How Not to Manage Pandemics

Michael Harrington
9 min readOct 21, 2020


We find ourselves approaching Month Nine of a world-wide pandemic shutdown with only a few isolated exceptions across countries. There seems to be no end in sight. This should suggest that the particular nature of a virus pathogen defies a rational, measured social response, especially for a free democratic society.

This nature is defined by an unmeasurable risk shrouded in a fog of uncertainty that is reflected in the emotional fear incited by the coronavirus. That fear is amplified by several scientific realities: there is yet no sure treatment cure, there is yet no effective, preventive vaccine, and there is yet little verified knowledge about how the virus behaves and what effects it may have on long-term human health. These truths create the impenetrable fog of uncertainty that incites our fears.

On a societal level, fear is manifested in a loss of trust among fellow citizens who harbor different tolerances for risk and uncertainty, which leads them to question whether others share the same risk profiles and associated safety protocols and what those protocols should be.

All these factors taken together present a formidable challenge to those charged with messaging and managing an effective policy response, from politicians and agency bureaucrats to scientists and medical practitioners.

We’ve seen historic cases of how this plays out. There was the Black Death, smallpox, and frequent outbreaks of the plague during the Middle Ages. We have the Spanish flu and the polio epidemic a century ago and, in more recent times, the Avian and Swine flus, AIDS, SARS and Ebola. We have discovered, despite our interventions, that most of these pandemics run their course before dying out. The coronavirus that afflicts us today appears to have certain unique characteristics that distinguish it from other, more familiar virus pathogens. One is that it seems to spread easily and effectively, despite strict hygienic practices. Second, the health and fatality risks seem to skew more seriously against the old and the infirm.

But what is far more salient to our fates with this virus are not the epidemiological factors, but the psychological effects on society at large. The costs of these effects are largely ignored because they are based on unknown probabilities. What we need to know is what is going on inside the human brain that influences mass social behavior, with its potential for hysteria.

Over the past fifty years we’ve accumulated a wealth of psychological research that addresses behavior under extreme uncertainty. The foundational research was produced by the collaboration of two Israeli psychologists, Amos Tversky and Daniel Kahneman. Much of their work helped shape our understanding of behavior and decision-making under uncertainty and led to a Novel Prize in economics for Prof. Kahneman in 2002.

What is interesting is that some of the experimental study scenarios they developed closely approximate what we are experiencing today in real time and help to illuminate why we mismanage such uncertainty. In particular, there was an experiment then became widely known as the Asian Disease Problem.

This experiment asked subjects to imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed, assuming that the exact scientific estimate of the consequence of the programs is as follows:

If Program A is adopted, 200 people will be saved.

If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no people will be saved. Which of the two programs would you favor?

An overwhelming majority chose Program A, and saved 200 lives with certainty rather than take a gamble where 600 might die. They chose the certainty over the risk of trying to save all.

A second group of subjects got the same setup but with a choice between two other programs:

If Program C is adopted, 400 people will die.

If Program D is adopted, there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die.

When the choice was framed this way, an overwhelmingly majority chose Program D.

One can see that the expected outcomes here are identical: 200 people are saved for sure, while 400 people die for sure vs. 1/3 probability of saving all and a 2/3 probability of losing all.

The authors explain this flip as “framing” the issue either as a sure win or loss. When you frame the sure thing as a loss, people choose the gamble. But when you frame it as a gain, people pick the sure thing. The choice is determined by how the problem is framed. Furthermore, what led subjects to distinguish between a gain and a loss was a psychological state, which differs from individual to individual. We’ll explore how this may apply to the coronavirus pandemic, but first we must examine another key finding of Kahneman and Tversky’s research.

In another series of studies, they found that subjects were confused by remote probabilities. They feared a one-in-a-billion chance of loss more than they should and attached more hope to a one-in-a-billion chance of gain than they should. They treat all remote probabilities as if they are possibilities. People’s emotional response to extremely long odds leads them to reverse their usual taste for risk, and to become risk-seeking when pursuing a long-shot gain and risk-averse when faced with the extremely remote possibility of loss.

In gambles that offer a certain outcome, people willingly pay a premium beyond the expected outcome for that certainty. To predict how people actually choose when faced with radical uncertainty, one had to “weight” the probabilities with emotion. Then one can explain why people overpay when they buy insurance or lottery tickets. We reconcile this behavior as loss aversion to distinguish it from pure risk aversion.

The desire for certainty can extract a high price, but the problem we all face in life is that the only certainty is uncertainty. So, people unknowingly pay a high price for the mere illusion of certainty.

I hope we are beginning to see how these human instinctual behaviors will play out when faced with a radically uncertain virus pandemic. So far, we have two premises supported by empirical studies:

1. People react differently to risk depending on how the narrative of uncertainty is framed;

2. People overestimate remote probabilities and react emotionally to loss aversion.

In the context of a real virus pandemic we have several more confounding factors. First, unlike the Asian Disease experiment, we have no idea of the probabilities of imagined scenarios, so we are heavily influenced by psychological factors. Second is the effect of the media and the bias of its messaging. It is common knowledge in the news industry that emotional sensationalism sells the product far better than sterile information: “If it bleeds, it leads.” We see this in the emphasis of news reporting on daily death counts and unconfirmed fatality, hospitalization, and infection rates. Then these frequency counts and rates of change are projected in a straight line or even exponential function to invoke wild predictions of an apocalyptic future.

The effect on mass psychology, especially with the prevalence of unfiltered social media, transmutes rational prudence and caution into unrestrained fear and panic. This is very bad science, but profitable, if not good, journalism.

Finally, we must turn to the challenges facing public leaders in managing an uncontrollable pandemic crisis. What do they do? To fathom that we need to examine the incentives that these public officials themselves face, given that they’re subject to the same psychological effects as everyone else.

Public officials mostly face downside risk: they get blamed for obvious failure, yet the upside consists mostly of avoiding that blame. If they project 500,000 people might die if nothing is done, but then only 200,000 die, they can claim success. But if they claim only 150,000 may die and 200,000 actually die, then they are blamed for losing 50,000 lives. We saw these incentives in play early in the pandemic with wild predictions of 65% infection rates combined with 2% mortality rates, implying that millions if not tens of millions of people would die unless drastic measures were taken.

State and local officials could also avoid taking responsibility for the costs of these measures under the guise of Federal revenue sharing and disaster relief. In other words, they can pass the buck.

The result of this risk-reward incentive structure means these public officials and politicians, with few notable exceptions, are inclined to be extremely risk and loss averse, no matter what the cost. This means rational economic trade-offs are ignored in favor of extreme measures pursuing the delusion of zero-risk tolerance.

Medical experts, trained under the Hippocratic Oath to do no harm, face these same incentives, no matter how competent they are. They are trained to heal and avoid deciding life-and-death trade-offs of a deadly pandemic.

We can summarize how this story inevitably plays out and if that helps us understand how the pandemic mismanagement has unfolded in reality. When the threat was a distant one in a distant land, almost all politicians understandably downplayed the threat. The outbreaks in Italy then set off the media messaging, hyping the fear of coronavirus infection and death from Covid-19. The fact that the crisis could be politicized in an election year added fuel to the fire.

Let us recall “Flatten the curve” back in March, 2020, when exponential functions of death counts were all the rage. At this time certain governors promoted incomprehensible exponential predictions of mortality rates amid demands for complete shutdowns of society. All of this unfolded in a deep shroud of uncertainty with media accounts of catastrophes in China, Italy, and Spain.

So, the risk was framed as the ultimate loss of life with probabilities that had little basis in empirical data and were presented as likely possibilities rather than truly remote probabilities. With public criticism and accountability falling on political leaders, “flatten the curve” suddenly was transformed into perpetual lockdowns with moving goal posts.

The uncertainty surrounding the virus pathology led medical experts to issue contradictory and inconsistent directives and advice. First masks were unnecessary, then they became mandatory. A distance of six feet was determined as the range of infectious spread, but then sitting on a beach in solitude was deemed unacceptable.

Soon the data began to reveal the narrowness of the at-risk population based on age and co-morbidities, but the media focus targeted singular anomalies associated with anecdotal cases, heightening fears that Covid-19 is a deadly threat to all. The demands for zero-risk tolerance grew among those more loss averse and less affected by economic lockdowns. Life could not return to normal until a vaccine was developed, ignoring the fact that no virus vaccines are near 100% effective.

Rational health practices to bolster immune systems and protect at-risk population cohorts have been forced into one-size-fits-all safety protocols. At the same time, the deadly health effects of an endless quarantine are being ignored for this overreaction to remote possibilities. More data has started to make this clear, yet we seem rooted in the emotion-driven mistakes of the status-quo.

The risks of Covid-19 vary widely across the population based on personal characteristics, mostly age and immune health, so a general risk factor cannot be established except that it is extremely small compared to most other health risks. For example, I am a 66-year-old, Caucasian male in good health with no immune issues. I plugged my data into two online calculators of my risk for contracting the virus and my contingent risk of dying from Covid-19. My risk of contracting the virus and getting sick is 2.2% or 2 in 100. Assuming that I actually do test positive and get ill, my chance of dying in considered high at 1.08% or 1 in 100. But multiplying these two risks (I can’t die of Covid-19 if I don’t contract the virus) gives me a risk factor of dying from Covid-19 of 0.024%. That’s not 2 of 100 or even 2 out of 1000, but 2 out of 10,000.

The question, of course, is how much am I really willing to pay to avoid a risk of 2 out of 10,000? And I am in a high-risk age category. Selling Covid-19 life insurance has got to be one of the most lucrative business ideas imaginable.

Given the behaviors driven by human instinct and emotion and the incentive structures of free democratic societies, we should concede the inevitability that such crises will be mismanaged with public policy. (Authoritarian regimes, on the other hand, don’t face the same hurdles.)

This coronavirus pandemic is a global tragedy, one that is still ongoing. Unfortunately, the crisis gets magnified by our human failings. Probably the best free democracies can do is to take simple, not drastic, precautions and wait for the virus to resolve itself. We must live with that uncertainty.

Chalk it up to a costly and painful learning experience.



Michael Harrington

I am currently a tech start-up founder in the creative media original content space. Social science academic and author.