Thoughts from the time of the COVID-19 crisis

As I write this, our world has been upended by the novel coronavirus, thrusting us headlong into the first full-blown global pandemic in more than a century. More than 2 million people worldwide have been confirmed as infected with SARS-CoV-2, and nearly 130,000 have died. The United States leads the world in both confirmed cases (more than 600,000) and fatalities (about 30,000), while Spain, where my family and I lived three years ago during my sabbatical, has suffered even more on a per capita basis.

The economic devastation that has accompanied the disease – fueled by a combination of fear of infection, shelter-in-place guidelines intended to slow the spread of the disease through social distancing, and economic anxiety – has also been staggering. In the United States alone, more than 16 million people have lost their jobs and filed for unemployment in the past few weeks. A recent IMF report projects that “the world’s economy will experience the largest recession since the Great Depression” [1].

Given all of this, I am regularly overcome with disbelief at how suddenly this plague seems to have descended on us everywhere, with grief for what has been lost, and with anxiety about what all of this portends for the future. But so far, I have been one of the lucky ones. I am still in good health, as are my wife, daughter, sister and 80-something-year-old parents. We have a comfortable home in which we are riding out this pandemic. And not only do I still have my job, but it feels as though I am working as hard as ever.

In early March, my university made the decision to move all instruction to an online-only mode for the remainder of the school year and to largely shut down our physical campus. Like so many colleagues at schools all over the country, I have suddenly had to figure out how to effectively teach remotely.

Despite all of this, the opportunity to keep working has been a godsend. My spirits are buoyed by my students’ enthusiasm and commitment. I am grateful for their inspiration, and for continued professional employment that also provides a sense of purpose. For their sake, I dare not succumb to my own sadness or sloth.

In addition to teaching an MBA course in data mining for business, I am also supervising seven student-team consulting projects. Managing such projects is always a challenging process, but this spring it is far more difficult because: (a) our clients are all dealing with significant business challenges while also operating under shelter-in-place orders, and (b) many of my students have returned home, some to various cities in the U.S. and others to India, Thailand, Taiwan, South Africa and the Philippines. Many of them also either get up very early or stay up very late to tune in to my Zoom-based data mining lectures and to attend weekly project review meetings. Despite this, all but one of our clients have renewed their commitment to our collaboration, and my students remain highly engaged. Sometimes I feel a little silly about the energy that all of us are pouring into these projects in the midst of this pandemic, but I strive to keep Gandhi’s assertion that “Whatever you do will be insignificant, but it is very important that you do it” in mind. Most days, my students and I do a good job of soldiering on. 

Sheltering-in-Place

For an extreme extrovert like me, the experience of sheltering-in-place is both uncomfortable and illuminating. In particular, one of the big discoveries from this lengthy period of forced confinement is that so much of my normal day-to-day routine involves bringing people together (classrooms, social gatherings, performance spaces) and being amidst large crowds (sporting events, mass transit, conferences, airports). And all of this, in turn, has revealed just how much of inner life is being lived (as David Brooks phrased it [2]) “in the future tense.” From speaking with friends, it is clear that many of us have benefited from being forced to redefine our relationship with time.

One benefit of this has been increased opportunities to connect with others. I have greatly enjoyed COVID-enabled Zoom calls with my extended family, high school friends, college roommates, former co-workers and other groups of friends. And it has been great to hear from many former students and colleagues as well.

One of my favorite former MBA students works at a company called Mode Analytics. When he called to check on me the other day, he reported that he, too, was busier than ever. “With all of this disruption in business caused by COVID-19,” he explained, “many of our customers are using our software in all sorts of ways to look at all kinds of data to figure out what all this means for their business.” I have heard similar things from other analytics platform vendors.

Indeed, one bizarre byproduct of this pandemic is a sudden interest in mathematical models, projections and data visualizations. So many people I routinely talk to offer up their opinion on models they have heard about that indicate how the virus is spreading, or their interpretation of a projected peak that they have seen on an interactive online graph somewhere. The sheer volume of information – and misinformation – that is available at our fingertips is breathtaking, though too many armchair epidemiologists seem to be unaware of the assumptions and thus ignorant of the limitations of what they are viewing.

In this same vein, another former student wrote me the other day with a link to a recent article [3] on fivethirtyeight.com titled, “Why It’s So Freaking Hard to Make a Good COVID-19 Model” along with a note of thanks (“helpful to have some background knowledge on what makes a good model from your decision modeling class”). The article points to many reasons that modeling the spread of the disease and its impact is challenging, including very noisy estimates of key inputs such as infection rates and mortality rates, an absence of widespread testing (and bias that is inherent in who has and has not been tested), incomplete and inconsistent tracking of fatalities caused by the virus (which are also confounded by comorbidities), the frequent delay between the time of infection and the appearance of symptoms, and varying demographics across different geographies. 

Quickly Released Science

The data scientists at the Human Rights Data Analysis Group (HRDAG) [4] are well aware of these kinds of issues. In a recent conversation, HRDAG’s executive director Megan Price (whose training includes a Ph.D. from the Rollins School of Public Health at Emory University) acknowledged that the range of model estimates for infections, hospitalizations and fatalities is not surprising given all of the input data uncertainties. Moreover, she pointed out to me that it is not at all unusual for initial epidemiological models to later show to be quite inaccurate as more information emerges, not only the “known unknowns” in the data but also increased scientific knowledge about things like testing parameters (e.g. false positive rates) and transmission mechanisms. All of this takes expertise, patience and persistence – and time.

But as today’s 24/7 news cycle and rabid social media machine continuously churn out images of overrun hospitals and stories about victims of the virus, there is a constant pressure for “scientific” information. In a recent blog post, Patrick Ball, HRDAG’s director of research and a Fellow of the American Statistical Association, warns that, “During the intense urgency of the pandemic, a lot of science is released quickly. There may be errors, the assumptions may be badly founded, and the models may be inappropriate. This means as a society and as scientifically engaged citizens, we need to be especially careful readers” [5].

One thing that I have been carefully reading is John M. Barry’s 2005 book “The Great Influenza” [6], a history of the 1918 global flu pandemic. It has brought me some comfort to understand how far our science, data collection and technology has advanced since that time. While things may not be moving as quickly as some on Twitter would like, it is clear that we will get through this with a far smaller fraction of the casualties that were suffered a century ago. (Barry reports that an estimated 50 million to 100 million people died due to the flu pandemic out of a then-total global population of 1.8 billion. In comparison, as of this writing, the worldwide reported number of COVID-19 deaths was about 130,000 out of a global population of 7.8 billion.)

But as I write this column, I remain puzzled – and haunted – by two questions:

• What is the path to something that more closely resembles what we think of as normalcy?
• What will be different on the other side?

Only time will tell.

References

1. https://blogs.imf.org/2020/04/14/the-great-lockdown-worst-economic-downturn-since-the-great-depression/
2. https://www.amazon.com/Paradise-Drive-Always-Future-Tense/dp/0743227387
3. https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/
4. https://pubsonline.informs.org/do/10.1287/LYTX.2011.06.10/full/
5. https://hrdag.org/2020/04/02/epidemiology-has-theories-we-should-study-them/
6. https://www.amazon.com/Great-Influenza-Deadliest-Pandemic-History/dp/0143036491