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It seems like people who get infected with SARS-CoV-2 retain immunity, but we can’t be sure how long that immunity will last. We still lack the testing capabilities to be certain.eamesBot / Shutterstock

This story was updated post-publication to include information from a study published on the preprint server medRxiv on April 17, 2020.

With more than half a million cases of COVID-19 in the United States1 and the number of deaths increasing daily, it remains unclear when and how we might return to some semblance of pre-pandemic life. This leaves many grappling with an important question: Do you become immune after SARS-CoV-2 infection? And, if so, how long might that immunity last?

In 2019, the virus SARS-CoV-2 jumped to a human host for the first time, causing the disease COVID-19. When you become infected with a new virus, your body does not possess the antibodies necessary to mount a targeted immune response. Antibodies, proteins belonging to the immunoglobulin family, consist of four chains of amino acids that form a characteristic Y-shaped structure. Antibodies are manufactured by the immune system to bind to antigens (viral proteins) to neutralize viral infectivity.

When you inhale an aerosolized droplet containing SARS-CoV-2, the virus encounters the cells of the mucous membrane lining the respiratory tract. If effective contact is made, the virus binds to a particular receptor on these cells called ACE-2. After binding ACE-2, a host enzyme is co-opted to cleave the virus’ surface protein, called the spike protein, allowing the virus to enter the cell.

It appears that individuals with COVID-19 do create neutralizing antibodies—the basis of immunity.

Within the first few hours of infection, the body’s first line of defense—the innate immune response—is activated. The innate immune response is non-specific. When a “foreign” molecule is detected, innate immune cells signal to other cells to alter their response or prepare to combat infection.

In the following days, the adaptive immune response is activated, which is more specific. The adaptive immune response will peak one to two weeks post-infection and consists of antibodies and specialized immune cells. It is called the “adaptive” immune response because of its ability to tailor the response to a specific pathogen. Antibodies can neutralize viral infectivity by preventing virus from binding to receptors, blocking cell entry, or causing virus particles to aggregate.2 Once an infection has resolved, some of these antibodies remain in the body as immunological memory to be recruited for protection in the case of reinfection. To be immune to a virus is to possess this immunological memory.

Many vaccines work by activating the adaptive immune response. Inactivated virus, viral protein, or some other construct specific to a particular virus are introduced into the body as vaccines to initiate an immune response. Ideally, the body creates antibodies against the viral construct so that it can mount a succinct response when infected by the virus. However, in order to work effectively, a vaccine must provoke an immune response that is sufficiently robust. If the body only produces low concentrations of neutralizing antibodies, adequate immunological memory may not be sustained.

While there is still much that we have to learn about SARS-CoV-2, it appears that individuals with COVID-19 do create neutralizing antibodies—the basis of immunity. However, we don’t know for certain how long that immunity might offer protection. On the question of COVID-19 re-infection, Matt Frieman, a coronavirus researcher at the University of Maryland School of Medicine, commented in a recent interview with NPR: “We don’t know very much … I think there’s a very likely scenario where the virus comes through this year, and everyone gets some level of immunity to it, and if it comes back again, we will be protected from it—either completely or if you do get reinfected later, a year from now, then you have much less disease. That’s the hope, but there is no way to know that.”3

Immunity to a virus is measured by serological testing—patient blood is collected and analyzed for the presence of antibodies against a particular virus. Serological data is most informative when collected long-term, so the data we have been able to obtain on SARS-CoV-2 is limited. However, data on other coronaviruses that we’ve had the opportunity to study in more depth can inform our estimations on how this outbreak may evolve.

First, we can look to the coronaviruses that are known to cause the common cold. Following infection with one of these coronaviruses, disease is often mild; therefore, the concentration of antibodies detected in the blood is low. This is because mild disease often indicates a less robust immune response. Interestingly, it is not the virus itself that causes us to feel sick, but, rather, our body’s response to it. Typically, the sicker we feel, the stronger the immune response; therefore, after a cold, we are often only protected for a year or two against the same virus.4 While SARS-CoV-2 wouldn’t necessarily act like these common coronaviruses, the body’s response to these coronaviruses serves as a point of reference upon which to make predictions in the absence of virus-specific data.

We can also look to coronaviruses that are known to cause severe disease, such as SARS-CoV, which caused the 2002-2003 outbreak of SARS in China. One study discovered that antibodies against SARS-CoV remained in the blood of healthcare workers for 12 years after infection.5 While it is not certain that SARS-CoV-2 will provoke a response similar to that of SARS-CoV, this study provides us with information that can inform our estimates on immunity following COVID-19 and provide hope that immunity will provide long-term protection.

If immunity to SARS-CoV-2 diminishes as it does for common cold coronaviruses, it is likely that wintertime outbreaks will recur.

Scientists have also been working to analyze antibodies in samples from individuals infected with SARS-CoV-2. A research group in Finland recently published a study detailing the serological data collected from a COVID-19 patient over the course of their illness.6 Antibodies specific to SARS-CoV-2 were present within two weeks from the onset of symptoms. Similarly, another recent report analyzing patients with confirmed COVID-19 indicated that it took approximately 11-14 days for neutralizing antibodies to be detected in blood.7 Both of these studies, while preliminary, suggest that the basis for immunity is present in patients infected with SARS-CoV-2.

Another report looked at the possibility for recurrence of COVID-19 following re-infection with SARS-CoV-2.8 In this study, rhesus macaques were infected with SARS-CoV and allowed to recover after developing mild illness. Once blood samples were collected and confirmed to test positive for neutralizing antibodies, half of the infected macaques were re-challenged with the same dose of SARS-CoV-2. The re-infected macaques showed no significant viral replication or recurrence of COVID-19. While macaques “model” human immunity, not predict it, these data further support the possibility that antibodies manufactured in response to SARS-CoV-2 are protective against short-term re-infection.

We can also analyze a virus’ structure, and the information gained from sequencing the viral genome, when trying to predict its behavior. All viruses continually undergo mutation in the process of rapid replication. They lack the necessary machinery to repair changes incurred to the genetic sequence (we as humans also incur mutations to our genetic sequence daily, but we have more sophisticated genetic repair mechanisms in place). The occurrence of significant genetic changes to the viral genome that result in viable genetic changes to a virus is termed antigenic variation. We see a lot of antigenic variation in influenza viruses (thus the need to create new vaccines each year); but the coronaviruses seem to be relatively stable antigenically.4 This is because most coronaviruses have an enzyme that allows them to correct genetic errors sustained during replication. The more stable a virus remains over time, the more likely that antibodies manufactured in response to infection or vaccination will remain effective at neutralizing viral infectivity.

All this considered, it appears that immunity is retained following SARS-CoV-2 infection. So too, that immunity might persist long enough to warrant the implementation of vaccination. However, we still have much to learn about this virus, and whether there may be some cross-immunity between SARS-CoV-2 and other coronaviruses. The widespread variation in patient immune responses adds an additional layer of complexity. We still don’t have a good understanding of why people have different responses to viral infection—some of this variation is owed to genetic variation, but how and why some people have more robust immune responses and more severe disease is still unknown.4 In some cases, individuals show a high immune response because the concentration of virus is high. In other cases, individuals show a high immune response because they differ in some aspect of immune regulation or efficiency. However, as levels of immunity increase generally across a population, the population approaches what is called “herd immunity”—when the percentage of a population immune to a particular virus is sufficiently high that viral load drops below the threshold required to sustain the infection in that population.9

How the pandemic will evolve in the coming months is uncertain. Outcomes depend on a myriad of factors—the duration of immunity, the dynamics of transmission and how we mitigate those dynamics through social distancing, the development of therapeutics and or vaccines, and the ability of healthcare systems to handle COVID-19 caseloads. If immunity to SARS-CoV-2 diminishes as it does for common cold coronaviruses, it is likely that wintertime outbreaks will recur in coming years.10 Whether immunity to other coronaviruses might offer some cross protective immunity to SARS-CoV-2 will also play a role, albeit to a lesser extent. Widespread serological testing to assess the duration of immunity to SARS-CoV-2 is imperative, but many countries still lack this capability.

A recent study looking at serological data from 3,300 symptomatic and asymptomatic individuals in California estimates that there may be as many as 48,000-81,000 people who have been infected with SARS-Cov-2 in Santa Clara County, which is 50- to 85-fold more cases than we previously thought.11 This small-scale survey emphasizes the importance of serological testing in determining the true extent of infection.

The continuation of rigid social distance also hangs in a balance—one-time social distancing measures may drive the SARS-CoV-2 epidemic peak into the fall and winter months, especially if there is increased wintertime transmissibility.10 New therapeutics, vaccines, or measures such as contact tracing and quarantine—once caseloads have been reduced and testing capacity increased—might reduce the need for rigid social distancing. However, if such measures are not put in place, mathematical models predict that surveillance and recurrent social distancing may be required through 2022.10 Only time will tell.

Helen Stillwell is a research associate in immunobiology at Yale University.

References

1. The COVID Tracking Project https://covidtracking.com/data/us-daily (2020).

2. Virology Blog: About Viruses and Viral Disease. Virus neutralization by antibodies. virology.ws (2009).

3. GreenfieldBoyce, N. Do you get immunity after recovering from a case of coronavirus? NPR (2020).

4. Racaniello, V., Langel, S., Leifer, C., & Barker, B. Immune 29: Immunology of COVID-19. Immune Podcast. microbe.tv (2020).

5. Guo, X., et al. Long-Term persistence of IgG antibodies in SARS-CoV infected healthcare workers. bioRxiv (2020). Retrieved from doi: 10.1101/20202/02/12/20021386

6. Haveri, A., et al. Serological and molecular findings during SARS-CoV-2 infection: the first case study in Finland, January to February 2020. Euro Surveillance 25, (2020).

7. Zhao, J., et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clinical Infectious Diseases (2020). Retrieved from doi: 10.1093/cid/ciaa344

8. Bao, L., et al. Reinfection could not occur in SARS-CoV-2 infected rhesus macaques. bioRxiv (2020). Retrieved from doi: 10.1101/20202.03.13.990226

9. Virology Blog: About Viruses and Viral Disease. Herd immunity. virology.ws (2008).

10. Kissler, S.M. Tedijanto, C., Goldstein, E., Grad, Y.H., & Lipsitch, M. Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period. Science eabb5793 (2020).

11. Bendavid, E., et al. COVID-19 antibody seroprevalence in Santa Clara County, California. medRxiv (2020). Retrieved from doi: 10.1101/2020.04.14.20062463

Social, cultural, economic, spiritual, psychological, emotional, intellectual: Everything is outside the box. And this new sheltered-in-place experience won’t fit into old containers.Photo Illustration by Africa Studio / Shutterstock

Many of us are stuck now, sheltered in our messy dwellings. A daily walk lets me appreciate the urban landscaping; but I can’t stop to smell anything because a blue cotton bandana shields my nostrils. Indoors, constant digital dispatches chirp to earn my attention. I click on memes, status updates, and headlines, but everything is more of the same. How many ways can we repackage fear and reframe optimism? I mop the wood-laminate floor of my apartment because I hope “ocean paradise” scented Fabuloso will make my home smell a little less confining. My thoughts waft toward the old cliché: Think outside the box. I’ve always hated when people say that.

To begin with, the directions are ineffectual. You can’t tell someone to think outside the box and expect them to do it. Creativity doesn’t happen on demand. Want proof? Just try to make yourself think a brilliant thought, something original, innovative, or unique. Go ahead. Do it. Right now. You can’t, no matter how hard you try. This is why ancient people believed that inspiration comes from outside. It’s external, bestowed on each of us like a revelation or prophecy—a gift from the Muses. Which means your genius does not belong to you. The word “genius” is the Latin equivalent of the ancient Greek “daemon” (δαίμονες)—like a totem animal, or a spirit companion. A genius walks beside us. It mediates between gods and mortals. It crosses over from one realm to the next. It whispers divine truth.

We are paralyzed by the prospect of chaos, uncertainty, and entropy.

In modern times, our mythology moves the daemons away from the heavens and into the human soul. We say, “Meditate and let your spirit guide you.” Now we think genius comes from someplace deep within. The mind? The brain? The heart? Nobody knows for sure. Yet, it seems clear to us that inspiration belongs to us; it’s tangibly contained within our corporeal boundaries. That’s why we celebrate famous artists, poets, physicists, economists, entrepreneurs, and inventors. We call them visionaries. We read their biographies. We do our best to emulate their behaviors. We study the five habits of highly successful people. We practice yoga. We exercise. We brainstorm, doodle, sign up for online personal development workshops. We do whatever we can to cultivate the fertile cognitive soil in which the springtime seeds of inspiration might sprout. But still, even though we believe that a genius is one’s own, we know that we cannot direct it. Therefore, no matter how many people tell me to think outside the box, I won’t do it. I can’t. 

Even if I could, I’m not sure thinking outside the box would be worthwhile. Consider the origins of the phrase. It started with an old brain teaser. Nine dots are presented in a perfect square, lined up three by three. Connect them all, using only four straight lines, without lifting your pencil from the paper. It’s the kind of puzzle you’d find on the back of a box of Lucky Charms breakfast cereal, frivolous but tricky. The solution involves letting the lines expand out onto the empty page, into the negative space. Don’t confine your markings to the dots themselves. You need to recognize, instead, that the field is wider than you’d assume. In other words, don’t interpret the dots as a square, don’t imagine that the space is constricted. Think outside the box! 

For years, pop-psychologists, productivity coaches, and business gurus have all used the nine-dot problem to illustrate the difference between “fixation” and “insight.” They say that we look at markings on a page and immediately try to find a pattern. We fixate on whatever meaning we can ascribe to the image. In this case, we assume that nine dots make a box. And we imagine we’re supposed to stay within its boundaries—contained and confined. We bring habitual assumptions with us even though we’re confronting a unique problem. Why? Because we are paralyzed by the prospect of chaos, uncertainty, and entropy. We cling to the most familiar ways of organizing things in order to mitigate the risk that new patterns might not emerge at all, the possibility that meaning itself could cease to exist. But this knee-jerk reaction limits our capacity for problem-solving. Our customary ways of knowing become like a strip of packing tape that’s accidentally affixed to itself—you can struggle to undo it, but it just tangles up even more. In other words, your loyalty to the easiest, most common interpretations is the sticky confirmation bias that prevents you from arriving at a truly insightful solution. 

At least that’s what the experts used to say. And we all liked to believe it. But our minds don’t really work that way. The box parable appeals because it reinforces our existing fantasies about an individual’s proclivity to innovate and disrupt by thinking in unexpected ways. It’s not true. 

Studies have found that solving the nine-dot problem has nothing to do with the box. Even when test subjects were told that the solution requires going outside the square’s boundaries, most of them still couldn’t solve it. There was an increase in successful attempts so tiny that it was considered statistically insignificant, proving that the ability to arrive at a solution to the nine-dot problem has nothing to do with fixation or insight. The puzzle is just difficult, no matter which side of the box you’re standing on.

Still, I bet my twelve-year-old son could solve it. Yesterday, we unpacked a set of oil paints, delivered by Amazon. He was admiring the brushes and canvases. He was thinking about his project, trying to be creative, searching for insight. “Think inside the outside of the box,” he said.  “What does that mean?” I pushed the branded, smiling A-to-Z packaging aside and I looked at him like he was crazy. “Like with cardboard, you know, with all the little holes inside.” 

He was talking about the corrugations, those ridges that are pasted between layers of fiberboard. They were originally formed on the same fluted irons used to make the ruffled collars of Elizabethan-era fashion. At first, single faced corrugated paper—smooth on one side, ridged on the other—was used to wrap fragile glass bottles. Then, around 1890, the double-faced corrugated fiberboard with which we’re familiar was developed. And it transformed the packing and shipping industries. The new paperboard boxes were sturdy enough to replace wooden crates. It doesn’t take an engineering degree to understand how it works: The flutes provide support; the empty space in between makes it lightweight. My son is right; it’s all about what’s inside the outside of the box.

Now I can’t stop saying it to myself, “Think inside the outside of the box.” It’s a perfect little metaphor. In a way, it even sums up the primary cognitive skill I acquired in graduate school. One could argue that a PhD just means you’ve been trained to think inside the outside of boxes. What do I mean by that? Consider how corrugation gives cardboard it’s structural integrity. The empty space—what’s not there—makes it strong and light enough that it’s a useful and efficient way to carry objects. Similarly, it’s the intellectual frameworks that make our interpretations and analyses of the world hold up. An idea can’t stand on its own; it needs a structure and a foundation. It needs a box. It requires a frame. And by looking at how those frames are assembled, by seeing how they carry a concept through to communication, we’re able to do our best thinking. We look at the empty spaces—the invisible, or tacit assumptions—which lurk within the fluted folds of every intellectual construction. We recognize that our conscious understanding of lived experience is corrugated just like cardboard. 

The famous sociologist Erving Goffman said as much in 1974 when he published his essay on “Frame Analysis.” He encouraged his readers to identify the principles of organization which govern our perceptions. This work went on to inspire countless political consultants, pundits, publicists, advertisers, researchers, and marketers. It’s why we now talk often about the ways in which folks “frame the conversation.” But I doubt my son has read Goffman. He just stumbled on a beautifully succinct way to frame the concept of critical thinking. Maybe he was inspired by Dr. Seuss. 

When my kids were little, they asked for the same story every night, “Read Sneetches Daddy!” I could practically recite the whole thing from memory: “Now, the Star-belly Sneetches had bellies with stars. The Plain-belly Sneetches had none upon thars.” It’s an us-versus-them story, a fable about the way a consumption economy encourages people to compete for status, and to alienate the “other.” If you think inside the outside of the box, it’s also a scathing criticism of a culture that’s obsessed with personal and professional transformation—always reinventing and rebranding. 

One day, Sylvester McMonkey McBean shows up on the Sneetches’ beaches with a peculiar box-shaped fix-it-up machine. Sneetches go in with plain-bellies and they come out with stars. Now, anyone can be anything, for a fee. McBean charges them a fortune; he exploits the Sneetches’ insecurities. He builds an urgent market demand for transformational products. He preys on their most familiar—and therefore, cozy and comforting—norms of character assessment. He disrupts their identity politics, makes it so that there’s no clear way to tell who rightfully belongs with which group. And as a result, chaos ensues. Why? Because the Sneetches discover that longstanding divisive labels and pejorative categories no longer provide a meaningful way to organize their immediate experiences. They’ve lost their frames, the structural integrity of their worldview. They feel unhinged, destabilized, unboxed, and confused.

Social, cultural, economic, spiritual, psychological, emotional, intellectual: Everything is outside the box.

It should sound familiar. After all, we’ve been living through an era in history that’s just like the Sneetches’. The patterns and categories we heretofore used to define self and other are being challenged every day—sometimes for good, sometimes for bad. How can we know who belongs where in a digital diaspora, a virtual panacea, where anyone can find “my tribe”? What do identity, allegiance, heredity, and loyalty even mean now that these ideas can be detached from biology and birthplace? Nobody knows for sure. And that’s just the beginning: We’ve got Sylvester-McMonkey-McBean-style disruption everywhere we look. Connected technologies have transformed the ways in which we make sense of our relationships, how we communicate with one another, our definitions of intimacy. 

Even before the novel coronavirus, a new global paradigm forced us to live and work in a world that’s organized according to a geopolitical model we can barely comprehend. Sure, the familiar boundaries of statehood sometimes prohibited migrant foot traffic—but information, microbes, and financial assets still moved swiftly across borders, unimpeded. Similarly, cross-national supply-chains rearranged the rules of the marketplace. High-speed transportation disrupted how we perceive the limits of time and space. Automation upset the criteria through which we understand meritocracy and self-worth. Algorithms and artificial intelligence changed the way we think about labor, employment, and productivity. Data and privacy issues blurred the boundaries of personal sovereignty. And advances in bioengineering shook up the very notion of human nature.

Our boxes were already bursting. And now, cloistered at home in the midst of a pandemic, our most mundane work-a-day routines are dissolved, making it feel like our core values and deeply-held beliefs are about to tumble out all over the place. We can already envision the mess that is to come—in fact, we’re watching it unfurl in slow motion. Soon, the world will look like the intellectual, emotional, and economic equivalent of my 14-year-old’s bedroom. Dirty laundry is strewn across the floor, empty candy wrappers linger on dresser-tops, mud-caked sneakers are tossed in the corner, and the faint yet unmistakable stench of prepubescent body odor is ubiquitous. Nothing is copasetic. Nothing is in its place. Instead, everything is outside the box. 

It’s not creative, inspiring, or insightful. No, it’s disorienting and anxiety-provoking. I want to tidy it up as quickly as possible. I want to put things back in their familiar places. I want to restore order and eliminate chaos. But no matter how hard I try, I can’t do it, because the old boxes are ripped and torn. Their bottoms have fallen out. Now, they’re useless. Social, cultural, economic, spiritual, psychological, emotional, intellectual: Everything is outside the box. And this new sheltered-in-place experience won’t fit into old containers.

Jordan Shapiro, Ph.D., is a senior fellow for the Joan Ganz Cooney Center at Sesame Workshop and Nonresident Fellow in the Center for Universal Education at the Brookings Institution. He teaches at Temple University, and wrote a column for Forbes on global education and digital play from 2012 to 2017. His book, The New Childhood, was released by Little, Brown Spark in December 2018.

In the COVID-19 pandemic, numerous models are being used to predict the future. But as helpful as they are, they cannot make sense of themselves. They rely on epidemiologists and other modelers to interpret them. Trouble is, making predictions in a pandemic is also a philosophical exercise. We need to think about hypothetical worlds, causation, evidence, and the relationship between models and reality.1,2

The value of philosophy in this crisis is that although the pandemic is unique, many of the challenges of prediction, evidence, and modeling are general problems. Philosophers like myself are trained to see the most general contours of problems—the view from the clouds. They can help interpret scientific results and claims and offer clarity in times of uncertainty, bringing their insights down to Earth. When it comes to predicting in an outbreak, building a model is only half the battle. The other half is making sense of what it shows, what it leaves out, and what else we need to know to predict the future of COVID-19.

Prediction is about forecasting the future, or, when comparing scenarios, projecting several hypothetical futures. Because epidemiology informs public health directives, predicting is central to the field. Epidemiologists compare hypothetical worlds to help governments decide whether to implement lockdowns and social distancing measures—and when to lift them. To make this comparison, they use models to predict the evolution of the outbreak under various simulated scenarios. However, some of these simulated worlds may turn out to misrepresent the real world, and then our prediction might be off.

In his book Philosophy of Epidemiology, Alex Broadbent, a philosopher at the University of Johannesburg, argues that good epidemiological prediction requires asking, “What could possibly go wrong?” He elaborated in an interview with Nautilus, “To predict well is to be able to explain why what you predict will happen rather than the most likely hypothetical alternatives. You consider the way the world would have to be for your prediction to be true, then consider worlds in which the prediction is false.” By ruling out hypothetical worlds in which they are wrong, epidemiologists can increase their confidence that they are right. For instance, by using antibody tests to estimate previous infections in the population, public health authorities could rule out the hypothetical possibility (modeled by a team at Oxford) that the coronavirus has circulated much more widely than we think.3

One reason the dynamics of an outbreak are often more complicated than a traditional model can predict is that they result from human behavior and not just biology.

Broadbent is concerned that governments across Africa are not thinking carefully enough about what could possibly go wrong, having for the most part implemented coronavirus policies in line with the rest of the world. He believes a one-size-fits-all approach to the pandemic could prove fatal.4 The same interventions that might have worked elsewhere could have very different effects in the African context. For instance, the economic impacts of social distancing policies on all-cause mortality might be worse because so many people on the continent suffer increased food insecurity and malnutrition in an economic downturn.5 Epidemic models only represent the spread of the infection. They leave out important elements of the social world.

Another limitation of epidemic models is that they model the effect of behaviors on the spread of infection, but not the effect of a public health policy on behaviors. The latter requires understanding how a policy works. Nancy Cartwright, a philosopher at Durham University and the University of California, San Diego, suggests that “the road from ‘It works somewhere’ to ‘It will work for us’ is often long and tortuous.”6 The kinds of causal principles that make policies effective, she says, “are both local and fragile.” Principles can break in transit from one place to the other. Take the principle, “Stay-at-home policies reduce the number of social interactions.” This might be true in Wuhan, China, but might not be true in a South African township in which the policies are infeasible or in which homes are crowded. Simple extrapolation from one context to another is risky. A pandemic is global, but prediction should be local.

Predictions require assumptions that in turn require evidence. Cartwright and Jeremy Hardie, an economist and research associate at the Center for Philosophy of Natural and Social Science at the London School of Economics, represent evidence-based policy predictions using a pyramid, where each assumption is a building block.7 If evidence for any assumption is missing, the pyramid might topple. I have represented evidence-based medicine predictions using a chain of inferences, where each link in the chain is made of an alloy containing assumptions.8 If any assumption comes apart, the chain might break.

An assumption can involve, for example, the various factors supporting an intervention. Cartwright writes that “policy variables are rarely sufficient to produce a contribution [to some outcome]; they need an appropriate support team if they are to act at all.” A policy is only one slice of a complete causal pie.9 Take age, an important support factor in causal principles of social distancing. If social distancing prevents deaths primarily by preventing infections among older individuals, wherever there are fewer older individuals there may be fewer deaths to prevent—and social distancing will be less effective. This matters because South Africa and other African countries have younger populations than do Italy or China.10

The lesson that assumptions need evidence can sound obvious, but it is especially important to bear in mind when modeling. Most epidemic modeling makes assumptions about the reproductive number, the size of the susceptible population, and the infection-fatality ratio, among other parameters. The evidence for these assumptions comes from data that, in a pandemic, is often rough, especially in early days. It has been argued that nonrepresentative diagnostic testing early in the COVID-19 pandemic led to unreliable estimates of important inputs in our epidemic modeling.11

Epidemic models also don’t model all the influences of the pathogen and of our policy interventions on health and survival. For example, what matters most when comparing deaths among hypothetical worlds is how different the death toll is overall, not just the difference in deaths due to the direct physiological effects of a virus. The new coronavirus can overwhelm health systems and consume health resources needed to save non-COVID-19 patients if left unchecked. On the other hand, our policies have independent effects on financial welfare and access to regular healthcare that might in turn influence survival.

A surprising difficulty with predicting in a pandemic is that the same pathogen can behave differently in different settings. Infection fatality ratios and outbreak dynamics are not intrinsic properties of a pathogen; these things emerge from the three-way interaction among pathogen, population, and place. Understanding more about each point in this triangle can help in predicting the local trajectory of an outbreak.

In April, an influential data-driven model, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, which uses a curve-fitting approach, came under criticism for its volatile projections and questionable assumption that the trajectory of COVID-19 deaths in American states can be extrapolated from curves in other countries.12,13 In a curve-fitting approach, the infection curve representing a local outbreak is extrapolated from data collected locally along with data regarding the trajectory of the outbreak elsewhere. The curve is drawn to fit the data. However, the true trajectory of the local outbreak, including the number of infections and deaths, depends upon characteristics of the local population as well as policies and behaviors adopted locally, not just upon the virus.

Predictions require assumptions that in turn require evidence.

Many of the other epidemic models in the coronavirus pandemic are SIR-type models, a more traditional modelling approach for infectious-disease epidemiology. SIR-type models represent the dynamics of an outbreak, the transition of individuals in the population from a state of being susceptible to infection (S) to one of being infectious to others (I) and, finally, recovered from infection (R). These models simulate the real world. In contrast to the data-driven approach, SIR models are more theory-driven. The theory that underwrites them includes the mathematical theory of outbreaks developed in the 1920s and 1930s, and the qualitative germ theory pioneered in the 1800s. Epidemiologic theories impart SIR-type models with the know-how to make good predictions in different contexts.

For instance, they represent the transmission of the virus as a factor of patterns of social contact as well as viral transmissibility, which depend on local behaviors and local infection control measures, respectively. The drawback of these more theoretical models is that without good data to support their assumptions they might misrepresent reality and make unreliable projections for the future.

One reason why the dynamics of an outbreak are often more complicated than a traditional model can predict, or an infectious-disease epidemiology theory can explain, is that the dynamics of an outbreak result from human behavior and not just human biology. Yet more sophisticated disease-behavior models can represent the behavioral dynamics of an outbreak by modeling the spread of opinions or the choices individuals make.14,15 Individual behaviors are influenced by the trajectory of the epidemic, which is in turn influenced by individual behaviors.

“There are important feedback loops that are readily represented by disease-behavior models,” Bert Baumgartner, a philosopher who has helped develop some of these models, explains. “As a very simple example, people may start to socially distance as disease spreads, then as disease consequently declines people may stop social distancing, which leads to the disease increasing again.” These looping effects of disease-behavior models are yet another challenge to predicting.

It is a highly complex and daunting challenge we face. That’s nothing unusual for doctors and public health experts, who are used to grappling with uncertainty. I remember what that uncertainty felt like when I was training in medicine. It can be discomforting, especially when confronted with a deadly disease. However, uncertainty need not be paralyzing. By spotting the gaps in our models and understanding, we can often narrow those gaps or at least navigate around them. Doing so requires clarifying and questioning our ideas and assumptions. In other words, we must think like a philosopher.

Jonathan Fuller is an assistant professor in the Department of History and Philosophy of Science at the University of Pittsburgh. He draws on his dual training in philosophy and in medicine to answer fundamental questions about the nature of contemporary disease, evidence, and reasoning in healthcare, and theory and methods in epidemiology and medical science.

References

1. Walker, P., et al. The global impact of COVID-19 and strategies for mitigation and suppression. Imperial College London (2020).

2. Flaxman, S., et al. Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020).

3. Lourenco, J., et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. medRxiv:10.1101/2020.03.24.20042291 (2020).

4. Broadbent, A., & Smart, B. Why a one-size-fits-all approach to COVID-19 could have lethal consequences. TheConversation.com (2020).

5. United Nations. Global recession increases malnutrition for the most vulnerable people in developing countries. United Nations Standing Committee on Nutrition (2009).

6. Cartwright, N. Will this policy work for you? Predicting effectiveness better: How philosophy helps. Philosophy of Science 79, 973-989 (2012).

7. Cartwright, N. & Hardie, J. Evidence-Based Policy: A Practical Guide to Doing it Better Oxford University Press, New York, New York (2012).

8. Fuller, J., & Flores, L. The Risk GP Model: The standard model of prediction in medicine. Studies in History and Philosophy of Biological and Biomedical Sciences 54, 49-61 (2015).

9. Rothman, K., & Greenland, S. Causation and causal inference in epidemiology. American Journal Public Health 95, S144-S50 (2005).

10. Dowd, J. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences 117, 9696-9698 (2020).

11. Ioannidis, J. Coronavirus disease 2019: The harms of exaggerated information and non‐evidence‐based measures. European Journal of Clinical Investigation 50, e13222 (2020).

12. COVID-19 Projections. Healthdata.org. https://covid19.healthdata.org/united-states-of-america.

13. Jewell, N., et al. Caution warranted: Using the Institute for Health metrics and evaluation model for predicting the course of the COVID-19 pandemic. Annals of Internal Medicine (2020).

14. Nardin, L., et al. Planning horizon affects prophylactic decision-making and epidemic dynamics. PeerJ 4:e2678 (2016).

15. Tyson, R., et al. The timing and nature of behavioural responses affect the course of an epidemic. Bulletin of Mathematical Biology 82, 14 (2020).

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