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As countries around the world are starting to lift restrictions and focus on reopening society following the COVID-19 pandemic, Christopher Gill (Boston University School of Public Health, MA, USA) discusses the concept of measuring risk as a spectrum, and the responsible reopening of society based on a more fluid measurement of perceived risk.

My name is Christopher Gill. I am a Clinician/Scientist and specialist in internal medicine and infectious diseases, with a career focus on child survival in low and middle-income countries. I have been a Professor in the Department of Global Health at Boston University School of Public Health (MA, USA) since 2002, excluding a 3-year leave of absence during which I served as the Clinical Director for the Phase III Clinical Trials program at Novartis Vaccines and Diagnostics developing their CRM-197 conjugated Meningitis ACWY vaccine.  

Investigating the reopening of society: the spectrum of risk

As with so many, I have felt deeply frustrated about my inability to contribute to the current crisis. This project emerged out of that general sense of frustration, but was actually prompted by an interaction with a Washington Post reporter who was researching a story about a live concert to be hosted in New Hampshire. He wanted my opinion about whether the mitigation strategies being employed by the sponsors of the event were realistic strategies for making this event safe (and I thought they were).  But it seemed to me that this event was merely one example of the many kinds of pragmatic social experiments that WE, as a community, should be considering as we move to reopening society. 

However, a major challenge seemed that we as a public health community had fallen onto a very black and white approach to considering COVID-19 risk, even though it is intuitively obvious that risk exists on a spectrum. The consequence of that is that If we insist on a zero-risk policy for everything, then we will forever be paralyzed by the fact that there is no way that COVID-19 risk can really, practically, usefully, be zero. Since living in a truly zero risk world is not possible, then we can choose to be paralyzed, or we can start to think about risk in shades of grey. An example was concerns about whether COVID-19 could be spread or contracted by individuals riding on bicycles. For some reason, this one really got under my skin because it viewed the risk in such a binary way. It ignored the fact that two cyclists would have to be precisely in line, timing exhalations and inhalations precisely, with no dispersal or dilution by wind, and sustained so that a sufficient dose of virus was acquired by the lagging cyclist to lead to infection. To my knowledge there has not been a single documented case of this actually occurring, and yet cyclists all wear masks as if someone this was a high threat to society. That is the level of anxiety that results when we insist on a zero-risk approach. 

The perceived need to see risk on a spectrum is ultimately what led to the idea for the ‘Risk Index’. This views risk as a continuum that emerges as the intersection of two forces: 1) the probability that by doing action X you will be infected; and 2) the consequences to the individual of that infection should they be infected. By looking at different combinations of these two vectors, we can start to map out the approximate indexed risk for a variety of scenarios. For example, we know that masks are quite effective (but not perfect) at preventing COVID-19 and that the increasing doses of exposure increase the risk of infection and the severity of infections – another example of how this is not binary. So, an elderly person going to the supermarket wearing a mask and spending minimal time in that enclosed space constitutes a pretty low risk of infection, but the consequences due to age and infirmity could be really high. At the opposite extreme, children unmasked in day care center represents a very high risk of infection, but due to their youthful ages, the consequences of infection tend to be very low. 

This is helpful for several reasons. First, the indexed score shows risk to be fungible and this allows for very different combinations of action and consequence to be considered and compared on a single scale. Secondly, and more importantly, it then permits one to ask the next set of questions, namely, ‘what should we be willing to exchange for that indexed risk?’. Stated another way, ‘how valuable is it to incur a given level of risk at a societal way?’ By combining these concepts, the framework offers an approximate model to evaluate all sorts of social experiments around the return to normality.

I love this approach because it is simple, flexible, and broadly applicable at any point in the pandemic and in any scenario. The specifics change over time, but the approach is universal.

Overcoming key challenges

An obvious challenge is that our estimates of risk and consequence and social value of different actions are hard to measure precisely, and, in the case of social value, inherently subjective. While that is true, the reality is that we make decisions, even weighty decisions, based on imperfect information all the time. COVID-19 is not exceptional in that regard and does not merit any special status. It is just another example of how we are forced to weigh risks and benefits, just in the same way that we accept risks when driving a car, or smoking a cigarette, or climbing a mountain, or even planting roses in the garden on a hot sunny day. 

The model can thus be useful as a way of framing the parameters of risk tolerance given known or assumed data, and it can be improved as more precise estimates of risk are incorporated. As such, it is a place to start debate, but which still allows us to take a more rational approach to policy making and even to our individual decisions. The framework is useful when based on loose approximations – hunches even – but becomes more useful as better data are incorporated over time. The truth is that decisions must be made. We can choose to make those decisions in an unstructured way, or we can approach with a bit more sophistication.

It is very encouraging to see that this general framework seems to have been adopted in current policy decisions. I don’t know if that is because others came up with the same realization that we did, which after all was really based on common sense, or whether some of this may be because of our paper. Either way, it is gratifying to see that this is finally the direction that we are moving.

The main points are:

1. Risk should not be seen as a binary concept: complete risk vs. zero risk. Obviously, it is on a spectrum.
2. Risk reflects the probability that an action will result in infection and the consequences of that infection. This allows for different combinations across an infinite range of scenarios. 
3. As such, it allows us to see risk as fungible, in much the same way as money is to economist, or that the DALY (disability adjusted life year) is to health epidemiologists. What that means is that certain combinations of risk and consequence may appear drastically different, but actually be functionally equivalent.  
4. By having this fungible concept of risk, we can now start to ask policy questions about what kinds of actions are worth absorbing different levels of indexed risk? That is extremely useful. 

We have all heard the metaphor of the frog in the frying pan. In a sense, I see that metaphor now running in reverse. Each day vaccinations make the world a bit safer, allow for a bit more easing up on our defenses, a closer step to the normalcy we so desperately crave, but I don’t think we are likely to see a single inflection point where we can suddenly say that normalcy has been achieved. Rather, I think it is far more likely that we approach this asymptotically, and that we will be surprised at how far we have come and how normal it all feels, but struggle to define when that state of mind was actually achieved. I am increasingly confident that this time will come and will come sooner than we think.

The importance of open research in the dissemination of information and ideas

The reason I became a scientist is that I love asking questions, answering them, pondering their implications, and then talking about this to whomever will listen. It is that last step that I think is so frustrating, because the prevalent publishing model creates immense barriers to the dissemination of information and ideas. In the US, science is largely funded by the US government, and as such is seen as being a national trust, an investment that reaps rewards for all our citizens. How then can we reconcile a journal refusing to allow free access to the fruit of those investments – potentially publicly funded – as if somehow that information is now proprietary and privileged to those who can pay? That philosophy offends me to the bone, and that is why I think Open Access is so important.

Read the full Opinion Article via F1000Reseach: A conceptual framework for reopening our society during the COVID-19 pandemic >>