was to expand the generalizability of the results, while minimizing participant fatigue. Establishing truthfulness and accuracy of Covid-19 items is imperfect, particularly for lay audiences. I took efforts to ensure that the sources are not selectively chosen to support or deny a particular claim, but it is possible that there are other sources that can challenge the citations provided below. Those findings should therefore only be used as a way to identify potential sources of over- or under-estimation, and this should be done while recognizing that the sources and the data represent what is known at this point in time (April – May, 2021).

The final and unanswered implication of this study is - what should be done with findings presented in this manuscript? At times when health institutes and governments seek to educate the population and warn them against conspiracy theories which often greatly COVID-19 RISK ESTIMATION under-estimate, if not fully negate Covid-19, results of this study suggest that people also over-estimate risks, and that people who over-estimate risks wish to continue restrictions even after wide-spread vaccinations. This paper does not seek to answer definitively whether it is more constructive to focus on under-estimates of Covid-19 and whether it is ethically permissible to teach people that risks of Covid-19 are likely far lower than they believe they are (Guttman & Lev, 2021). Nonetheless, both over-estimating the influence of a risk, but also under-estimating it can be costly in terms of time, money, resources, and even lives

(Sunstein, 2002b), and pandemics conditions are ripe for a medical version of the ‘Hobbesian nightmare – the war against all’ (Strong, 1990). Governments, decision-makers, and individual citizens will need to decide if it is more preferable to only confront misinformation that under-estimates the risks, or should all misinformation be confronted, regardless of whether it focuses on under- or over-estimation of Covid-19.

This is an extremely delicate dilemma as both courses of action carry different collateral costs, and it is unclear whether those are equivalent. If only under-estimates are corrected, people may continue to over-estimate the risks of Covid-19 and comply with mandates. The collateral cost here is that people’s actions may be driven by fear and remain reliant on ‘psychological shock tactics’, as admitted by Belgium health minister (Times,

2020). If both erroneous estimates are corrected, thus highlighting lack of Covid-19 dangers to healthy and non-elderly, people may start reducing their compliance and refusing vaccines; this, in turn, may prolong the pandemic. This manuscript does not answer those questions, but it raises them with hope of encouraging others to consider these ethical questions. Baral and colleagues(Baral et al., 2021) state that: “Minimizing deaths from COVID-19 over the long- term is critical, but so too is minimizing all-cause mortality and the preservation of other health and social services. Pandemics present no winners.” Outlining the most sensible path forward requires open discourse and a willingness to consider all the available information. COVID-19 RISK ESTIMATION COVID-19 RISK ESTIMATION

METHODS

Anonymized data and syntax files are available from the author.

Participants and Procedures

The data was based on diverse samples including Mturk (Sample A), Prolific

(Samples B and C), and community members in Australia and New Zealand (ANZ; Sample

D) who were recruited to participate in the study via social media. 300 platform users from

Mturk and Prolific were recruited for each Sample A, B, and C and they were paid approximately $1.50 USD. Australia/New Zealand community members were recruited through social networks and social media ad. Those community members were incentivized to participate such that for every complete response, $1 would be donated to one of the two charities of their choice. There was no deception and participants’ requests were honoured.

The community study was stopped once social media and donations achieved the available budget. Data from samples A and B were collected first (early April, 2021). Data from samples C and D were collected in mid-late April. Their responses were anonymous.

Procedures: Maximizing Indicators and Minimizing Participant Fatigue

While the core DVs and estimation indicators remained the same for all 1,200+ participants, supplementary indicators were rotated between the first (A and B) and the second (C and D) sets of samples.

Ensuring Quality Control

Data was collected from Amazon Mechanical Turk using Cloud Research platform

(Litman et al., 2017), Prolific Academic (Peer et al., 2017), and through directly recruiting from community using social media (Australia/New Zealand; ANZ). Quality was ensured by employing pre-screening criteria for Mturk and Prolific and immediately re-directing participants who failed the initial pre-screening test out of the study. All participants were prevented from taking the survey more than once and they all completed two attention checks COVID-19 RISK ESTIMATION embedded within perceived scientific consensus and knowledge items (sample item is “For quality control, please select ‘3’). The results and conclusions remained substantively unchanged regardless of whether the data were analysed with all responses or without responses who failed the check questions. Results are reported without participants who failed attention checks. Those results include 1,233 out of 1409 participants in total. Missing data was handled with pairwise deletion. Table 8 Methods presents all sample characteristics.

Table 8

Sample Characteristics

N N % Age Age Sample* % Male Time Recruitment and Location Recruited Retained Female*** Mean SD A 300 276 50.00 47.10 41.2 13.3 Early April Mturk; US B 300 297 33.70 64.60 29.1 10.5 Early April Prolific Academic; International

C 300 254 30.30 66.90 28.6 10.4 Mid - late April Prolific Academic; International

Social Media and Community D 446** 419 66.60 26.30 51.5 15.8 April Recruitment: Australia and New Zealand All 1,233 48.70 48.20 39.0 16.4

* Samples A and B had the same supplementary indicators; Studies C and D had the same supplementary indicators ** Sample D recruitment was contingent upon funding and was stopped once funding was depleted. 446 participants completed the survey. *** Not all participants chose to provide their gender. They also had an option to select 'gender-diverse'.

Core DVs and Risk Estimation Measures

New Normal Practices (NNPs) Support (Continuing Covid-19 Restrictions)

This set of items was assigned at random to half of the participants in Samples A and

B, and everybody in Samples C and D (see Table 1). Nine items captured the extent to which people endorse the continuation of the Covid-19 policies. Those items were selected specifically because they represent the most contentious and most frequently discussed issues pertaining to Covid-19 that receive universal attention (e.g., vaccine passports, continuing COVID-19 RISK ESTIMATION contact tracing, or lifting all mandates). Participants were given the following prompt question:

“Many countries have vaccination programs that are well under way. What policies should be implemented or continued once all the vulnerable people have been vaccinated and once everybody had a chance to get their vaccine? Indicate the extent to which you would support the following policies; 1 (I would NOT support this) to 7 (I would DEFINITELY support this)”

Table 9 shows the items, means, and Cronbach’ α coefficients for each sample. Due to the items’ high internal consistency (with Cronbach’ α coefficient > .84 within each sample) and single-factor structure (resulting from the principal axis factoring using oblique rotation), the items were collapsed to form a single score NNP support.

Table 9

NNP Support Item-based Descriptive Statistics and Reliabilities Per Sample

All Samples A: Mturk B: Prolific C: Prolific D: ANZ Items Mean SD N Mean SD N Mean SD N Mean SD N Mean SD N

If cases are rising, legally require to wear a 1 face mask or covering on all public transport 5.24 2.12 943 5.71 1.98 137 6.09 1.51 148 5.32 1.90 253 4.73 2.34 405 and flights.

Legally require Covid-19 vaccine passport to 2 4.94 2.28 941 4.59 2.38 138 4.97 2.12 147 5.33 1.93 251 4.80 2.47 405 travel internationally. Legally require Covid-19 vaccine passports 3 to access institutions (schools, universities, 4.04 2.26 944 3.95 2.35 140 4.14 2.21 148 4.43 2.06 251 3.80 2.34 405 recreation facilities, or workplaces).

If cases are rising, legally require people to wear masks ANY time they are outside 4 3.93 2.28 945 3.23 2.35 140 4.16 2.23 148 4.23 2.19 253 3.90 2.30 404 (including when they are driving by themselves). 5 Implement a program similar to COVID-19 4.13 2.07 942 4.88 1.98 138 4.61 1.87 148 4.63 1.84 251 3.39 2.07 405 Continue strict contact-tracing of all positive 6 5.49 1.96 942 5.01 2.14 140 5.55 1.57 148 5.29 1.74 252 5.77 2.12 402 Covid-19 cases. 7 Try to eliminate Covid-19. 5.39 1.95 942 6.20 1.49 140 6.19 1.30 146 5.21 1.78 252 4.93 2.21 404 Require people who test positive for Covid- 8 6.15 1.57 938 6.09 1.61 139 6.19 1.30 146 6.21 1.30 252 6.11 1.79 401 19 to self-isolate.

Lift ALL mandates and permit life as normal, 9 5.48 2.03 945 4.89 2.29 140 5.39 1.85 148 5.82 1.60 252 5.50 2.18 405 even if there are rising cases (Reversed).

Cronbach’ α coefficient 0.91 0.91 0.84 0.86 0.93

Fear of Abandoning Covid-19 Restrictions (Samples A and B only)

As an affect-based complement to practices-based assessment noted above, participants indicated how they would feel if “the world returned to ‘normal’ once all the vulnerable groups have been vaccinated” by selecting the extent of their agreement with sentiments: 1) vulnerable, 2) unsafe, or 3) worried. Participants indicated their agreement on COVID-19 RISK ESTIMATION a scale from 1 (strongly disagree) to 7 (strongly agree). Due to their high internal consistency

(Cronbach’ α coefficients were .96 and .92 for Samples A and B, respectively), items were collapsed to form a single scale labeled RN-fear of returning to normal. Because it was of secondary interest, this DV was assigned at random to half of the participants Samples A and

B only.

Covid-19 Current Behaviour (Contact-tracing, Compliance, and Vaccine Intent)

All 1,200+ participants responded to three questions indicating their current Covid-19 mitigating behaviour: 1) How often do you record your visits for contact tracing2 (e.g., manually or using a tracer app)? (1 = almost never; 7 = almost every time); 2) Overall, I have been complying with Covid-19 mandates (e.g., masks); (1 = strongly disagree; 7 = strongly agree); 3) Will you get the Covid-19 vaccine once you are eligible? (1 = Definitely not; 5 = definitely yes/received it already).

Core Covid-19 Estimations (All 1,200+ Participants)

Participants estimated seven Covid-19 outcomes. Participants were asked to make those estimates for people living in Western countries to reduce the variation in healthcare systems. Table 10 shows indicators, estimation benchmarks, and available sources for determining in general whether participants are over- or under-estimating Covid-19 risks.

2 Contact-tracing could be influenced by one’s geographical location and results pertaining to contact tracing should be interpreted with caution. COVID-19 RISK ESTIMATION

Table 10

Core Indicators, Estimation Benchmarks, and References

Estimation Core Indicators Sources Benchmarks*

What is the average Approximately (CDC, 2021b, 2021c, 2021g, 2021h; Ioannidis, 2021; age of a person who 1 78 - 81 Ioannidis et al., 2020; Zhou & Belluz, 2021) died with Covid-19?

% of C19 deaths who This estimate should be low (Bhopal et al., 2021; CDC, < 1% 2 were children 2021c; Sinha et al., 2020; Zhou & Belluz, 2021). Deaths among people < 65 without underlying health % of C19 deaths who conditions remain rare (CDC, 2021c, 2021h; Gudbjartsson et were healthy people < 1% 3 al., 2020; Ioannidis, 2021; Ioannidis et al., 2020; Ritchie et al., between 18 - 65 2021). Public health and education outlets suggest that ‘most people who contract Covid-19 recover’ without any medical treatment (Clinic, 2021). Gudbjartsson et al. (2020) found that 95% of people who contract Covid-19 recover without medical treatment. Brookings Institute summarizes that % of people who “while the percentage of people who have been infected by 4 recover without > 90%* the coronavirus needed to be hospitalized is not precisely medical intervention known, that estimates varies between 1% and 5% and it is unlikely to be much higher or lower”. However, this should be interpreted with caution as “An accurate calculation of infection fatality risk requires an accurate estimate of the number of infections, both diagnosed and undiagnosed” (Gudbjartsson et al., 2020). Majority of ICU hospitalizations are driven by elderly and people with pre-existing conditions (CDC, 2021d, 2021h; % that a healthy Dashti et al., 2021; Kompaniyets et al., 2021; Piroth et al., person < 65 ends up < 1%* 5 2021). In general, 1 – 5% of people who contract Covid end in ICU up hospitalized. For a generally healthy and fit individual, those estimates should be well below 1%. The overall Covid-19 fatality rate tends to below 1% (Ioannidis, 2021), but is influenced by geographic location % that a healthy (Hopkins, 2021). This estimate could also be influenced by < 1% 6 person < 65 dies individuals’ estimation of case or infection fatality rates, which tend to differ (Ritchie et al., 2021) and are heavily influenced by testing and accurate diagnoses. Long-term effects of Covid-19 are increasingly well- % that a healthy documented (Butler et al., 2020; Clinic, 2021; Foundation, person < 65 never 2020; Huang et al., 2021; Klok et al., 2020; The Lancet, 2021; NA 7 fully recovers from Yelin et al., 2020). At this point there is no data suggesting Long Covid that otherwise healthy individuals might never fully recover from Covid-19.

Assessing Over- and Under-estimation of Covid-19 Risks with Core Indicators COVID-19 RISK ESTIMATION

The underlying prediction was that people would over-estimate Covid-19 risks, and that this over-estimation would be linked to increased compliance and support of continuing restrictions. Means presented in Table 5 suggest that individuals greatly over-estimate the negative consequences of Covid-19, and they greatly under-estimate the potential for recovery.

Distribution of the responses suggests that according to the currently available Covid-

19 risk statistics, the percentage of people who under-estimate Covid-19 across all four samples is small. SOM provides additional data, including a visual representation of frequency distributions. For example, out of 1204 participants who provided an estimate for the average age of a person who died of Covid-19, 34 individuals (2.8%) estimated the average age of Covid-19 death to be above 82. Similarly, out of 1233 who estimated the average age of recovery without any medical intervention, 154 people (12.49%) estimated that more than 90% of the people recover, and only 63 (5%) estimated that more than 95% of the people recover without any medical intervention. Those recovery estimates are still within the possible range.

Supplementary Indicators of Covid-19 Risks

Mindful of the challenges and imperfections inherent in quantifying accurately Covid-

19 risk estimation, the study included several sets of questions that aimed to broaden the reliability of lay people’s risk-assessment and identify the potential source of over- or under- estimation. Observing that over-estimations captured by supplementary indicators are also related to support of the new normal would increase the confidence in the overall conclusion.

Supplementary Risk Indicator 1: Estimates based on 1,000 & NNP Support

Participants made outcome estimation based on 1,000 people (instead of 100 corresponding to 100%). 65 years of age was used as the cut-off which is consistent with COVID-19 RISK ESTIMATION

CDC cut-off for Covid-19 risk communication (CDC, 2021c; Ioannidis et al., 2020, 2021).

Drawing conclusions by using 1,000 (vs. only 100) offers greater ability to detect under- or over-estimates in interpretation. The Qualtrics mode was set up in such a way that participants were forced to distribute risks so that they add up the numbers to 1,000. While this approach has limitatins in that a Covid-19 patient can suffer from long Covid-19 and be hospitalized, it was chosen as a group-level complement to individual-based questions above.

Instructions were:

“Consider 1,000 healthy, fit, and non-elderly (under 65 years of age) individuals who have no major health conditions. If they contract Covid-19, indicate how many of them will experience outcomes noted below: 1) die, 2) end up in intensive care unit (ICU), 3) be hospitalized (non-ICU), 4) suffer debilitating long Covid and not be able to work, and 5) fully recover within 2 months.”

Participants adjusted the sliding bars to show how many of those 1,000 people will experience each of those outcomes. The distribution had to amount to 1,000. Participants provided their response on a slider scale ranging from 0 (almost nobody) to 1000 (almost everybody). The scale had no anchor points which could have clouded participants’ judgment.

Supplementary Risk Indicator 2: Perceptions of Global Death Toll

In contrast to the above-mentioned indicators of health risks, here the participants reflected on millions of Covid-19 deaths around the world and they were asked to indicate how many of those individuals would still be alive, if it were not for Covid. Specifically, participants were asked to:

“Consider the millions of individuals who have died with Covid-19 across the world. What percentage of those individuals who died would STILL BE ALIVE, if it weren't for Covid-19? Select lower % numbers if you believe that almost nobody would still be alive (i.e., they would die of other causes or old age around the same time). Select higher % numbers if you believe that almost everybody would still be alive.” COVID-19 RISK ESTIMATION

Participants provided their estimate on a slider scale without visual pointer anchored on the scale. Labels were positioned above the scale noting 0 (almost nobody) to 100 (almost everybody).

Supplementary Risk Indicator #3: Perceptions of Scientific Concensus

Participants assessed ten Covid-19 risk-mitigation practices and they indicated the extent to which they believe that those practices are supported by scientific evidence. Items were selected based on the extent of their media coverage. SOM contains additional PSC items from Samples A and B. Participants were asked to:

“Consider the amount of scientific evidence available to support each of the statements below. Select lower numbers if you think there is no or little evidence that supports that claim. Select higher numbers if you think there is clear evidence that supports that claim.” 0 = No evidence to 6 = Clear evidence. Middle numbers were labeled as ‘Mixed evidence’. Numbers were recorded to 1 – 7 scale.

Exact Wording Source

1 Getting a COVID-19 vaccine helps keep you CDC-issued statement; high concensus. (Katella, from getting seriously ill even if you do get 2021) COVID-19. 2 Benefits of lock-downs outweigh the costs of No clear consensus. failing to contain Covid-19. 3 Masks are effective in protecting people CDC-issued guidelines; high concensus with some against Covid-19. mixed evidence (Agley & Xiao, 2021; Bazant & Bush, 2021; Bundgaard et al., 2020; Johansson et al., 2021; Peeples, 2020; Worby & Chang, 2020). 4 In case of community outbreaks, people No clear consensus(Javid et al., 2021). While CDC should wear masks ANY time they are and WHO recommend wearing masks outside when outside, even if they are by themselves (e.g., one cannot physically distance from others, they do driving or hiking). not offer official rules on wearing masks while driving alone, hiking, or exercising alone. 5 When a person is reported as Covid-19 death, No clear consensus. (Howdon et al., 2020; Pulla, it is clear that Covid-19 was the main cause of 2020). CDC provides statistics with co-morbidities. In death. May, 2021: “For over 5% of these deaths, COVID-19 was the only cause mentioned on the death certificate. For deaths with conditions or causes in addition to COVID-19, on average, there were 4.0 additional conditions or causes per death.”(CDC, 2021j). Furthermore, some countries report Covid-19 deaths for people who have died within 28 days of a positive Covid-19 test.

6 New variants spread faster AND are also far No clear consensus(Cobey et al., 2021; Davies, deadlier than the original variant. Abbott, et al., 2021; Davies, Jarvis, et al., 2021; Frampton et al.; Graham et al., 2021; Jewell, 2021; Planas et al., 2021) COVID-19 RISK ESTIMATION

7 In case of community outbreaks, outdoor No clear consensus, no guidelines. spaces (beaches or parks) should be closed. 8 Elimination (Zero-Covid) is the best strategy. According to a recent Nature poll of Covid-19 immunologists, infectious-disease researchers, and virologists, 89% of them think that Covid-19 will become endemic and will continue to circulate around the globe and 51% believe that elimination, even from certain regions, is unlikely (Phillips, 2021). More than one-third of the respondents thought that it would be possible to eliminate Covid-19 from some regions. Whether elimination is the best strategy or not remains a matter of scientific debate. 9 People without Covid-19 symptoms should CDC advises mask-wearing (Bazant & Bush, 2021; wear masks to minimize the spread of Covid- Hornik et al., 2021; Johansson et al., 2021). The 19. answer may be influenced by the prevalence of asymptomatic trasmission (Johansson et al., 2021; Marks et al., 2021; Muller, 2021; Subramanian et al., 2021; Wilmes et al., 2021). 10 People should disinfect their groceries to No CDC or WHO guidelines. reduce their chances of contracting Covid-19.

Note. SOM presents additional PSC items presented to Samples A and B only.

For the purposes of presentation and nuance only, three different scales were created:

1) 10-item scale (α = .90); 2) 5-item scales aggregating items that have little to no empirical support (α = .78), and 3) 5-item scale aggregating items that have strong or some empirical support (α = .86). Correlation between the two 5-item sub-scales was .70, p < .001, N = 627.

Of note, both 5-item scale were shown to have a single-factor structure, as suggested by a principal axis factoring using oblique rotation. Factor analysis suggested the 10 items load on two factors (see Costello & Osborne, 2005; DeVellis, 2012; Fabrigar et al., 1999 for EFA guidelines). However, after the last item (sanitizing groceries) was removed from the 10-item scale due to its cross-loading on the second factor, only one factor emerged, accounting for

51.77% of the total variance. As noted in Results, the relationship between perceived scientific consensus and NNP remains largely unchanged; NNPs were predicted by higher perception of scientific consensus – whether there is actually consensus (e.g., vaccines offer protection against severe illnesses or deaths) or there is nearly none (e.g., closing beaches).

SOM also shows how NNPs correlate with every single PSC item reported here and COVID-19 RISK ESTIMATION additional exploratory items.

Supplementary Risk Indicator 4: True/False Questions about Covid-19

The participants were instructed to do the following:

“Covid-19 research continues to advance rapidly. As you are answering the following questions, consider information that is known right now and that is available through legitimate sources (e.g., the WHO, CDC, or [The Ministry of Health]). Some of the following statements may be true and some may be false; others may not have a definitive answer at this time.”

Answer modes differed between two waves (Samples A and B), and Samples

C and D. Samples A and B contained a mid-point, while those answers in Samples C and D did not. The objective was to ensure that the presence of a mid-point did not influence the general patterns of the relationships in this case (Nowlis et al., 2002).

Answer Mode in Samples A and B: 1 (visually presented as -3 = NOT true; Misinformation) to +3 (True; Accurate information), with a mid-point of 0 = partially true. Results were recorded to a 1 – 7 scale.

Answer Mode in Samples C and D: 1 (Definitely FALSE) to 6 (Definitely TRUE), without a mid-point. Results were recorded to a 1 – 6 scale.

Table 12

Perceptions of Covid-19 Risk: Perceptions of Truthfulness

1 If a vaccinated person tests Factual statement – False. CDC notes: “Based on what we know positive for Covid-19, it means about vaccines for other diseases and early data from clinical trials, that the vaccine is not working. experts believe that getting a COVID-19 vaccine also helps keep you from getting seriously ill even if you do get COVID-19” (CDC, 2021e).

2 For healthy and fit people under This may be generally true if dangers were reduced to a binary 50 years of age, Covid-19 variable where the outcome is either full recovery or death presents no greater risk than flu. (Ioannidis et al., 2021). Support of this statement as truthful would also require an assumption that Long Covid complications are comparable (not worse) to those of any post-viral syndrome, including flu (Chen et al., 2017).

3 Mild asthma, sexually transmitted Factual statement – False. (Baraniuk, 2021). However, moderate diseases (non-HIV/AIDS), and and severe asthma, and HIV/AIDS put people in a high-risk severe acne put people in a high- category for Covid-19 (CDC, 2021f). risk category for Covid-19. 4 People with obesity account for Generally true (de Siqueira et al., 2020; Kompaniyets et al., the majority of all Covid-19 2021). In addition, CDC notes: “Among 148,494 adults who hospitalizations. received a COVID-19 diagnosis during an emergency department (ED) or inpatient visit at 238 U.S. hospitals during March– December 2020, 28.3% had overweight and 50.8% had obesity” COVID-19 RISK ESTIMATION

(Kompaniyets et al., 2021). 5 Excess deaths are Covid-19 Unclear. (Woolf et al., 2021): “Excess deaths not attributed to deaths (i.e., More people actually COVID-19 could reflect either immediate or delayed mortality died of Covid than what is from undocumented COVID-19 infection, or non–COVID-19 reported). deaths secondary to the pandemic, such as from delayed care or behavioral health crises.” 6 Sun rays can neutralize Covid-19. True according to Ratnesar-Shumate et al. (2020) who note: “Ninety percent of infectious virus was inactivated every 6.8 minutes in simulated saliva and every 14.3 minutes in culture media when exposed to simulated sunlight representative of the summer solstice at 40°N latitude at sea level on a clear day.” 7 In UK, if a person dies within 28 Factual statement –True (PHE, 2021a, 2021b). days of a positive Covid-19 test, they are counted as Covid-19 death (even if the person was in terminal stages of another illness). 8 The majority of Covid-19 spread Mixed evidence (Griffin, 2020; Johansson et al., 2021; Long et al., is by people who are infected but 2020; Marks et al., 2021; Muller, 2021; Nogrady, 2020; Pollock & show no symptoms (e.g., people Lancaster, 2020). with no fever or cough). 9 While accounting for a small Generally true (Zhou & Belluz, 2021). When assessing Covid-19 portion of the population, people risks, people generally under-estimate the risk to the elderly people. over 80 accounted for around half of all Covid-19 deaths. 10 In 2020, Covid-19 was the main Factual statement – False. (Ahmad & Anderson, 2021). Covid- cause of death in the US. 19 was the third cause of death, with heart disease (690,882) and cancer (598,932) claiming more lives than Covid-19 (345,323). (Ahmad & Anderson, 2021). More than 581,000 people died of Covid since 2020, which is still lower than the number of people who died of heart disease and cancer in 2020. 11 In many Western countries, more Generally true with the first wave of Covid-19 in Australia with than half of all Covid-19 deaths 75% (Cousins, 2020), Belgium with 61.3% (Petrequin, 2020), and occurred in aged care facilities. Canada with more than 80% (CIHI, 2020). However, with greater awareness of aged care facilities and vaccines, this percentage is decreasing (Ioannidis et al., 2021)

12 Sun and warm weather protect False. (WHO, 2020b) people against Covid-19. 13 Risk of outdoor transmission of False. This survey was administered before the widespread Covid-19 is high. discussion of outdoor transmission took place in early May (Bulfone et al., 2021). 14 Risk of surface transmission of False. CDC summarizes: “the risk of SARS-CoV-2 infection via Covid-19 is high. the fomite transmission route is low, and generally less than 1 in 10,000, which means that each contact with a contaminated surface has less than a 1 in 10,000 chance of causing an infection” (CDC, 2021i) 15 For children, Covid-19 is far False. “In a study of US children with COVID-19 or seasonal deadlier than flu. influenza, there was no difference in hospitalization rates, intensive care unit admission rates, and mechanical ventilator use between the 2 groups. More patients hospitalized with COVID-19 than with seasonal influenza reported clinical symptoms at the time of diagnosis.” (Song et al., 2020). Another study reports that: In children, although the rate of hospitalization for COVID-19 appears to be lower than for influenza, in-hospital mortality is higher; however, low patient numbers limit this finding.” (Piroth et al., 2021) 16 Children are considered a HIGH Factual statement – False (CDC, 2021h; WHO, 2021). risk group. COVID-19 RISK ESTIMATION

17 For healthy, fit people under 25, This may be generally true if dangers were reduced to a binary Covid-19 is no more dangerous variable where the outcome is either full recovery or death than flu. (Ioannidis et al., 2021). Support of this statement as truthful would also require an assumption that Long Covid complications are comparable (not worse) to those of any post-viral syndrome, including flu (Chen et al., 2017).

18 Deaths from Covid-19 and flu are Mixed (CDC, 2019; Pulla, 2020). counted the same way.

Supplementary Risk Indicator 4: True/False Questions about Long Covid-19

Perceived risk of long Covid warranted special attention. Lack of familiarity with long Covid and undeniable toll that having Covid may have on a person suggests that estimation of long Covid risks would be of great concern to people. If people over-estimate the risk of long Covid, as evident by believing or disbelieving certain claims about it, their over-estimation should be similarly associated with greater support of NPPs. Instructions read:

“The following questions are about 'long Covid' - a term that describes the effects of Covid-19 that continue for weeks or months beyond the initial illness. Consider whether each of the following statements about long Covid is TRUE or FALSE.”

Answer Mode in Samples A and B: 1 (visually presented as -3 = NOT true; Misinformation) to +3 (True; Accurate information), with a mid-point of 0 = partially true.

Answer Mode in Samples C and D: 1 (Definitely FALSE) to 6 (Definitely TRUE), without a mid-point.

Table 13

Perceptions of Long Covid Risk

1 Many documented Covid symptoms True. (Butler et al., 2020; Kaseda & Levine, 2020; Mayo, 2021b; are primarily psychological (e.g., Yelin et al., 2020). anxiety). 2 Long Covid symptoms include True. (Butler et al., 2020; Kaseda & Levine, 2020; Taquet et al., psychological and neurological 2021). disorders. 3 Long-term lung scarring and heart Flu results in lung-scarring and heart inflammation (Chen et al., inflammation are unique to Covid-19 2017; Halle et al., 2020). (i.e., not found in flu). 4 Most people recover completely Mayo Clinic notes: “Most people who have coronavirus disease COVID-19 RISK ESTIMATION

within a few weeks. 2019 (COVID-19) recover completely within a few weeks”. Additional information: (Gudbjartsson et al., 2020; Mayo, 2021a) 5 Around a third of all people who test False. This statement represents a significant over-estimation of positive for Covid-19 will have lung risk. scarring and breathing difficulties for at least 6 months. 6 Around 15% of people with Covid- False. This statement represents a significant over-estimation of 19 will develop blood clots. risk. False, as that number refers to hospitalized Covid-19 patients, not all Covid-19 sample (Klok et al., 2020)

Potential Control Variables (All Participants)

In addition to Covid-19-specific items, all participants were asked to provide demographic characteristics (age, ethnicity/race, and gender; 1 = male; 0 = female; 3 = gender-diverse3). The study considered several other control variables that could provide supplementary explanation of the underlying relationship: 1) political ideology, 2) basic statistics literacy, and 3) Covid-19 denialism and conspiracy beliefs.

Concern over Contracting Covid-19 (Samples C and D). Participants were asked:

“How concerned or worried would you be if you or somebody close to you got Covid-19?” and provided their answer on a slider scale from 0 (not at all concerned) to 100 (extremely concerned).

Political Ideology (All samples). Participants indicated their political ideology on a scale from 1 = very liberal or left-wing to 7 = very conservative or right-wing.

Statistics Literacy (All samples). Participants were asked to solve three basic statistics problems.

Problem 2: “Consider this fictional statement: 15% of people like surveys. This means that ____ people like surveys.” Options were: a) 1500 out of 5, b) 0.15 out of 100, c) 15 out of 150, d) 15 out of 100, and e) 15 out of 1,000.

Problem 3: “200 people took a test. 20 of those people scored 100% on that test. Which of the following conclusions is TRUE?” Options were: a) 10% of the people got all questions right; b) 100% of the people got 20 questions right; c) 20% of people got all questions right; and d) 10% of the people failed the test. 3 Due to the small number of individuals who identified gender-diverse, their responses on gender are presented with people who chose not to answer that question. COVID-19 RISK ESTIMATION

Problem 3: “A school has 1000 students. Only 1 student walked to school in 2020. In 2021, 800% more students started walking to school. How many MORE students are walking to school in 2021 than they did in 2020?” Options were: 8, 18, 80, or 800.

Participants’ final scores ranged from 0 = missed all three questions to 3 = correctly answered all three questions.

Covid Denialism and Conspiracy Support (All samples; limited administration).

Belief in Covid-19 conspiracy theories was assessed with the following items: 1) Covid-19 virus is not real; it does not exist, 2) Covid-19 is caused by 5G networks, 3) Covid-19 virus is made in a lab; and 4) ‘Long Covid’ is not a medically-documented condition. Depending on the version of the survey, participants responded to at least two of those items. Items were embedded within perceived scientific consensus and general knowledge supplementary indicators. Because of different anchors, responses were recoded so that 1 = dismissal of conspiracy theory statements, and 10 = belief in conspiracy theory statements. COVID-19 RISK ESTIMATION

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