A Simple Lesson from a Hockey Great for Coping with the Coronavirus

A Simple Lesson from a Hockey Great for Coping with the Coronavirus

As the coronavirus pandemic wreaks more and more medical and social havoc worldwide, we need to recall the observation of The Great One—no, not Dr. Tony Fauci of the National Institutes of Health—the other one, hockey player Wayne Gretzky, who said, “I skate to where the puck is going to be, not where it has been.”

Anticipating what’s coming is especially important in confronting an emerging infectious disease whose dynamics and possible impacts we don’t yet know. If we react too slowly to changing circumstances, we can fall off a metaphorical cliff.

There’s an old brain teaser that perfectly illustrates this point. It posits a pond of a certain size, on which there is a single lily pad. This particular species of lily pad reproduces and duplicates itself once a day, so that on Day 2, you have two lily pads. On Day 3, you have four; on Day 4, you have eight; and so on.

Here’s the teaser: If it takes the lily pads 48 days to cover the pond completely, how long will it take for the pond to be covered halfway? The answer is 47 days. Moreover, on Day 40, you’d still hardly know the lily pads were there. If that happens with a virulent, highly contagious infectious agent, you don’t know you’re in trouble until you wake up one morning to find that you’re overwhelmed.

That brings us to today’s public health mantra, “flatten the curve,” which refers to this graphic:

The blue curve is the viral equivalent of the lily pads suddenly covering the pond. A large number of people (shown on the vertical axis) become infected over a short time (horizontal axis), overwhelming our health care system with people who need hospitalization, or even an Intensive Care Unit (ICU).

If individuals and communities take steps to slow the virus’s spread, the same number of cases of the novel coronavirus, or COVID-19, will stretch out across a longer period of time, as depicted by the flatter, yellow curve. The number of cases at any given time doesn’t cross the dotted line that marks the capacity of our nation’s health care system to accommodate everyone who is very sick.

In January, the Chinese city of Wuhan experienced the phenomenon of the blue curve, and the case fatality rate, or the fraction of infections that result in death, was very high.

The graphic is, of course, a simple illustration, but an epidemic modeling group at Imperial College London released a report on March 16 with simulations that attempt to predict the number of critical care beds occupied in the UK under different scenarios. They are shown in the figure below. The most aggressive regime—a combination of case isolation, home quarantine, and social distancing of people 70 and older—gave rise to the flattest predicted curve over the next few months.

Knowing what the curve looks like and how it might be influenced in the US has been difficult up to now because of a shortage of testing kits here, which has drastically hampered effective management of the epidemic. As Marc Lipsitch, professor of epidemiology at Harvard’s T.H. Chan School of Public Health, put it, “The basic tenet of public health is to know the situation so you can deal with it appropriately. … If you don’t look, you won’t find cases.” And if you fail to find cases, many people who are infected but asymptomatic at a given time will be missed, not be quarantined, and go on to infect others.

Reinforcing the importance of testing, an article by Professor Jeffrey Shaman and collaborators published in the journal Science on March 16 offered estimates from computer modeling of the magnitude and importance of “undocumented,” or unconfirmed, cases of COVID-19. They found that 86 percent of infections were undetected in China before January’s strict travel restrictions. Most of those infections were asymptomatic or caused only mild symptoms, and the undocumented infections were 55 percent as contagious as documented ones because those infected were not coughing and sneezing and dispersing droplets.

However, because those undocumented cases were found in such large numbers, they were responsible for about two-thirds of documented infections, and people with mild symptoms were able to transmit an infection that gave rise to serious illness in others. The authors are careful to note that their findings about undocumented infections and contagiousness could be altered by different control, surveillance, and reporting practices in other countries.

Those findings explain the rapid geographic spread of the disease and suggest that control could be difficult. If applied to the US population, this modeling predicts that far more Americans were infected than the 1,629 infections reported as of early March by the US Centers for Disease Control and Prevention (CDC). The real number, the authors say, would be closer to 13,000. Given that there are now more than 16,000 confirmed cases in the US, the real number could be well over 100,000.

The US testing situation is improving by the day, with more tests being shipped, small clinical laboratories being certified, and, especially, high-capacity clinical testing companies increasingly coming online. It’s important to recognize, however, that, echoing Professor Lipsitch and reflecting the findings of the Science article, as the capacity to test for and detect the virus increases, many more cases will be discovered. That won’t necessarily be a sign of an acceleration of the number of new infections.

There’s more to the testing saga, however. The test kits currently in use in the US detect viral genetic material—RNA, in the case of coronaviruses—which can be infectious material or noninfectious fragments. Once the patient has recovered and the RNA has been cleared, the tests will be negative. If we’re trying to ascertain what proportion of the population has been infected and recovered, such post-infection testing yields “false negatives.”

Therefore, additional, essential information will need to come from “serological tests” that measure antibodies in blood, which will tell us whether a person has been recently infected with SARS-CoV-2 (the official name of the virus that causes COVID-19) and recovered. (Note that antibodies take about 10 to 14 days from exposure to the virus to appear.) Serological tests are currently being used in Singapore and China, and two are being developed at the CDC.

Understanding the full scope of the outbreak, past and present, from the results of both RNA and serological testing will provide critical data about how the virus spreads, and about the case fatality rate. Equally important in the medium- and long-term will be an understanding of the penetration, past and present, of COVID-19 in the community because knowledge about new cases and previously undiagnosed old cases may determine when the restrictions on Americans can begin to be rolled back.

If we are to avoid the catastrophe that has befallen Italy and Iran, the measures currently being recommended by federal, state, and local governments in the US—that is, trying to anticipate where the puck will be in a week or two—seem to be prudent, but this outbreak is very much a moving target. The current measures include, in varying degrees and in different locales: home quarantine of people suspected of having the virus and their contacts; separating the most vulnerable people from others; entire populations “sheltering in place”; closing schools, restaurants, bars, and shops (other than those that sell food, pharmaceuticals, and other “essential” products); canceling sports, cultural events, and other large gatherings; and restricting non-essential travel.

These mitigation interventions will certainly be inconvenient and annoying, but they will help to flatten the curve. That will tend to spread out the demands on hospitals, which must have sufficient space, supplies, and healthy staff to care for those who need hospital-level care—whether it’s for coronavirus, a stroke, trauma, or childbirth.

It’s strong, but necessary, medicine.

Dr. Miller, a physician and molecular biologist, and the co-discoverer of a critical enzyme in the influenza virus, is a senior fellow at the Pacific Research Institute. He was the founding director of the FDA’s Office of Biotechnology.

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