Pandemic Fears: When the ‘availability heuristic’ meets ‘belief bias’

The authors of this blog were recently featured in the UMass Daily Collegian and a subsequent post of the article in the ‘Overheard at UMass’ Facebook page generated a spirited debate. That spirited debate is what we have been trying to promote with this blog. To arrive at the best policy, there should be a lively and scientifically informed debate. Science does not tell you what to do. (In the words of Sabine Hossenfelder, “science does not tell you not to pee on a high voltage line, it just tells you that urine is a good conductor”). Science provides evidence, and it is up to policy makers to review the evidence from different perspectives and make a policy decision that weighs the costs and benefits. One comment on the Facebook page criticized the authors of this blog for our lack of medical credentials. It is true that none of us is a doctor or epidemiologist. Rather, we are statisticians and scientists with expertise in data analyses. In addition, we are Cognitive Psychologists with expertise in decision making. For the most part, policy responses to this pandemic have been driven by doctors and epidemiologists, but a well-rounded and better-informed policy will emerge from a debate that includes individuals with other kinds of expertise, such as the way that human decision making can go awry in light of well-documented biases.

The most powerful and common form of decision-making error is termed the ‘availability heuristic’. This error occurs when people have an inaccurate understanding of the probabilities of events because they base their understanding on personal anecdotes or anecdotes portrayed in the news media. For example, considering the news coverage of COVID-19, it may surprise many to learn that approximately as many people die every year from smoking as died during the first year of the COVID-19 pandemic (according to the CDC, both killed approximately half a million Americans).

Considering this numerical equivalence between smoking deaths and COVID deaths, we can ask what it would require to nearly eliminate all COVID-19 deaths versus all smoking deaths. In the case of smoking, the government could ban smoking, shut-down tobacco companies, burn tobacco fields, and impose harsh penalties for smoking. What might have worked with COVID-19 was the approach taken in Wuhan, China: An extreme shelter-in-place order (no exceptions) and harsh penalties for violators, combined with forcible removal from their home for anyone suspected of COVID infection, placing them in a massive state-run quarantine hospital (shades of leprosy colonies). It is clear which of these policies is more draconian, and those who would counter “but many more could have died from COVID-19 than from smoking” must be reminded that the annual smoking death toll is repeated every year, whereas no pandemic in human history has lasted indefinitely. Moreover, the Wuhan approach violates core values of a free democratic society. Instead, most democracies attempted various forms of ‘lockdown lite’. It is not clear whether these measures ultimately changed the course of the virus, but it is certainly clear that these measures resulted in the loss of education for millions of children, a looming mental health crisis, the permanent closure of local businesses, deaths from deferred cancer screening, an enormous debt burden for the next generation, and the potential starvation of millions in East Africa as the developed world focused all resources on COVID-19.

The scientists and epidemiologists directing COVID policy claim there is substantial evidence to support mitigation policies, such as social distancing, mask mandates, and school closures. To be sure, laboratory studies (and common sense) show that people cannot infect each other if sufficiently separated and that masks greatly reduce airborne virus. The real world is messy and complicated though: law-abiding citizens with the best of intentions do not always follow guidelines, masks slip off noses, people touch their face reflexively, etc. When considering the net outcome of these lockdown-lite mitigation policies, there is little evidence to suggest that they did much beyond delay waves of infections (e.g., California appeared to do better than Florida initially, owing to more extreme restrictions in California, but eventually these two states ended up with similar COVID death rates even though California did substantially more damage to its economy, mental health of its citizens, and education of its children).

Why are people dismissive of evidence suggesting that these mitigation policies have been ineffective? This is where the availability heuristic collides with the ‘belief bias’. In decision making, ‘belief bias’ is the tendency to judge the strength of an argument based on plausibility rather than on evidence and logic. For instance, in one logic puzzle, people are told that “some college professors are intellectuals” and that “some intellectuals are liberals” and then asked whether this necessarily implies that “some college professors are liberals”. Most people respond yes and yet the first two statements do not necessarily imply the third (easily shown by drawing a Venn diagram). This occurs because the common knowledge that many professors are liberals (including the authors of this blog), clouds the ability to make an accurate judgment about the implications of the two statements. As applied to COVID-19, the commonsense notion that mask mandates and social distancing ought to stop the virus clouds rational analysis of the evidence as to whether these mitigation policies work in practice.

Now that vaccines are changing the calculus at a rapid pace (the most vulnerable have been offered vaccines in the U.S., and this offer of protection is now being extended to the less vulnerable in many states), we can consider the lingering consequences of these decision biases. After a year of pandemic in which people have been consistently bombarded with messages of death and fear and constant reminders regarding mask wearing and social distancing, thoughts and behavior have been permanently altered. For example, Rutgers University recently announced not only that all students must be vaccinated before the Fall semester, but that despite this mandate, students must still wear masks, practice social distancing, and undergo regular COVID testing. Why? The CDC is currently investigating the remote possibility that vaccinated individuals are nevertheless effective spreaders of the virus. Perhaps this remote possibility is why Rutgers will impose a continuance of these mitigation policies. But even if the vaccinated can spread the disease, what possible benefit would come from these social restrictions and constant testing once all of the vulnerable and most others have been vaccinated? And at what cost? By Fall, everyone who wants a vaccine will have had the opportunity to get one and even if the virus is spreading in a silent manner among the vaccinated, the health consequences for the vaccinated will be minimal (all of the currently deployed vaccines in the U.S. are 100% effective in preventing death – “Not a single vaccinated person has died of COVID-19”).

We live in a democratic nation and individual responsibility is a pillar of our society. Once everyone has been offered a vaccine, then all restrictions should be removed (if not earlier, considering that the most vulnerable have already been offered a vaccine). The news media should stop skewing our fears by a constant focus on the worst-case examples and worst-case possibilities (evidenced in this recent study). At our core, humans are social animals, and society is the thing that emerges from our social interaction. For the last year, we have been actively suppressing that which makes us human, and it is time to stop the damage.

David Huber:

Thanks to Adrian Staub, Carlo Dallapiccola, and Rosie Cowell for helpful discussion and comments.

Will mask mandate enthusiasts please confront the data?

On March 2, Texas Governor Greg Abbott made headlines by lifting the state’s mask mandate and removing all restrictions on business capacity.  The condemnation from public health authorities was swift.  Dr. Anthony Fauci stated that this move was “inviting” another virus surge.  CNN reported that Abbott faced a “torrent of criticism,” including from President Biden, who characterized Abbott’s move as “Neanderthal thinking.”

We are now, on March 23, three weeks out from Abbott’s decision.  Here are the data on cases in Texas, from the NY Times:

On March 2, the Times reported a seven-day average of 7,259 new cases per day; as of yesterday, the seven-day average is 3,714 cases per day.  Clearly, there is no sign of a surge; far from it, the number of cases in Texas has dropped by 49%.  Note that this is much steeper drop than the 17% national decline in cases over the same period, based on the NY Times data (64,469 cases/day to 54,190 cases/day).  

Mississippi eliminated its mask mandate and business restrictions on the same day as Texas.  Almost exactly like Texas, Mississippi has seen a 50% drop in the seven-day average of new cases from March 2 (582/day) to March 22 (293/day).  Here’s the plot:

Maybe three weeks is not long enough to see the predicted rise in cases?  Three other states lifted their mask mandates earlier:  North Dakota in mid-January, and Iowa and Montana in early-to-mid February.  Here are the plots:

Not a surge in sight; in all three states, there are fewer new cases per day now than there were when the mask mandates were lifted.  

What about states that never had mask mandates in the first place?  Surely, during the periods of the worst spread of Covid-19, mask mandates must have made some difference?  The following chart shows the number of Covid-19 deaths per 100,000 people as of March 22, 2021, by state (from Statista).  I have added the arrows to indicate the 11 states that never had mask mandates.

A couple of states without mandates have had a relatively high toll (Arizona, South Dakota), and a few states without mandates have had a very low toll (Nebraska, Idaho, Alaska).  The rest are in the middle somewhere.  Overall, there is no indication that the impact of Covid-19 has been greater in states that have lacked a mask mandate.  

One year into this pandemic, policies with regard to masks and other restrictions on personal and business activity do not seem to explain the variability in the prevalence of Covid-19 between states, nor do they seem to explain changes in prevalence over time.  This is not a new observation, as the almost identical impact in Florida and California, despite their very different approaches, has been much discussed, such as in this article from March 13: 

“Nearly a year after California Gov. Gavin Newsom ordered the nation’s first statewide shutdown because of the coronavirus, masks remain mandated, indoor dining and other activities are significantly limited, and Disneyland remains closed.  By contrast, Florida has no statewide restrictions. Republican Gov. Ron DeSantis has prohibited municipalities from fining people who refuse to wear masks. And Disney World has been open since July. Despite their differing approaches, California and Florida have experienced almost identical outcomes in COVID-19 case rates.”

One possibility, of course, is that mask mandates and business restrictions don’t have much effect on the spread of Covid-19 because people’s behavior is mostly governed by their own judgment and proclivities, whatever the official rules may be; however, there is at least some evidence that mask mandates do increase mask wearing.  The other possibility is that mask wearing itself is simply not very effective in preventing community spread, in the real world, contrary to what we have been told since near the beginning of the pandemic.  Indeed, the only randomized, controlled trial that I have heard of, which was carried out in Denmark, did not find a statistically significant reduction in Covid-19 cases due to mask wearing. 

Whichever of these explanations turns out to be right – or if both are partly right – an important question is whether the supporters of mask mandates and business restrictions will start to change their views about the efficacy of these measures or, if not, will be held accountable for them by the media.  I am afraid that there is little sign of this happening, yet.  On February 26, CDC Director Rochelle Walensky stated that “Now is not the time to relax restrictions.”  But why, exactly?  It is long past time for public health and other governmental authorities to point to the actual data demonstrating the efficacy of these restrictions.  They are often espoused as ‘common sense.’  Perhaps this is a correct characterization – it does seem like mandating mask-wearing and business closures should work – but we now have more than enough data to demonstrate whether they actually do. 

I’ll note that I would welcome any argument as to why the above data should not be taken at face value.  Please reply with a comment.  

I’ll also note, however, that we should all try not to cherry-pick data from a particular time, or a particular place, that are favorable to a case for or against mask mandates. For example, on December 4 Vox published a graph very much like the one above, but showing that many of the states without mask mandates had among the highest rates of new cases in the month of October. But why focus on October? Presumably because the data from this particular month happen to fit that article’s narrative. Indeed, these were not even the most recent data at the time of publication. As the plot in the present article clearly shows, states without mask mandates have not been hit particularly hard overall, which implies that we could have focused on other months when the states without mandates did better than average. The graph I’ve shown here covers the entire pandemic; we are not picking and choosing a time period that favors any one case.

Similarly, we should avoid cherry-picking specific comparisons that make a state’s policies look good or bad. I pointed out in a previous post that the frequent comparison of Sweden’s pandemic impact to the impact in Norway and Finland, which makes it look like Sweden’s less restrictive policies have been a mistake, is quite misleading, because Sweden’s pandemic impact has actually been quite typical for European countries.

Adrian Staub (

Thanks, as usual, to Rosie Cowell, Carlo Dallapiccola, and Dave Huber.