Burnout has become a central theme of the COVID-19 pandemic, impacting essentially everyone in different ways, from those who were, and continue to be subject to endless, stressful ‘essential work.’ to those in the relatively privileged position of being trapped at home for more than a year. While there is a pretty clear, clinical-ish definition of burnout from an occupational or psychological perspective, it’s important to attend to the different potential types of burnout and their implications for what we should be doing with our lives, careers. In this post, I want to take the opportunity to think about what can and should be learned from the experience of being ‘burned out’ as a working quantitative epidemiologist.

For many of us working in public health – or I’ll at least speak for myself – this experience has been destabilizing because it has shaken our faith in the meaningfulness of what we do, even as it has also become a painfully relevant and routine part of everyone’s lives. This is a moment where epidemiological data are presented as frequently and casually as the weather. But it’s also a time in which epidemiological models have often been wrong, where our tools and techniques have been drawn into intense and politicized scrutiny, and as individuals we have become characters in the never-ending culture wars.

While before the pandemic, we might have been sometimes too confident in our ideas, models, and authority, many of us – again, I’ll speak for myself – are now working through a sort of crisis of confidence. Some of it just comes from the exhaustion of confronting an enormous crisis and being continually reminded of how difficult it is to make a meaningful impact on large-scale outcomes. This exhaustion will likely pass, and when it does, we need to focus on the very real needs to change, innovate, and respond more effectively to future crises that have been illuminated by the crisis.

The following three issues have – and continue to – dog me as I try to figure out what to do next. I don’t claim to have come up with all – if any – of these ideas, but instead have found them to be among the more significant challenges I’m wrestling with as we stare into the murky post-crisis future:

1. We are political actors whether or not we like it or want to be. This point is now so obvious and trite that it belongs alongside “all models are wrong” in the COVID-19 rhetorical hall of shame. But it is – I think – an existential question for those of us who work in this field. The limits of our ability to impact the covid crisis as individuals should turn our attention towards collective action in all its messy forms. We need to be able to think more broadly about ‘intervention’ than the action of the state or a quasi-governmental authority operating in a top-down fashion on populations. What this means pragmatically remains unclear to me, but I know that meaningful, sustained, and opinionated engagement with politics and social movements is the only path to long-term utility for the work we do. Otherwise, we can expect to see the same ‘shit rolls downhill’ pattern play out in the next crisis, as the benefits of the insights and innovations of public health and medicine are funnelled first to the wealthy and other privileged groups.
2. Top-down approaches to interventions generally fail, infectious disease epidemiology policy is not an exception. We can easily mistake the ability to impose a well thought-out intervention cleanly on a simulation model to be a ground-truth, base-case from which deviations represent failures. The problem with this is that when the real world is more messy and dysfunctional than the idealized one we imagine, it is a short leap to blaming individuals and groups for not getting with the program as we imagined it. I sometimes think of this as analogous to a problem I have with my daughter in the winter: If she would get her boots and coat on quickly, we wouldn’t be late for school essentially every morning. I find her inability to move at the speed I want her to to be extremely frustrating, especially on a freezing Michigan morning when she needs to wear eight layers of clothes. But the tendency to dawdle is so central to her existence – and to being a four year-old – that there is no point in comparing our outcomes against those of an alternative universe in which she is an AM speed demon. And rather than asking why she can’t move more quickly, I should probably ask why I don’t start the process of getting ready sooner and make it a bit more fun for her? (But hey, I don’t want to think about that…)
3. There are sharp limits to the tools of quantitative analysis in public health. Epidemiologists may have long prided ourselves on being one of the hard health sciences in much the same way that economists and other quantitative social scientists have long been held up as the rigorous thought-leaders of social inquiry as compared to their more-qualitative peers. But just as the faith in economic models was at least partially responsible for our collision with the financial iceberg of 2007, we should ask what role quantitative epidemiology may have played in worsening or slowing progress in this crisis, in addition to all the good it has done. For example: What was the potential impact of overly-optimistic transmission modeling projections earlier in the pandemic on the trajectory of infection and death? Is there a process for presenting modeling results and their uncertainty in a way that makes it harder to cherry-pick the most politically convenient ones? Should we be making public forecasts at all?

To me, these existential questions are frustrating and somewhat confounding, but they also reflect what is – I hope – a once-in-a-career opportunity to reassess the alignment between what I do and the impact I hope to have on the problems I want to address. While my insights are obviously specific to the realm of infectious disease epidemiology, I think they might carry over into the broader world of people who want to do good things – broadly construed – with the tools of the quantitiative sciences.