Defining Success: Zero-risk is not an option

We still do have to learn to live with all viruses

Only if we end the pandemic everywhere can we end the pandemic anywhere. The entire world has the same goal: cases of COVID-19 need to go to zero. 

Our World in Data

The above quote is from a site that aggregates data on the COVID-19 pandemic. I have seen similar ideas expressed around the internet. Though well-intentioned, this type of thinking is damaging our global public policy response. Here’s why.

Zero cases of a COVID-19 is an unattainable goal. Investing resources trying to reach a goal that is unattainable usually causes problematic second-order consequences.

Here is a more realistic expectation. Even after a vaccine exists, we expect this coronavirus to cause seasonal and localized outbreaks for many decades. We should aim to get near this baseline level of risk as quickly as possible, using the least costly measures that get us there.

We cannot eradicate viruses or even effectively prevent them from spreading by changing our actions. Though they vary in severity and prevalence at any given time, nearly all our viral enemies are still around on earth today. Polio still exists. Measles still exists. HPV still exists. Herpes still exists. Influenza still exists. West Nile still exists. Ebola still exists. Chickenpox still exists. HIV still exists. Zika still exists. The list goes on and on.

Smallpox is the only virus that infects humans ever to be eradicated and one of two viruses in total. That is a total of two viruses out of the gazillions of viruses on earth today (and gazillions underestimates the total number of viruses). Smallpox is an exceedingly rare exception to the rule.

Nature, not scientists, determine which viruses spread. There have only been a handful of viruses that have ever met the conditions necessary to attempt eradication. We knew early in 2020 that SARS-CoV-2 was definitely not one.

As this virus is not eradicable, SARS-CoV-2 would still be circulating in our environment no matter what policy actions we had taken in response to its initial spread. Even if the entire world socially distanced for weeks and the number of infections got to zero, it would be a temporary win. Before too long, outbreaks would start again. As long as we remain a naive population (not immune), new infections risk starting the pandemic cycle over again.

Risk is a game of probabilities and expected outcomes. We seek policies that reduce the odds of severe disease (benefits) without unnecessary sacrifices to other aspects of life and health (costs). In other words, we must do a trade-off analysis.

There is nothing to trade if one doesn't first have that ability to change outcomes, i.e., control. We have some amount of control, but less than we think. For example, we had enough resources and the capabilities to minimize disease in isolated, high-risk populations, such as nursing homes. We should have done this. We didn't do this. That's a topic for a different day. Despite our failures, no country ever had enough resources or capacity to prevent all resurgences and or all outbreaks of a virus that is this infectious, with so much asymptomatic spread.

Hindsight bias leads commentators and analysts to believe that outbreaks are preventable or controllable. After an outbreak occurs, it is possible to trace the series of events with precision. Each step seems to have a clear cause and effect. Did someone sing? That was the cause. We should ban singing. An analyst cannot observe how many other environments with identical initial conditions (i.e., situations where people sang) in which outbreaks did not result. This is critical context if one wants to design effective policy. If we ban every action that has ever led to a person spreading disease (hugging, buying food, saying hi, exercising, treating a patient, etc.), there would be nothing left in our lives.

Not everything is predictable. Thus is the nature of chance. Data nerds like myself will be the first to point out average trends and interesting correlations. The worst outbreaks spread in crowded neighborhoods, the worst outcomes occur in groups with poor healthcare, and most outbreaks happen near international transportation hubs. But these correlations will never be sufficient to be used as a decision threshold. They are real correlations but neither necessary nor sufficient to design policy.  They are not predictive.

Hindsight bias leads us to believe we have control. This leads to unrealistic expectations and definitions of success. When we overestimate the scale of possible benefits, strategic leaders will reject solutions that offer effective and efficient risk mitigation and instead choose to pursue an impossible goal. Trade-off analysis breaks down when you do not correctly define success.

Economists, CEOs, governors, and prime ministers are working from the assumption that long term gains will outweigh the short-term sacrifices. But those gains will not materialize, not in the ways many are imagining. Whatever the lower bound is on COVID-19 prevalence, it is non-zero. Risk will still exist. Factors outside our control set the lower bound, i.e., how fast the virus mutates, how strong our immunity remains, and the remote possibility of future technological invention. When will scientists figure out how to cure a viral disease? No one has any idea.

The graph below is a conceptual representation of available trade-offs. The shape of the curve may look familiar. It’s just a depiction of diminishing returns. If it were possible, to stop COVID-19 forever, the benefits would keep accumulating even as we adopt increasing costly preventative measures. But since we cannot eliminate the disease, we end up in a plateau region where we make marginal improvements to the COVID-19 pandemic while incurring massive costs.

Diminishing Returns from Increasingly Costly Public Health Policies. For Illustrative Purposes. Not Based on Models or Data. Image by Author.

Some believe that we should do everything in our power to save even one life. That's wrong for several reasons. To start, it is not within the set of solutions available. The techniques we use to mitigate damage from infectious disease — quarantines, shutdowns, social distancing— cause harm too. Often this is presented as a false choice, pretending as if our choice is to accept increasing economic damage in exchange for public health gains. The reality is that shutdowns and social distancing create a public health crisis of their own, causing deaths and increasing risk in different ways than the problem they seek to solve. Even if we choose to go all-in on COVID-19, zero health risk is not an option.

We should focus on where we have control: minimizing the extent of the damage done on the path to herd immunity. Instead of obsessing over which states currently have outbreaks (which is unavoidable for any highly infectious disease), policy thinkers should be using data to inform decision-makers about what is ‘as good as it gets’ when it comes to controlling infectious disease. Experts in their fields can then make informed trade-offs, ideally implementing strategies that keep us in the cost-effective part of the diminishing returns curve. Until we do this thinking, we are likely to be engaging in a type of self-flagellation.

Beyond ineffective policy, the mistaken belief that we could eliminate this virus’s spread is also sowing social unrest and preventing economic recovery. It is preventing individuals from doing the necessary risk assessment and personal trade-off analysis in their own lives. Disease is part of human existence. We will never be at zero risk, not from COVID-19, not from cancer, not from the flu. Yet, time moves on, and our lives progress. 

Fear tends to dominate our thinking. As policies everywhere continue to fail to meet impossible expectations, people get increasingly scared. The public starts to look for someone to blame. They have been told that outbreaks are preventable, so see every bump in the graph caused by personal and individual failures within their community. The fear-driven desire to see anyone, even friends or family, as the root causes of the continued pandemic leads to angry outbursts. Here in Brooklyn, I have seen strangers yelling at each other in the streets about perceived threats to their safety. This fear and anger will make recovering from this natural disaster much more painful than it needed to be.

Blame and shame have no place in a public health response. It wasn't appropriate to blame the gay community for AIDs and certainly does not make sense to blame our friends and family (or college students or beach-goers) for COVID-19. The idea that one person’s actions could set off a chain reaction that might somewhere down the line cause another type of harm is another example of hindsight bias. It assumes perfect knowledge of the consequences of a series of random events, i.e., ‘the butterfly effect.’

Chaos theory is a weak starting place for the design of effective public health policy. This sort of thinking will make us end up like Chidi in the TV show 'The Good Place.’ Chidi was so afraid that every action he took could eventually cause harm —even something small like buying a blueberry muffin—that he ended up paralyzed by indecision. Chidi sacrificed everything and accomplished nothing, good or bad. To get out of this negative cycle, we have to start being pragmatic: differentiating what is possible from what is likely, what is within our control and what is not.

The risk of infectious viral disease is not something we eliminate or avoid. This type of risk is something we accept and mitigate. We weigh different types of harm and consider a variety of stakeholders. We acknowledge that individual people measure their lives differently and have competing interests. Individuals cannot be held responsible for second or fifth order consequences of day to day choices they need to make to live. We need effective strategies that mitigate harm proportional to the costs incurred. In public health, ethical value systems often clash; autonomy may support different decisions than utilitarianism. But this is not the first time our ethical frameworks have come into conflict with each other. With this risk mindset, we can start discussing which measures have the greatest return on investment. 

As Harvard Medical School epidemiologist Julia Marcus has written about beautifully, risk is not binary. Zero risk is an imaginary goal. Our resources are not unlimited. Our lives are not infinite. Let's keep our policy solutions in the part of the diminishing returns curve where the cost-benefit analysis makes sense and builds policies that take a holistic perspective on human health.

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