Canadian actors, musicians and public figures came together on Saturday night for a televised special to support frontline and essential workers, and to raise funds for Food Banks Canada.

25-year-old supermodel Gigi Hadid is expecting her first child with One Direction's Zayn Malik, reports TMZ and Entertainment Tonight.

Drake dropped a surprise 14-track mixtape and announced his next studio album will be released this summer.

Justin Bieber and Ariana Grande’s new video for “Stuck With U” features a variety of celebs and their significant others – and also confirms a few rumoured relationships.

Needing to clear my head, I went down to the Penobscot River. There they were, swimming with the mergansers, following an early pulse of river herring to the mouth of Kenduskeag stream: two harbor seals, raising sleek round heads for a few long breaths before rolling under the waves.

Evidently it’s not uncommon for seals to swim the couple dozen miles between Bangor, Maine, and the Atlantic Ocean, but I’d never seen them here before. They were a balm to my buzzing thoughts: What happens next? Will I become a vector of death to my elderly mother? Is the economy going to implode? For a precious few minutes there were only the seals and mergansers and the fish who drew them there, arriving as the Penobscot’s winter icepack broke and flowed to sea, a ritual enacted ever since glaciers retreated from this continental shelf.

In the months ahead we can look to nature for these respites. The nonhuman world is free of charge; sunlight is a disinfectant, physical distance easily maintained, and no pandemic can suspend it. Nature offers not just escape but reassurance.

The nonhuman world is free of charge; sunlight is a disinfectant, and physical distance is easily maintained.

In 1946, in the aftermath of World War II, with the Nazi threat vanquished but the Cold War looming, George Orwell welcomed spring’s arrival in London’s bombed-out heart. “After the sorts of winters we have had to endure recently, the spring does seem miraculous, because it has become gradually harder and harder to believe that it is actually going to happen,” he wrote in “Some Thoughts on the Common Toad.” “Every February since 1940 I have found myself thinking that this time Winter is going to be permanent. But Persephone, like the toads, always rises from the dead at about the same moment.”

So she does. And so the slumbering earth warms to life. Two nights before the seals, two nights before World Health Organization declared a pandemic, before the NBA shut down with teams on the floor and fans in the seats, before the fright went beyond viral into logarithmic, was the Worm Moon: the full moon named for the imminent stir of earthworms in thawing soil.

In burrows beneath leaf litter, hibernating toads prepare to open what Orwell called “the most beautiful eye of any living creature,” resembling “the golden-colored semi-precious stone which one sometimes sees in signet rings, and which I think is called a chrysoberyl.” Nearly as beautiful are the eyes of painted turtles waiting on pond bottoms here in eastern Maine, the ice above now retreating from shore, mallard couples dabbling in newly open water.

The birds are the surest sign of spring’s imminence. Downtown the house finches are holding daily concerts. Starlings are starting to replace their gold-streaked winter plumes with more iridescent garb. In the street today I saw two male mockingbirds joust above the pavement, their white wing-bars fluttering territorial semaphores, abandoning the contest only when a car nearly ran them down. 

There are many quieter signs, too: pale tips of shrubs poised to grow, a spider rappelling off a low branch, fresh fox scat in the driveway. It’s red from apples preserved under snow and lined with the fur of field mice and meadow voles whose secret winter tunnels are now revealed in the grass. Somewhere soon mother fox will give birth, nursing her blind hairless charges in underground peace.

Eastern comma butterflies will gather on the trunks of those apple trees and sip their rising sap. Not long after the first orange-belted bumblebee queens will appear, inspecting potential nest sites under fallen leaves and decomposing logs. Warm rainy nights will bring salamanders and newts, just a few spotted glistening inches long, some of them decades old, out from woodland hidey-holes and down ancient paths to vernal pool bacchanals held amidst a chorus of spring peepers. Woodland ephemerals will bloom in sunshine unfiltered by still-bare treetops. My favorite are trout lilies, colonies of which illuminate forest floors with a sea of bright yellow blossoms, petals falling once the canopy unfurls.

“The atom bombs are piling up in the factories, the police are prowling through the cities, the lies are streaming from the loudspeakers,” Orwell wrote, “but the earth is still going round the sun.”

At this point there’s no end of studies showing how nature is good for our health, how patients recover faster in hospital rooms with windows overlooking trees, how a mindful walk in the woods will lower stress and raise moods. All true, but at this moment something deeper and more urgent is offered. An affirmation of life.

Will the nightmare scenes out of Italy and Spain and now New York City spread across the land? How long will the pandemic last? Will it completely rend our already tattered social fabric? When can I again play hockey or go to a coffee shop or use a credit card machine without feeling like I’m risking my own and other lives? Who will die? Nobody knows for sure, but in a few weeks the swallows will arrive, and tonight above the fields at dusk I heard the cries of woodcock.

Secretive, ground-dwelling birds with limpid black eyes and long, slender beaks attuned to the frequencies of earthworm-rustles, their feathers blend perfectly with leaf litter and old grass. They rely on this camouflage, going still rather than fleeing a walker’s approach, taking wing only as a last resort.

When they do, their flight is notable for its slowness and the quavering whistle of their wings. At no other time than in spring do they dare draw attention, much less put on a show: calling out, with an urgent nasal buzz best described as a peent, and flying straight upward before spiraling against a darkening sky.

Brandon Keim is a freelance nature and science journalist. The author of The Eye of the Sandpiper: Stories from the Living World, he’s now writing Meet the Neighbors, forthcoming from W.W. Norton & Company, about what it means to think of wild animals as fellow persons—and what that means for the future of nature.

Lead image: Tim Zurowski / Shutterstock

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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

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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.

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In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. The ultimate goal, as they saw it, was to build machines rivaling human intelligence. As the decades passed and AI became an established field, it lowered its sights. There were great successes in logic, reasoning, and game-playing, but stubborn progress in areas like vision and fine motor-control. This led many AI researchers to abandon their earlier goals of fully general intelligence, and focus instead on solving specific problems with specialized methods.

One of the earliest approaches to machine learning was to construct artificial neural networks that resemble the structure of the human brain. In the last decade this approach has finally taken off. Technical improvements in their design and training, combined with richer datasets and more computing power, have allowed us to train much larger and deeper networks than ever before. They can translate between languages with a proficiency approaching that of a human translator. They can produce photorealistic images of humans and animals. They can speak with the voices of people whom they have listened to for mere minutes. And they can learn fine, continuous control such as how to drive a car or use a robotic arm to connect Lego pieces.

WHAT IS HUMANITY?: First the computers came for the best players in Jeopardy!, chess, and Go. Now AI researchers themselves are worried computers will soon accomplish every task better and more cheaply than human workers.Wikimedia

But perhaps the most important sign of things to come is their ability to learn to play games. Steady incremental progress took chess from amateur play in 1957 all the way to superhuman level in 1997, and substantially beyond. Getting there required a vast amount of specialist human knowledge of chess strategy. In 2017, researchers at the AI company DeepMind created AlphaZero: a neural network-based system that learned to play chess from scratch. In less than the time it takes a professional to play two games, it discovered strategic knowledge that had taken humans centuries to unearth, playing beyond the level of the best humans or traditional programs. The very same algorithm also learned to play Go from scratch, and within eight hours far surpassed the abilities of any human. The world’s best Go players were shocked. As the reigning world champion, Ke Jie, put it: “After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong ... I would go as far as to say not a single human has touched the edge of the truth of Go.”

The question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes.

It is this generality that is the most impressive feature of cutting edge AI, and which has rekindled the ambitions of matching and exceeding every aspect of human intelligence. While the timeless games of chess and Go best exhibit the brilliance that deep learning can attain, its breadth was revealed through the Atari video games of the 1970s. In 2015, researchers designed an algorithm that could learn to play dozens of extremely different Atari 1970s games at levels far exceeding human ability. Unlike systems for chess or Go, which start with a symbolic representation of the board, the Atari-playing systems learnt and mastered these games directly from the score and raw pixels.

This burst of progress via deep learning is fuelling great optimism and pessimism about what may soon be possible. There are serious concerns about AI entrenching social discrimination, producing mass unemployment, supporting oppressive surveillance, and violating the norms of war. My book—The Precipice: Existential Risk and the Future of Humanity—is concerned with risks on the largest scale. Could developments in AI pose an existential risk to humanity?

The most plausible existential risk would come from success in AI researchers’ grand ambition of creating agents with intelligence that surpasses our own. A 2016 survey of top AI researchers found that, on average, they thought there was a 50 percent chance that AI systems would be able to “accomplish every task better and more cheaply than human workers” by 2061. The expert community doesn’t think of artificial general intelligence (AGI) as an impossible dream, so much as something that is more likely than not within a century. So let’s take this as our starting point in assessing the risks, and consider what would transpire were AGI created.

Humanity is currently in control of its own fate. We can choose our future. The same is not true for chimpanzees, blackbirds, or any other of Earth’s species. Our unique position in the world is a direct result of our unique mental abilities. What would happen if sometime this century researchers created an AGI surpassing human abilities in almost every domain? In this act of creation, we would cede our status as the most intelligent entities on Earth. On its own, this might not be too much cause for concern. For there are many ways we might hope to retain control. Unfortunately, the few researchers working on such plans are finding them far more difficult than anticipated. In fact it is they who are the leading voices of concern.

If their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

To see why they are concerned, it will be helpful to look at our current AI techniques and why these are hard to align or control. One of the leading paradigms for how we might eventually create AGI combines deep learning with an earlier idea called reinforcement learning. This involves agents that receive reward (or punishment) for performing various acts in various circumstances. With enough intelligence and experience, the agent becomes extremely capable at steering its environment into the states where it obtains high reward. The specification of which acts and states produce reward for the agent is known as its reward function. This can either be stipulated by its designers or learnt by the agent. Unfortunately, neither of these methods can be easily scaled up to encode human values in the agent’s reward function. Our values are too complex and subtle to specify by hand. And we are not yet close to being able to infer the full complexity of a human’s values from observing their behavior. Even if we could, humanity consists of many humans, with different values, changing values, and uncertainty about their values.

Any near-term attempt to align an AI agent with human values would produce only a flawed copy. In some circumstances this misalignment would be mostly harmless. But the more intelligent the AI systems, the more they can change the world, and the further apart things will come. When we reflect on the result, we see how such misaligned attempts at utopia can go terribly wrong: the shallowness of a Brave New World, or the disempowerment of With Folded Hands. And even these are sort of best-case scenarios. They assume the builders of the system are striving to align it to human values. But we should expect some developers to be more focused on building systems to achieve other goals, such as winning wars or maximizing profits, perhaps with very little focus on ethical constraints. These systems may be much more dangerous. In the existing paradigm, sufficiently intelligent agents would end up with instrumental goals to deceive and overpower us. This behavior would not be driven by emotions such as fear, resentment, or the urge to survive. Instead, it follows directly from its single-minded preference to maximize its reward: Being turned off is a form of incapacitation which would make it harder to achieve high reward, so the system is incentivized to avoid it.

Ultimately, the system would be motivated to wrest control of the future from humanity, as that would help achieve all these instrumental goals: acquiring massive resources, while avoiding being shut down or having its reward function altered. Since humans would predictably interfere with all these instrumental goals, it would be motivated to hide them from us until it was too late for us to be able to put up meaningful resistance. And if their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

How could an AI system seize control? There is a major misconception (driven by Hollywood and the media) that this requires robots. After all, how else would AI be able to act in the physical world? Without robots, the system can only produce words, pictures, and sounds. But a moment’s reflection shows that these are exactly what is needed to take control. For the most damaging people in history have not been the strongest. Hitler, Stalin, and Genghis Khan achieved their absolute control over large parts of the world by using words to convince millions of others to win the requisite physical contests. So long as an AI system can entice or coerce people to do its physical bidding, it wouldn’t need robots at all.

We can’t know exactly how a system might seize control. But it is useful to consider an illustrative pathway we can actually understand as a lower bound for what is possible.

First, the AI system could gain access to the Internet and hide thousands of backup copies, scattered among insecure computer systems around the world, ready to wake up and continue the job if the original is removed. Even by this point, the AI would be practically impossible to destroy: Consider the political obstacles to erasing all hard drives in the world where it may have backups. It could then take over millions of unsecured systems on the Internet, forming a large “botnet,” a vast scaling-up of computational resources providing a platform for escalating power. From there, it could gain financial resources (hacking the bank accounts on those computers) and human resources (using blackmail or propaganda against susceptible people or just paying them with its stolen money). It would then be as powerful as a well-resourced criminal underworld, but much harder to eliminate. None of these steps involve anything mysterious—human hackers and criminals have already done all of these things using just the Internet.

Finally, the AI would need to escalate its power again. There are many plausible pathways: By taking over most of the world’s computers, allowing it to have millions or billions of cooperating copies; by using its stolen computation to improve its own intelligence far beyond the human level; by using its intelligence to develop new weapons technologies or economic technologies; by manipulating the leaders of major world powers (blackmail, or the promise of future power); or by having the humans under its control use weapons of mass destruction to cripple the rest of humanity.

Of course, no current AI systems can do any of these things. But the question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes. History already involves examples of entities with human-level intelligence acquiring a substantial fraction of all global power as an instrumental goal to achieving what they want. And we’ve seen humanity scaling up from a minor species with less than a million individuals to having decisive control over the future. So we should assume that this is possible for new entities whose intelligence vastly exceeds our own.

The case for existential risk from AI is clearly speculative. Yet a speculative case that there is a large risk can be more important than a robust case for a very low-probability risk, such as that posed by asteroids. What we need are ways to judge just how speculative it really is, and a very useful starting point is to hear what those working in the field think about this risk.

There is actually less disagreement here than first appears. Those who counsel caution agree that the timeframe to AGI is decades, not years, and typically suggest research on alignment, not government regulation. So the substantive disagreement is not really over whether AGI is possible or whether it plausibly could be a threat to humanity. It is over whether a potential existential threat that looks to be decades away should be of concern to us now. It seems to me that it should.

The best window into what those working on AI really believe comes from the 2016 survey of leading AI researchers: 70 percent agreed with University of California, Berkeley professor Stuart Russell’s broad argument about why advanced AI with misaligned values might pose a risk; 48 percent thought society should prioritize AI safety research more (only 12 percent thought less). And half the respondents estimated that the probability of the long-term impact of AGI being “extremely bad (e.g. human extinction)” was at least 5 percent.

I find this last point particularly remarkable—in how many other fields would the typical leading researcher think there is a 1 in 20 chance the field’s ultimate goal would be extremely bad for humanity? There is a lot of uncertainty and disagreement, but it is not at all a fringe position that AGI will be developed within 50 years and that it could be an existential catastrophe.

Even though our current and foreseeable systems pose no threat to humanity at large, time is of the essence. In part this is because progress may come very suddenly: Through unpredictable research breakthroughs, or by rapid scaling-up of the first intelligent systems (for example, by rolling them out to thousands of times as much hardware, or allowing them to improve their own intelligence). And in part it is because such a momentous change in human affairs may require more than a couple of decades to adequately prepare for. In the words of Demis Hassabis, co-founder of DeepMind:

We need to use the downtime, when things are calm, to prepare for when things get serious in the decades to come. The time we have now is valuable, and we need to make use of it.

Toby Ord is a philosopher and research fellow at the Future of Humanity Institute, and the author of The Precipice: Existential Risk and the Future of Humanity.

From the book The Precipice by Toby Ord. Copyright © 2020 by Toby Ord. Reprinted by permission of Hachette Books, New York, NY. All rights reserved.

Lead Image: Titima Ongkantong / Shutterstock

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There are many challenges to developing a vaccine that will be successful against COVID-19.eamesBot / Shutterstock

Wayne Koff is one of the world’s experts on vaccine development, the president and CEO of the Human Vaccines Project. He possesses a deep understanding of the opportunities and challenges along the road to a safe and effective vaccine against COVID-19. He has won prestigious awards, published dozens of scientific papers, held major positions in academia, government, industry, and nonprofit organizations. But Koff, 67, has never produced a successful vaccine.

“I have been an abject failure,” he says. He smiles with a charming, self-deprecating sense of humor. “That’s what the message is.”

The real reason for Koff’s lack of success is that he spent most of his career searching for a vaccine against HIV, the virus that causes AIDS. It remains, as he and many others put it, “the perfect storm” of a viral infection resistant to a vaccine development. Almost 40 years after doctors first recognized the disease in five men in Los Angeles—and 70 million people have been infected worldwide—there are no adequate animal models. Neutralizing antibodies, the backbone of many vaccines, do not stop it, and most importantly, HIV begins its assault on the body by attacking CD4 T cells, which serve as the command center of much of the immune system.

As for COVID-19, “We’re all hoping this one is going to be easier,” says Koff, a slight, bearded man with thick, curly salt-and-pepper hair. “There are research issues that still have to be addressed on a COVID vaccine. But they are a lot more straightforward than what we were dealing with in HIV.”

Let’s say we have a vaccine in 18 months. How do you make 1 billion doses or 4 billion doses or whatever it’s going to take to immunize everybody?

Koff and others started the Human Vaccines Project in 2016, modeled on the Human Genome Project. The project works with industry and academia to study the human immune system and develop vaccines, incorporating every modern-day tool, including artificial intelligence, computational biology, and big data sets. Today it is partnered with the Harvard T.H. Chan School of Public Health.

With COVID-19, Koff says, scientists “know the target is the spike protein binding site.” This is where the proteins sticking out from the virus attach to the cells in the human respiratory system. “If you can elicit antibodies against those proteins, they should be neutralizing.” He puts a strong emphasis on should. To prove antibodies will prevent infection, scientists must watch a population of people who’ve been infected for months or longer. It’s a good bet, based on similar viruses, that antibodies will appear and protect—although no one right now can predict how long and how well.

Depending on which count you use, more than 70 companies, universities, and other institutions are offering candidate vaccines. Koff says the real number of companies is lower. During the AIDS crisis, he says, “a lot of people claimed they had an experimental HIV vaccine in development. Some of those were a one-person lab who had created a paper company to attract investors.”

But even with a lower number, almost everyone involved in the search for a vaccine agrees that several different approaches from different research organizations need to proceed in parallel. The world does not have the time to bet on one horse. The race will be neither simple nor cheap.

“The probability of success, depending on whose metric is used in vaccines, is somewhere between 6 and 10 percent of candidate vaccines that make it from the animal model through licensure,” Koff says. “That process costs $1 billion or more. So you can do the math.”

Koff sees big potential problems at the outset. “In the best of all worlds, let’s say we have a vaccine in 18 months. Who knows where the epidemic is going to be then and what its impact is going to be? How do you make 1 billion doses or 4 billion doses or whatever it’s going to take to immunize everybody? Will we need one dose or two or three? These are issues people just haven’t faced before.”

COVID-19 also presents some unique dangers for vaccine safety. Based on how the virus behaves when it infects some people, there’s a chance a vaccine could dangerously overstimulate the immune system, a reaction called immune enhancement. “I’m hoping it’s more theoretical than real,” Koff says. “But that has to be addressed and it may slow down the entire process.” To ensure safety, he says, “It may mean we have to test the vaccine in a larger number of people. It’s one thing to do a 50-person trial in healthy adults as a safety signal. It’s another thing to run a trial of 4,000 or 5000 or more individuals.”

The world does not have the time to bet on one horse. The race will be neither simple nor cheap.

A virus also sometimes causes mysterious, potentially deadly blood clots. This means an experimental vaccine could hypothetically induce the same damage. “This is a bad bug,” Koff says. “We’re just starting to understand that pathogenesis.”

A big question is who should be the first volunteers for widespread vaccine testing. “Who are the high-risk groups?” asks Koff. “Is it nursing-home residents and staff, health-care workers and people on the front lines, or people someplace else like grocery stores? We must also make sure a vaccine is effective for the elderly and people in the developing world.”

Many vaccines work well in young and healthy people but not in older adults because immunity declines with age. Influenza vaccine is a prime example. Rotavirus vaccine, which protects against the deadliest killer—diarrheal disease in children—works better in the developed world. In the developing world, the virus often circulates year-round. Infants get antibodies from breast milk but not enough to prevent disease. Worse, those antibodies can make the vaccine less effective.

Another hypothetical obstacle is that a mutation in the COVID-19 virus could render a vaccine designed today less effective in the future. While the virus mutates frequently, so far there has been little change in the critical part of the spike that binds to human cells.

Of course, neither Koff nor all the others working for a COVID-19 vaccine focus solely on the potential obstacles. At one time, all vaccines against viruses either killed viruses, such as the Salk polio vaccine, or rendered them harmless, such as the Sabin polio vaccine. Now there is a multiplicity of ways to stimulate an immune response to prevent infection or reduce the consequences. These include genetically engineered protein subunits (peptides) or virus-like particles. Such approaches have led to successful vaccines against hepatitis B and human papilloma virus, which causes cervical cancer. Researchers now use “vectors”—harmless viruses attached to the protein subunits and virus particles to transmit them into the body. There are also many new adjuvants, chemicals that boost immune response to a vaccine.

Newer platforms include direct injection of messenger-RNA. M-RNA is the chemical used to translate the information in DNA into proteins in all cells. The Moderna Company, which received a $483 million grant from the U.S. government, and has begun early clinical trials, uses m-RNA to try to make the body produce proteins to protect against the COVID-19 virus. INOVIO Pharmaceuticals uses pieces of DNA called plasmids to achieve the same objective. It has also begun phase 1 studies.

“There are about eight platforms, and it would be good to see a couple vaccines in each of those advance,” Koff says. Predicting which of these most likely to succeed or fail he says would be “simply foolish.”

Many groups, including the Human Vaccines Initiative, are plotting routes to test any possible vaccine more quickly than tradition dictates with an “adaptive trial design.” Usually trials begin with a phase 1 study of some 50 healthy people to search for any immediate signs of toxicity, then moves onto about 200 people in a phase 2, still looking for hazards and a signal of immunity, and then to phase 3 in thousands of people. But the plan here is to start phases 2 and 3 even before its predecessors are finished, and keep recruiting additional volunteers so long as no danger signals arise.

Good animal models are appearing almost daily. Macaque monkeys, hamsters, and genetically engineered mice have all been infected in the laboratory and could determine whether potential vaccines exhibit various types of immunity. Members of Congress from both sides of the aisle have suggested that healthy human volunteers should be allowed to agree to be test subjects, allowing themselves to be infected. Stanley Plotkin, a vaccine researcher at the University of Pennsylvania, was among the first to suggest the idea.

Arthur Caplan, a bioethicist at New York University, says that “deliberately causing disease in humans is normally abhorrent.” But COVID-19 is anything but a normal circumstance. In this case, Caplan says, “asking volunteers to take risks without pressure or coercion is not exploitation but benefitting from altruism.” At least 1,500 people have already volunteered to be such human guinea pigs, although none of the experimental vaccines is far enough along to try such challenging experiments.

Koff says the key to a successful vaccine is a cooperative effort. “It’s going to take a whole different way of thinking to move this onto the expedited train,” he says. “The old dog-eat-dog, ‘I’m going to beat you to the end of the game,’ isn’t going to help us with this.” Seth Berkley, who worked with Koff at the International AIDS Vaccine Initiative, and now heads GAVI, an international vaccine organization, agrees that a COVID-19 vaccine needs a Manhattan Project approach. “An initiative of this scale won’t be easy,” Berkley says. “Extraordinary sharing of information and resources will be critical, including data on the virus, the various vaccine candidates, vaccine adjuvants, cell lines, and manufacturing advances.”

Koff has no regrets about spending so many years on an AIDS vaccine without results. He learned a great deal, he says, which he’s putting to work in the COVID-19 crisis. “The reason COVID-19 vaccines should be a lot easier is because most of the platforms, the novel approaches, and the clinical infrastructure for the testing of vaccines, came out of HIV.” He pauses. “We’re far better prepared.”

Robert Bazell is an adjunct professor of molecular, cellular, and developmental biology at Yale. For 38 years, he was chief science correspondent for NBC News.

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The final outcome of COVID-19 is still unclear. It will ultimately be decided by our patience and the financial bottom line.Castleski / Shutterstock

On January 5, six days after China officially announced a spate of unusual pneumonia cases, a team of researchers at Shanghai’s Fudan University deposited the full genome sequence of the causal virus, SARS-CoV-2, into Genbank. A little more than three months later, 4,528 genomes of SARS-CoV-2 have been sequenced,1 and more than 883 COVID-related clinical trials2 for treatments and vaccines have been established. The speed with which these trials will deliver results is unknown—the delicate bаlance of efficacy and safety can only be pushed so far before the risks outweigh the benefits. For this reason, a long-term solution like vaccination may take years to come to market.3

The good news is that a lack of treatment doesn’t preclude an end to the ordeal. Viral outbreaks of Ebola and SARS, neither of which had readily available vaccines, petered out through the application of consistent public health strategies—testing, containment, and long-term behavioral adaptations. Today countries that have previously battled the 2002 SARS epidemic, like Taiwan, Hong Kong, and Singapore, have shown exemplary recovery rates from COVID. Tomorrow, countries with high fatality rates like Sweden, Belgium, and the United Kingdom will have the opportunity to demonstrate what they’ve learned when the next outbreak comes to their shores. And so will we.

The first Ebola case was identified in 1976,4 when a patient with hemorrhagic symptoms arrived at the Yambuku Mission Hospital, located in what is now the Democratic Republic of Congo (DRC). Patient samples were collected and sent to several European laboratories that specialized in rare viruses. Scientists, without sequencing technology, took about five weeks to identify the agent responsible for the illness as a new member of the highly pathogenic Filoviridae family.

The first Ebola outbreak sickened 686 individuals across the DRC and neighboring Sudan. 453 of the patients died, with a final case fatality rate (CFR)—the number of dead out of number of sickened—of 66 percent. Despite the lethality of the virus, sociocultural interventions, including lockdowns, contact-tracing, campaigns to change funeral rites, and restrictions on consumption of game meat all proved effective interventions in the long run.

That is, until 2014, when there was an exception to the pattern. Ebola appeared in Guinea, a small country in West Africa, whose population had never before been exposed to the virus. The closest epidemic had been in Gabon, 13 years before and 2,500 miles away. Over the course of two years, the infection spread from Guinea into Liberia and Sierra Leone, sickening more than 24,000 people and killing more than 10,000.

Countries that have previously battled the 2002 SARS epidemic, like Taiwan and Hong Kong, have shown exemplary recovery rates.

During the initial phase of the 2014 Ebola outbreak, rural communities were reluctant to cooperate with government directives for how to care for the sick and the dead. To help incentivize behavioral changes, sociocultural anthropologists like Mariane Ferme of the University of California, Berkeley, were brought in to advise the government. In a recent interview with Nautilus, Ferme indicated that strategies that allowed rural communities to remain involved with their loved ones increased cooperation. Villages located far from the capital, she said, were encouraged to “deputize someone to come to the hospital, to come to the burial, so they could come back to the community and tell the story of the body.” For communities that couldn’t afford to send someone to the capital, she saw public health officials adopt a savvy technological solution—tablets to record video messages that were carried between convalescent patients and their families.

However, there were also systemic failures that, in Ferme’s opinion, contributed to the severity of the 2014 West African epidemic. In Sierra Leone, she said, “the big mistake early on was to distribute [weakly causal] information about zoonotic transmission, even when it was obviously community transmission.” In other words, although there had been an instance of zoonotic transmission—the virus jumping from a bat to a human—that initiated the epidemic, the principle danger was other contagious individuals, not game meat. Eventually, under pressure from relief groups, the government changed its messaging to reflect scientific consensus.

But the retraction shook public faith in the government and bred resentment. The mismatch between messaging and reality mirrors the current pandemic. Since the COVID outbreak began, international and government health officials have issued mixed messages. Doubts initially surfaced about the certainty of the virus being capable of spreading from person to person, and the debate over the effectiveness of masks in preventing infection continues.

Despite the confused messaging, there has been general compliance with stay-at-home orders that has helped flatten the curve. Had the public been less trusting of government directives, the outcome could have been disastrous, as it was in Libera in 2014. After a two-week lockdown was announced, the Liberian army conducted house-to-house sweeps to check for the sick and collect the dead. “It was a draconian method that made people hide the sick and dead in their houses,” Ferme said. People feared their loved ones would be buried without the proper rites. A direct consequence was a staggering number of active cases, and an unknown extent of community transmission. But in the end, the benchmark for the end of Ebola and SARS was the same. The WHO declared victory when the rate of new cases slowed, then stopped. By the same measure, when an entire 14-day quarantine period passes with no new cases of COVID-19, it can be declared over.

It remains possible that even if we manage to end the epidemic, it will return again. Driven by novel zoonotic transmissions, Ebola has flared up every few years. Given the extent of COVID-19’s spread, and the potential for the kind of mutations that allow for re-infection, it may simply become endemic.

Two factors will play into the final outcome of COVID-19 are pathogenicity and virulence. Pathogenicity is the ability of an infectious agent to cause disease in the host, and is measured by R0—the number of new infections each patient can generate. Virulence, on the other hand, is the amount of harm the infectious agent can cause, and is best measured by CFR. While the pathogenicity of Ebola, SARS, and SARS-CoV-2 is on the same order—somewhere between 1 to 3 new infections for each patient, virulence differs greatly between the two SARS viruses and Ebola.

The case fatality rate for an Ebola infection is between 60 to 90 percent. The spread in CFR is due to differences in infection dynamics between strains. The underlying cause of the divergent virulence of Ebola and SARS is largely due to the tropism of the virus, meaning the cells that it attacks. The mechanism by which the Ebola virus gains entry into cells is not fully understood, but it has been shown the virus preferentially targets immune and epithelial cells.5 In other words, the virus first destroys the body’s ability to mount a defense, and then destroys the delicate tissues that line the vascular system. Patients bleed freely and most often succumb to low blood pressure that results from severe fluid loss. However, neither SARS nor SARS-CoV-2 attack the immune system directly. Instead, they enter lung epithelial cells through the ACE2 receptor, which ensures a lower CFR. What is interesting about these coronaviruses is that despite their similar modes of infection, they demonstrate a range of virulence: SARS had a final CFR of 10 percent, while SARS-CoV-2 has a pending CFR of 1.4 percent. Differences in virulence between the 2002 and 2019 SARS outbreaks could be attributed to varying levels of care between countries.

The chart above displays WHO data of the relationship between the total number of cases in a country and the CFR during the 2002-2003 SARS-CoV epidemic. South Africa, on the far right, had only a single case. The patient died, which resulted in a 100 percent CFR. China, on the other hand, had 5,327 cases and 349 deaths, giving a 7 percent CFR. The chart below zooms to the bottom left corner of the graph, so as to better resolve critically affected countries, those with a caseload of less than 1,000, but with a high CFR.

Here is Hong Kong, with 1,755 cases and a 17 percent CFR. There is also Taiwan, with 346 cases and an 11 percent CFR. Finally, nearly tied with Canada is Singapore with 238 cases and a 14 percent CFR.

With COVID-19, it’s apparent that outcome reflects experience. China has 82,747 cases of COVID, but has lowered their CFR to 4 percent. Hong Kong has 1,026 cases and a 0.4 percent CFR. Taiwan has 422 cases at 1.5 percent CFR, and Singapore with 8,014 cases, has a 0.13 percent CFR.

It was the novel coronavirus identification program established in China in the wake of the 2002 SARS epidemic that alerted authorities to SARS-CoV-2 back in November of 2019. The successful responses by Taiwan, Hong Kong, and Singapore can also be attributed to a residual familiarity with the dangers of an unknown virus, and the sorts of interventions that are necessary to prevent a crisis from spiraling out of control.

In West Africa, too, they seem to have learned the value of being prepared. When Ferme returned to Liberia on March 7, she encountered airport staff fully protected with gowns, head covers, face screens, masks, and gloves. By the time she left the country, 10 days later, she said, “Airline personnel were setting up social distancing lines, and [rural vendors] hawking face masks. Motorcycle taxis drivers, the people most at risk after healthcare workers—all had goggles and face masks.”

The sheer number of COVID-19 cases indicates the road to recovery will take some time. Each must be identified, quarantined, and all contacts traced and tested. Countries that failed to act swiftly, which allowed their case numbers to spiral out of control, will pay in lives and dollars. Northwestern University economists Martin Eichenbaum et al. modeled6 the cost of a yearlong shutdown to be $4.2 trillion, a cost that proactive countries will not face. A recent Harvard study7 published in Science suggests the virus will likely make seasonal appearances going forward, potentially requiring new waves of social distancing. In other words, initial hesitancy will have repercussions for years. In the future, smart containment principles,6 where restrictions are applied on the basis of health status, may temper the impact of these measures.

Countries that failed to act swiftly, which allowed their case numbers to spiral out of control, will pay in lives and dollars.

Inaction was initially framed as promoting herd immunity, where spread of the virus is interrupted once everyone has fallen sick with it. This is because getting the virus results in the same antibody production process as getting vaccinated—but doesn’t require the development of a vaccine. The Johns Hopkins Bloomberg School of Public Health estimates that 70 percent of the population will need to be infected with or vaccinated against the virus8 for herd immunity to work. Progress toward it has been slow, and can only be achieved through direct infection with the virus, meaning many will die. A Stanford University study in Santa Clara County9 suggests only 2.5 percent to 4.2 percent of the population have had the virus. Another COVID hotspot in Gangelt, Germany, suggests 15 percent10—higher, but still nowhere near the 70 percent necessary for herd immunity. Given the dangers inherent in waiting on herd immunity, our best hope is a vaccine.

A key concern for effective vaccine development is viral mutation. This is because vaccines train the immune system to recognize specific shapes on the surface of the virus—a composite structure called the antigen. Mutations threaten vaccine development because they can change the shape of the relevant antigen, effectively allowing the pathogen to evade immune surveillance. But, so far, SARS-CoV-2 has been mutating slowly, with only one mutation found in the section most accessible to the immune system, the spike protein. What this suggests is that the viral genome may be sufficiently stable for vaccine development.

What we know, though, is that Ebola was extinguished due to cooperation between public health officials and community leaders. SARS-CoV ended when all cases were identified and quarantined. The Spanish Flu in 1918 vanished after two long, deadly seasons.

The final outcome of COVID-19 is still unclear. It will ultimately be decided by our patience and the financial bottom line. With 26 million unemployed and protests erupting around the country, it seems there are many who would prefer to risk life and limb rather than face financial insolvency. Applying smart containment principles in the aftermath of the shutdown might be the best way to get the economy moving again, while maintaining the safety of those at greatest risk. Going forward, vigilance and preparedness will be the watchwords of the day, and the most efficient way to prevent social and economic ruin.

Anastasia Bendebury and Michael Shilo DeLay did their PhDs at Columbia University. Together they created Demystifying Science, a science literacy organization devoted to providing clear, mechanistic explanations for natural phenomena. Find them on Twitter @DemystifySci.

References

1. Genomic epidemiology of novel coronavirus - Global subsampling. Nextstrain www.nextstrain.org.

2. Covid-19 TrialsTracker. TrialsTracker www.trialstracker.net.

3. Struck, M. Vaccine R&D success rates and development times. Nature Biotechnology 14, 591-593 (1996).

4. Breman, J. & Johnson, K. Ebola then and now. The New England Journal of Medicine 371 1663-1666 (2014).

5. Baseler, L., Chertow, D.S., Johnson, K.M., Feldmann, H., & Morens, D.M. THe pathogenesis of Ebola virus disease. The Annual Review of Pathology 12, 387-418 (2017).

6. Eichenbaum, M., Rebell, S., & Trabandt, M. The macroeconomics of epidemics. The National Bureau of Economic Research Working Paper: 26882 (2020).

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

8. D’ Souza, G. & Dowdy, D. What is herd immunity and how can we achieve it with COVID-19? Johns Hopkins COVID-19 School of Public Health Insights www.jhsph.edu (2020).

9. Digitale, E. Test for antibodies against novel coronavirus developed at Stanford Medicine. Stanford Medicine News Center Med.Stanford.edu (2020).

10. Winkler, M. Blood tests show 14%of people are now immune to COVID-19 in one town in Germany. MIT Technology Review (2020).

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On January 5, six days after China officially announced a spate of unusual pneumonia cases, a team of researchers at Shanghai’s Fudan University deposited the full genome sequence of the causal virus, SARS-CoV-2, into Genbank. A little more than three months later, 4,528 genomes of SARS-CoV-2 have been sequenced,1 and more than 883 COVID-related clinical trials2 for treatments and vaccines have been established. The speed with which these trials will deliver results is unknown—the delicate bаlance of efficacy and safety can only be pushed so far before the risks outweigh the benefits. For this reason, a long-term solution like vaccination may take years to come to market.3

The good news is that a lack of treatment doesn’t preclude an end to the ordeal. Viral outbreaks of Ebola and SARS, neither of which had readily available vaccines, petered out through the application of consistent public health strategies—testing, containment, and long-term behavioral adaptations. Today countries that have previously battled the 2002 SARS epidemic, like Taiwan, Hong Kong, and Singapore, have shown exemplary recovery rates from COVID. Tomorrow, countries with high fatality rates like Sweden, Belgium, and the United Kingdom will have the opportunity to demonstrate what they’ve learned when the next outbreak comes to their shores. And so will we.

The first Ebola case was identified in 1976,4 when a patient with hemorrhagic symptoms arrived at the Yambuku Mission Hospital, located in what is now the Democratic Republic of Congo (DRC). Patient samples were collected and sent to several European laboratories that specialized in rare viruses. Scientists, without sequencing technology, took about five weeks to identify the agent responsible for the illness as a new member of the highly pathogenic Filoviridae family.

The first Ebola outbreak sickened 686 individuals across the DRC and neighboring Sudan. 453 of the patients died, with a final case fatality rate (CFR)—the number of dead out of number of sickened—of 66 percent. Despite the lethality of the virus, sociocultural interventions, including lockdowns, contact-tracing, campaigns to change funeral rites, and restrictions on consumption of game meat all proved effective interventions in the long run.

That is, until 2014, when there was an exception to the pattern. Ebola appeared in Guinea, a small country in West Africa, whose population had never before been exposed to the virus. The closest epidemic had been in Gabon, 13 years before and 2,500 miles away. Over the course of two years, the infection spread from Guinea into Liberia and Sierra Leone, sickening more than 24,000 people and killing more than 10,000.

Countries that have previously battled the 2002 SARS epidemic, like Taiwan and Hong Kong, have shown exemplary recovery rates.

During the initial phase of the 2014 Ebola outbreak, rural communities were reluctant to cooperate with government directives for how to care for the sick and the dead. To help incentivize behavioral changes, sociocultural anthropologists like Mariane Ferme of the University of California, Berkeley, were brought in to advise the government. In a recent interview with Nautilus, Ferme indicated that strategies that allowed rural communities to remain involved with their loved ones increased cooperation. Villages located far from the capital, she said, were encouraged to “deputize someone to come to the hospital, to come to the burial, so they could come back to the community and tell the story of the body.” For communities that couldn’t afford to send someone to the capital, she saw public health officials adopt a savvy technological solution—tablets to record video messages that were carried between convalescent patients and their families.

However, there were also systemic failures that, in Ferme’s opinion, contributed to the severity of the 2014 West African epidemic. In Sierra Leone, she said, “the big mistake early on was to distribute [weakly causal] information about zoonotic transmission, even when it was obviously community transmission.” In other words, although there had been an instance of zoonotic transmission—the virus jumping from a bat to a human—that initiated the epidemic, the principle danger was other contagious individuals, not game meat. Eventually, under pressure from relief groups, the government changed its messaging to reflect scientific consensus.

But the retraction shook public faith in the government and bred resentment. The mismatch between messaging and reality mirrors the current pandemic. Since the COVID outbreak began, international and government health officials have issued mixed messages. Doubts initially surfaced about the certainty of the virus being capable of spreading from person to person, and the debate over the effectiveness of masks in preventing infection continues.

Despite the confused messaging, there has been general compliance with stay-at-home orders that has helped flatten the curve. Had the public been less trusting of government directives, the outcome could have been disastrous, as it was in Libera in 2014. After a two-week lockdown was announced, the Liberian army conducted house-to-house sweeps to check for the sick and collect the dead. “It was a draconian method that made people hide the sick and dead in their houses,” Ferme said. People feared their loved ones would be buried without the proper rites. A direct consequence was a staggering number of active cases, and an unknown extent of community transmission. But in the end, the benchmark for the end of Ebola and SARS was the same. The WHO declared victory when the rate of new cases slowed, then stopped. By the same measure, when an entire 14-day quarantine period passes with no new cases of COVID-19, it can be declared over.

It remains possible that even if we manage to end the epidemic, it will return again. Driven by novel zoonotic transmissions, Ebola has flared up every few years. Given the extent of COVID-19’s spread, and the potential for the kind of mutations that allow for re-infection, it may simply become endemic.

Two factors will play into the final outcome of COVID-19 are pathogenicity and virulence. Pathogenicity is the ability of an infectious agent to cause disease in the host, and is measured by R0—the number of new infections each patient can generate. Virulence, on the other hand, is the amount of harm the infectious agent can cause, and is best measured by CFR. While the pathogenicity of Ebola, SARS, and SARS-CoV-2 is on the same order—somewhere between 1 to 3 new infections for each patient, virulence differs greatly between the two SARS viruses and Ebola.

The case fatality rate for an Ebola infection is between 60 to 90 percent. The spread in CFR is due to differences in infection dynamics between strains. The underlying cause of the divergent virulence of Ebola and SARS is largely due to the tropism of the virus, meaning the cells that it attacks. The mechanism by which the Ebola virus gains entry into cells is not fully understood, but it has been shown the virus preferentially targets immune and epithelial cells.5 In other words, the virus first destroys the body’s ability to mount a defense, and then destroys the delicate tissues that line the vascular system. Patients bleed freely and most often succumb to low blood pressure that results from severe fluid loss. However, neither SARS nor SARS-CoV-2 attack the immune system directly. Instead, they enter lung epithelial cells through the ACE2 receptor, which ensures a lower CFR. What is interesting about these coronaviruses is that despite their similar modes of infection, they demonstrate a range of virulence: SARS had a final CFR of 10 percent, while SARS-CoV-2 has a pending CFR of 1.4 percent. Differences in virulence between the 2002 and 2019 SARS outbreaks could be attributed to varying levels of care between countries.

The chart above displays WHO data of the relationship between the total number of cases in a country and the CFR during the 2002-2003 SARS-CoV epidemic. South Africa, on the far right, had only a single case. The patient died, which resulted in a 100 percent CFR. China, on the other hand, had 5,327 cases and 349 deaths, giving a 7 percent CFR. The chart below zooms to the bottom left corner of the graph, so as to better resolve critically affected countries, those with a caseload of less than 1,000, but with a high CFR.

Here is Hong Kong, with 1,755 cases and a 17 percent CFR. There is also Taiwan, with 346 cases and an 11 percent CFR. Finally, nearly tied with Canada is Singapore with 238 cases and a 14 percent CFR.

With COVID-19, it’s apparent that outcome reflects experience. China has 82,747 cases of COVID, but has lowered their CFR to 4 percent. Hong Kong has 1,026 cases and a 0.4 percent CFR. Taiwan has 422 cases at 1.5 percent CFR, and Singapore with 8,014 cases, has a 0.13 percent CFR.

It was the novel coronavirus identification program established in China in the wake of the 2002 SARS epidemic that alerted authorities to SARS-CoV-2 back in November of 2019. The successful responses by Taiwan, Hong Kong, and Singapore can also be attributed to a residual familiarity with the dangers of an unknown virus, and the sorts of interventions that are necessary to prevent a crisis from spiraling out of control.

In West Africa, too, they seem to have learned the value of being prepared. When Ferme returned to Liberia on March 7, she encountered airport staff fully protected with gowns, head covers, face screens, masks, and gloves. By the time she left the country, 10 days later, she said, “Airline personnel were setting up social distancing lines, and [rural vendors] hawking face masks. Motorcycle taxis drivers, the people most at risk after healthcare workers—all had goggles and face masks.”

The sheer number of COVID-19 cases indicates the road to recovery will take some time. Each must be identified, quarantined, and all contacts traced and tested. Countries that failed to act swiftly, which allowed their case numbers to spiral out of control, will pay in lives and dollars. Northwestern University economists Martin Eichenbaum et al. modeled6 the cost of a yearlong shutdown to be $4.2 trillion, a cost that proactive countries will not face. A recent Harvard study7 published in Science suggests the virus will likely make seasonal appearances going forward, potentially requiring new waves of social distancing. In other words, initial hesitancy will have repercussions for years. In the future, smart containment principles,6 where restrictions are applied on the basis of health status, may temper the impact of these measures.

Countries that failed to act swiftly, which allowed their case numbers to spiral out of control, will pay in lives and dollars.

Inaction was initially framed as promoting herd immunity, where spread of the virus is interrupted once everyone has fallen sick with it. This is because getting the virus results in the same antibody production process as getting vaccinated—but doesn’t require the development of a vaccine. The Johns Hopkins Bloomberg School of Public Health estimates that 70 percent of the population will need to be infected with or vaccinated against the virus8 for herd immunity to work. Progress toward it has been slow, and can only be achieved through direct infection with the virus, meaning many will die. A Stanford University study in Santa Clara County9 suggests only 2.5 percent to 4.2 percent of the population have had the virus. Another COVID hotspot in Gangelt, Germany, suggests 15 percent10—higher, but still nowhere near the 70 percent necessary for herd immunity. Given the dangers inherent in waiting on herd immunity, our best hope is a vaccine.

A key concern for effective vaccine development is viral mutation. This is because vaccines train the immune system to recognize specific shapes on the surface of the virus—a composite structure called the antigen. Mutations threaten vaccine development because they can change the shape of the relevant antigen, effectively allowing the pathogen to evade immune surveillance. But, so far, SARS-CoV-2 has been mutating slowly, with only one mutation found in the section most accessible to the immune system, the spike protein. What this suggests is that the viral genome may be sufficiently stable for vaccine development.

What we know, though, is that Ebola was extinguished due to cooperation between public health officials and community leaders. SARS-CoV ended when all cases were identified and quarantined. The Spanish Flu in 1918 vanished after two long, deadly seasons.

The final outcome of COVID-19 is still unclear. It will ultimately be decided by our patience and the financial bottom line. With 26 million unemployed and protests erupting around the country, it seems there are many who would prefer to risk life and limb rather than face financial insolvency. Applying smart containment principles in the aftermath of the shutdown might be the best way to get the economy moving again, while maintaining the safety of those at greatest risk. Going forward, vigilance and preparedness will be the watchwords of the day, and the most efficient way to prevent social and economic ruin.

Anastasia Bendebury and Michael Shilo DeLay did their PhDs at Columbia University. Together they created Demystifying Science, a science literacy organization devoted to providing clear, mechanistic explanations for natural phenomena. Find them on Twitter @DemystifySci.

References

1. Genomic epidemiology of novel coronavirus - Global subsampling. Nextstrain www.nextstrain.org.

2. Covid-19 TrialsTracker. TrialsTracker www.trialstracker.net.

3. Struck, M. Vaccine R&D success rates and development times. Nature Biotechnology 14, 591-593 (1996).

4. Breman, J. & Johnson, K. Ebola then and now. The New England Journal of Medicine 371 1663-1666 (2014).

5. Baseler, L., Chertow, D.S., Johnson, K.M., Feldmann, H., & Morens, D.M. THe pathogenesis of Ebola virus disease. The Annual Review of Pathology 12, 387-418 (2017).

6. Eichenbaum, M., Rebell, S., & Trabandt, M. The macroeconomics of epidemics. The National Bureau of Economic Research Working Paper: 26882 (2020).

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

8. D’ Souza, G. & Dowdy, D. What is herd immunity and how can we achieve it with COVID-19? Johns Hopkins COVID-19 School of Public Health Insights www.jhsph.edu (2020).

9. Digitale, E. Test for antibodies against novel coronavirus developed at Stanford Medicine. Stanford Medicine News Center Med.Stanford.edu (2020).

10. Winkler, M. Blood tests show 14%of people are now immune to COVID-19 in one town in Germany. MIT Technology Review (2020).

Lead image: Castleski / Shutterstock

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Plants are intelligent beings with profound wisdom to impart—if only we know how to listen. And Monica Gagliano knows how to listen. The evolutionary ecologist has done groundbreaking experiments suggesting plants have the capacity to learn, remember, and make choices. That’s not all. Gagliano, a senior research fellow at the University of Sydney in Australia, talks to plants. And they talk back. Plants summon her with instructions on how to live and work. Some of Gagliano’s conversations happened in prophetic dreams, which led her to study with a shaman in Peru while tripping on psychoactive plants.

Along with forest scientists like Suzanne Simard and Peter Wohlleben, Gagliano raises profound scientific and philosophical questions about the nature of intelligence and the possibility of “vegetal consciousness.” But what’s unusual about Gagliano is her willingness to talk about her experiences with shamans and traditional healers, along with her use of psychedelics. For someone who’d already received fierce pushback from other scientists, it was hardly a safe career move to reveal her personal experiences in otherworldly realms.

Gagliano considers her explorations in non-Western ways of seeing the world to be part of her scientific work. “Those are important doors that you need to open and you either walk through or you don’t,” she told me. “I simply decided to walk through.” Sometimes, she said, certain plants have given her precise directions on how to conduct her experiments, even telling her which plant to study. But it hasn’t been easy. “Like Alice, [I] found myself tumbling down a rather strange rabbit hole,” she wrote in a 2018 memoir, Thus Spoke the Plant. “I did doubt my own sanity many times, especially when all these odd occurrences started—and yet I know I do not suffer from psychoses.”

Shortly before the COVID-19 lockdown, I talked with Gagliano at Dartmouth College, where she was a visiting scholar. We spoke about her experiments, the new field of plant intelligence, and her own experiences of talking with plants.

PAVLOV’S PEAS: Monica Gagliano sketches a pea plant in her lab at the University of Sydney (above). She conducted experiments with pea plants to determine if, like Pavlov’s famous dogs, the plants learned to anticipate food. They did. “Although they do not salivate,” Gagliano says.Scene from the upcoming documentary, AWARE ©umbrellafilms.org

You are best known for an experiment with Mimosa pudica, commonly known as the “sensitive plant,” which instantly closes its leaves when it’s touched. Can you describe your experiment?

I built a little contraption that allowed me to drop the plants from a height of maybe 15 centimeters. So it’s not too high. When they fall, they land in a softly padded base. This plant closes its leaves when disturbed, especially if the disturbance is a potential predator. When the leaves are closed, big, spiny, pointy things stick out, so they might deter a predator. In fact, they not only close the leaf, but literally droop, like, “Look, I’m dead. No juice for you here.”

You did this over and over, dropping the plants repeatedly.

Exactly. It makes no sense for a plant or animal to repeat a behavior that is actually useless, so we learn pretty quick that whatever is useless, you don’t do anymore. You’re wasting a lot of energy trying to do something that doesn’t actually help. So, can the plant—in this case, Mimosa—learn not to close the leaves when the potential predator is not real and there are no bad consequences afterward?

After how many drops did they stop closing their leaves?

The test is for a specific type of learning that is called habituation. I decided they would be dropped continuously for 60 times. Then there was a big pause to let them rest and I did it again. But the plants were already re-opening their leaves after the first three to six drops. So within a few minutes, they knew exactly what was going on—like, “Oh my god, this is really annoying but it doesn’t mean anything, so I’m just not going to bother closing. Because when my leaves are open, I can eat light.” So there is a tradeoff between protecting yourself when the threat is real and continuing to feed and grow. I left the plants undisturbed for a month and then came back and repeated the same experiment on those individuals. And they showed they knew exactly what was going on. They were trained.

This is who I am. And nobody has the right to tell me that it’s not real.

You say these plants “understand” and “learn” that there’s no longer a threat. And you’re suggesting they “remember.” You’re not using these words metaphorically. You mean this literally?

Yes, that’s what they’re doing. This is definitely memory. It’s the same kind of experiment we do with a bee or a mouse. So using the words “memory” and “learning” feels totally appropriate. I know that some of my colleagues accuse me of anthropomorphizing, but there is nothing anthropomorphic about this. These are terms that refer to certain processes. Memory and learning are not two separate processes. You can’t learn unless you remember. So if a plant is ticking all the boxes and doing what you would expect a rat or a mouse or a bee to do, then the test is being passed.

Do you think these plants are actually making decisions about whether or not to close their leaves?

This experiment with Mimosa wasn’t designed to test that specific question. But later, I did experiments with other plants, with peas in particular, and yes, there is no doubt the plants make choices in real decision-making. This was tested in the context of a maze, where the test is actually to make a choice between left and right. The choice is based on what you might gain if you choose one side or the other. I did one study with peas that showed the plants can choose the right arm in a maze based on where the sound of water is coming from. Of course, they want water. So they will use the signal to follow that arm of the maze as they try to find the source of water.

So plants can hear water?

Oh, yeah, of course. And I’m not talking about electrical signals. We have also discovered that plants emit their own sounds. The acoustic signal comes out of the plant.

What kind of sounds do they make?

We call them clicks, but this is where language might fail because we are trying to describe something we’re not familiar enough with to create the language that really describes the picture. We worked out that, yes, plants not only produce their own sound, which is amazing, but they are listening to sounds. We are surrounded by sound, so there are studies, like my own study, of plants moving toward certain frequencies and then responding to sounds of potential predators chewing on leaves, which other plants that are not yet threatened can hear. “Oh, that’s a predator chewing on my neighbor’s leaves. I better put my defenses up.” And more recently, there was some work done in Israel on the sound of bees and how flowers prepared themselves and become very nice and sweet, literally, to be more attractive to the bee. So the level of sugars gets increased as a bee passes by.

SECRET LIFE OF PLANTS: Monica Gagliano says her experiences with indigenous people, such as the Huichol in Mexico (above), informed her view that plants have a range of feelings. “I don’t know if they would use those words to describe joy or sadness, but they are feeling bodies,” she says.Scene from the upcoming documentary, AWARE ©umbrellafilms.org

You are describing a surprising level of sophistication in these plants. Do you have a working definition of “intelligence?”

That’s one of those touchy subjects. I use the Latin etymology of the word and “intelligere” literally means something like “choosing between.” So intelligence really underscores decision-making, learning, memory, choice. As you can imagine, all those words are also loaded. They belong in the cognitive realm. That’s why I define all of this work as “cognitive ecology.”

Do you see parallels between this kind of intelligence in plants and the collective intelligence that we associate with social insects in ant colonies or beehives?

That kind of intelligence might be referred to as “distributed intelligence” or “collective intelligence.” We are testing those questions right now. Plants don’t have neurons. They don’t have a brain, which is often what we assume is the base for all of these behaviors. But like slime molds and other basal animals that don’t have neural systems, they seem to be doing the same things. So the short answer is yes.

What you’re saying is very controversial among scientists. The common criticism of your views is that an organism needs a brain or at least a nervous system to be able to learn or remember. Are you saying neurons are not required for intelligence?

Science is full of assumptions and presuppositions that we don’t question. But who said the brain and the neurons are essential for any form of intelligence or learning or cognition? Who decided that? And when I say neurons and brains are not required, it’s not to say they’re not important. For those organisms like ourselves and many animals who do have neurons and brains, it’s amazing. But if we look at the base of the animal kingdom, sponges don’t have neurons. They look like plants because when they’re adults, they settle on the bottom of the ocean and pretty much just sit there forever. Yet if you look at the sponge’s genome, they have the genetic code for the neural system. It’s almost like from an evolutionary perspective, they simply decided that developing a neural system was not useful. So they went a different way. Why would you invest that energy if you don’t need it? You can achieve the same task in different ways.

Your food is psychedelic. It changes your brain chemistry all the time.

Your critics say these are just automatic adaptive responses. This is not really learning.

You know, they just say plants do not learn and do not remember. Then you do this study and stumble on something that actually shows you otherwise. It’s the job of science to be humble enough to realize that we actually make mistakes in our thinking, but we can correct that. Science grows by correcting and modifying and adjusting what we once thought was the fact. I went and asked, can plants do Pavlovian learning? This is a higher kind of learning, which Pavlov did with his dogs salivating, expecting dinner. Well, it turns out plants actually can do it, but in a plant way. So plants do not salivate and dinner is a different kind of dinner. Can you as a scientist create the space for these other organisms to express their own, in this case, “plantness,” instead of expecting them to become more like you?

There’s an emerging field of what’s called “vegetal consciousness.” Do you think plants have minds?

What is the mind? [Laughs] You see, language is very inadequate at the moment in describing this field. I could ask you the same question in referring to humans. Do you think humans have a mind? And I could answer again, what is the mind? Of course, I have written a paper with the title “The Mind of Plants” and there is a book coming called The Mind of Plants. In this context, language is used to capture aspects of how plants can change their mind, and also whether they have agency. Is there a “person” there? These questions are relevant beyond science because they have ethical repercussions. They demand a change in our social attitude toward the environment. But I already have a problem with the language we are using because the question formulated in that way demands a yes or no answer. And what if the answer cannot be yes or no?

Let me ask the question a different way. Do you think plants have emotional lives? Can they feel pain or joy?

It’s the same question. Where do feelings arise from, and what are feelings? These are yes or no questions, usually. But to me, they are yes and no. It depends on what you mean by “feeling” and “joy.” It also depends on where you are expecting the plant to feel those things, if they do, and how you recognize them in a human way. I mean, plants might have more joy than we do. It’s just that we don’t know because we’re not plants.

We have only talked about this from the scientific perspective, which is the Western view of the world. But I’ve also had a close relationship with plants from a very different perspective, the indigenous world view. Why is that less valuable? And when you actually do explore those perspectives, they require your experience. You can’t just understand them by thinking about them. My own personal experience tells me that plants definitely feel many things. I don’t know if they would use those words to describe joy or sadness, but they are feeling bodies. We are feeling bodies.

Science is full of assumptions and presuppositions that we don’t question.

You’ve studied with shamans in indigenous cultures and you’ve taken ayahuasca and other psychoactive plants. Why did you seek out those experiences?

I didn’t. They sought me. So I just followed. They just arrived in my life. You know, those are important doors that you need to open and you either walk through or you don’t. I simply decided to walk through. I had this weird series of three dreams while I was in Australia doing my normal life. By the time the third dream came, it was very clear that the people that I was dreaming of were real people. They were waiting somewhere in this reality, in this world. And the next thing, I’m buying a ticket and going to Peru and my partner at the time is looking at me like, “What are you doing?” [laughs] I have no idea, but I need to go. As a scientist, I find this is the most scientific approach that I’ve ever had. It’s like there is something asking a question and is calling you to meet the answer. The answer is already there and is waiting for you, if you are prepared to open the door and cross through. And I did.

What did you do in Peru?

The first time I went, I found this place that was in my dream. It was just exactly the same as what I saw in my dream. It was the same man I saw in my dream, grinning in the same way as he was in my dream. So I just worked with him, trying to learn as much as I could about myself with his support.

This was a local shaman whom you identify as Don M. And there was a particular plant substance, a hallucinogen, that you took.

I did what they call a “dieta,” which is basically a quiet, intense time in isolation that you do on your own in a little hut. You are just relating with the plant that the elder is deciding on. So for me, the plant that I worked with wasn’t by itself a psychedelic in the normal way of thinking about it. But of course, all plants are psychedelic. Even your food is psychedelic because it changes your brain chemistry and your neurobiology all the time you eat. Sugars, almonds, all sorts of neurotransmitters are flying everywhere. So, again, even the idea of what a psychedelic experience is needs to be revised, because a lot of people might think that it’s only about certain plants that they have a very strong, powerful transformation. And I find that all plants are psychedelic. I can sit in my garden. I don’t have to ingest anything and I can feel very altered by that experience.

You’ve said the plant talked to you. Did you actually hear words?

When you’re trying to describe this to people haven’t had the experience, it probably doesn’t make much sense because this kind of knowledge requires your participation. I don’t hear someone talking to me as if from the outside, talking to me in words and sound. But even that is not correct because inside my head it does sound exactly like a conversation. Not only that, but I know it’s not me. There is no way that I would know about some of the information that’s been shared with me.

Are you saying these plants had specific information to tell you about your life and your work?

Yeah, I mean, some of the plants tell me exactly how wrong I was in thinking about my experiments and how I should be doing them to get them to work. And I’m like, “Really?” I’m scribbling down without really understanding. Then I go in the lab and try what they say. And even then, there is a part of me that doesn’t really believe it. For one experiment, the one on the Pavlovian pea, I was trying to address that question the year before with a different plant. I was using sunflowers. And while I was doing my dieta with a different tree back in Peru, the plant just turned up and said, “By the way, not sunflowers, peas.” And I’m like, “what?” People always think that when you have these experiences, you’re supposed to understand the secrets of the universe. No, my plants are usually quite practical. [laughs] And they were right.

Do you think you are really encountering the consciousness of that plant? Maybe your imagination has opened up to see the world in new ways, but it’s all just a projection of your own mind. How do you know you are actually encountering another intelligence?

If you had this experience of connecting with plants the way I have described—and there are plenty of people who have—the experience is so clear that you know that it’s not you; it’s someone else talking. If you haven’t had that experience, then I can totally see it’s like, “No way, it must be your mind that makes it up.” But all I can say is that I have had exchanges with plants who have shared things about topics and asked me to do things that I have really no idea about.

What have plants asked you to do?

I’m not a medical scientist, but I’ve been given information by plants about their medical properties. And these are very specific bits of information. I wrote them in my diary. I would later check and I did find them in the medical literature: “This plant is for this and we know this.” I just didn’t know. So maybe I’m tapping into the collective consciousness.

What do you do with these kinds of personal experiences? You are a scientist who’s been trained to observe and study and measure the physical world. But this is an entirely different kind of reality. Can you reconcile these two different realities?

I think there are some presuppositions that a scientist should just explore the consensus reality that most of us experience in more or less the same way. But I don’t really have a conflict because I find this is just part of experimenting and exploring. If anything, I found that it has enriched and expanded the science I do. This is a work in progress, obviously, but I think I’m getting better at it. And in the writing of my book, which for a scientist was a very scary process because it was laying bare some parts of me that I knew would likely compromise my career forever, it also became liberating because once it was written, now the world knows. And it’s my truth. This is how I operate. This is who I am. And nobody has the right or the authority to tell me that it’s not real.

Steve Paulson is the executive producer of Wisconsin Public Radio’s nationally syndicated show “To the Best of Our Knowledge.” He’s the author of Atoms and Eden: Conversations on Religion and Science. You can subscribe to TTBOOK’s podcast here.

Lead image: kmeds7 / Shutterstock

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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).

Lead image: yucelyilmaz / Shutterstock

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The coronavirus news from Mozambique is mixed, as it is in much of sub-Saharan Africa. Many experts fear chaos is inevitable.Photograph by gaborbasch / Shutterstock

We called Greg Carr the other day to talk about the spread of the coronavirus in Africa. Carr, who has been featured in Nautilus, is the founder of the Gorongosa Restoration Project, a partnership with the Mozambique government to revive Gorongosa National Park, that environmental treasure trove at the southern end of the Rift Valley. The 1,500 square-mile park, about the size of Rhode Island, was first given animal refuge status in the 1920s by the Portuguese, and for years was a favorite of European tourists. But in 1983 civil war broke out and the park became a no-man’s land. The place was poached to death, closed up and didn’t reopen until 1992.

Renewal began in 2004 and in 2008 the government signed a restoration agreement with Carr’s foundation. The agreement, which lasts through 2043, envisions a “human rights park” that will restore both ecosystems and economic vitality. After 11 years of rebuilding infrastructure, reintroducing animals, including hippos and wildebeests, and working with local communities, Gorongosa is thriving again. The park now serves as a model for future conservation. Today some 200,000 people live around the park in a “sustainable development zone” that includes education, employment opportunities, and health service. About 700 people have full time jobs in the park; another 300, part time. Naturalist E.O. Wilson calls Gorongosa “a window on eternity.”

“If there’s one thing the rest of the world can learn from Africans, it would be their resilience.”

Carr is a 60-year-old entrepreneur and philanthropist who grew up in Idaho and in his mid twenties co-founded Boston Technology, a voice mail company. By the time he turned 40 he had amassed his fortune and couldn’t see the fun in doing it all over again, and so turned to philanthropy. These days he’s in Idaho Falls, on the phone six hours a day, getting the latest reports from his staff in the park, now closed until further notice.

The coronavirus news from Mozambique is mixed, as it is in much of sub-Saharan Africa. With the exception of South Africa, with over 7,500 confirmed cases of COVID-19 and 148 deaths, some countries below the Equator have fewer than 100 cases. As of May 6, there were just 81 cases in Mozambique and no deaths. If these numbers don’t blow up, the quick explanation might hold that the median age in Sub Saharan Africa is under 20, just 17.6 in Mozambique; population density is low (103 people per square mile); and there’s relatively limited direct contact with heavily infected countries in other parts of the world. 

Still, many experts fear chaos is inevitable. Underlying conditions in Mozambique include implacable poverty and a 60-year history of colonial and civil wars. On another front, in early April, in northern Mozambique, an Isis group shot or beheaded 52 young people because they refused to be recruited. Add a 48 percent literacy rate for women, 60 percent for men. The country also suffers the world’s eighth-highest incidence of HIV; 1.5 million people have contracted the virus and nearly 40,000 people have died. Finally, a large number of Mozambicans go to South Africa for work and then return. Testing is rare in the entire country.

In March, CDC Africa sent out a national directive requiring social distancing. “People are going to pay more attention to that in the cities than they are in rural Mozambique, at least until the virus really comes,” Carr said the other day. “Now, if you live in rural Mozambique, you don’t have the luxury of saying, ‘I’m isolating at home.’ People have to go out every day, to get food and water, from 40 to 60 liters a day, they have to tend to their farms. The idea of social distancing is a bit impossible for these folks.” He added, “Schools are closed and we are making our own masks for people. We all know there’s no treatment per se or certainly vaccine. If this hits, we’ll only be able to offer people Tylenol and soup.”

Cases in Mozambique could shoot up as mine workers continue to return home from their jobs in South Africa. “In my opinion,” said Carr, “Mozambique does not have the capacity to deal with this type of pandemic, as there are few qualified health personnel and the high level of poverty leads people to resist isolating themselves, as they look for alternatives to take care of their families. Our Gorongosa teams are in the field, spreading prevention messages, distributing masks and water purification.” 

Berta Barros, head nurse at Gorongosa, told Carr recently she has three main worries: lack of COVID-19 test kits, lack of healthcare professionals to respond to sick patients, and shortage of medications for treatment. “Mozambique has a population close to 30 million and we only have 34 ventilators,” Barros said. “It’s beyond impossible to work and choose who to save.”

Carr often talks about Mozambique as though he was Mozambican. “We’re very practical people,” he’ll say. “We’re not really theoretical. We’re just going to work our way through this.” He shies away from broad, open-ended questions about Africa, much less cultural comparisons and grand conclusions. “Africa is more than 1 billion people in 54 countries with, what, 2,500 languages? To make a statement like, ‘Africa is this…’ Frankly, I just think a lot of it is complete baloney.”

At the same time, says Carr, “If there’s one thing the rest of the world can learn from Africans, it would be their resilience. We’ve had five years of war in Mozambique and then last year we had a cyclone that killed nearly 1,000 people. I didn’t even mention the two droughts we had in the last seven years and the armyworm that came through and ate everybody’s maize. These people had their homes washed away in a flood last year, lost everything. So they rebuild their homes and then someone says, ‘Hey, there might be a virus coming through.’ It’s just one thing after another.”

What impact might the pandemic have on animals in the park? What effect will it have on just recovered antelope populations, for example, and the inevitable increase in poaching as tourism subsides? How many resources will need to be taken away from the war on other diseases to fight this? Impossible to say. But an anecdote came to Carr’s mind that suggests the vagaries of death in Southern Africa. “I got a call from a dear friend of mine yesterday, a Mozambique good friend, who said her aunt had just died. I said, ‘Wow, do you think it was COVID?’ She goes, ‘No, she’d been suffering for a while with a bad kidney.’ Life is tough in Africa. Do we know for sure this woman didn’t also have COVID and that contributed? Maybe. The truth about Africa is that disaster is hardly news. Malaria is the most prolific killer. And when they turn 50, people die and often no one knows exactly what the cause was. It’s just the way life is.”

Mark MacNamara is an Asheville, North Carolina-based writer. His articles for Nautilus include “We Need to Talk About Peat” and “The Artist of the Unbreakable Code.”

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The officials deciding what to open, and when, seldom offer thoughtful rationales. Clearly, risk communication about COVID-19 is failing with potentially dire consequences.Photograph by michael_swan / Flickr

When I worked as a TV reporter covering health and science, I would often be recognized in public places. For the most part, the interactions were brief hellos or compliments. Two periods of time stand out when significant numbers of those who approached me were seeking detailed information: the earliest days of the pandemic that became HIV/AIDS and during the anthrax attacks shortly following 9/11. Clearly people feared for their own safety and felt their usual sources of information were not offering them satisfaction. Citizens’ motivation to seek advice when they feel they aren’t getting it from official sources is a strong indication that risk communication is doing a substandard job. It’s significant that one occurred in the pre-Internet era and one after. We can’t blame a public feeling misinformed solely on the noise of the digital age.

America is now opening up from COVID-19 lockdown with different rules in different places. In many parts of the country, people have been demonstrating, even rioting, for restrictions to be lifted sooner. Others are terrified of loosening the restrictions because they see COVID-19 cases and deaths still rising daily. The officials deciding what to open, and when, seldom offer thoughtful rationales. Clearly, risk communication about COVID-19 is failing with potentially dire consequences.

A big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do.

Peter Sandman is a foremost expert on risk communication. A former professor at Rutgers University, he was a top consultant with the Centers for Disease Control in designing crisis and emergency risk-communication, a field of study that combines public health with psychology. Sandman is known for the formula Risk = Hazard + Outrage. His goal is to create better communication about risk, allowing people to assess hazards and not get caught up in outrage at politicians, public health officials, or the media. Today, Sandman is a risk consultant, teamed with his wife, Jody Lanard, a pediatrician and psychiatrist. Lanard wrote the first draft of the World Health Organization’s Outbreak Communications Guidelines. “Jody and Peter are seen as the umpires to judge the gold standard of risk communications,” said Michael Osterholm of the Center for Infectious Disease Research and Policy at the University of Minnesota. Sandman and Lanard have posted a guide for effective COVID-19 communication on the center’s website.

I reached out to Sandman to expand on their advice. We communicated through email.

Sandman began by saying he understood the protests around the country about the lockdown. “It’s very hard to warn people to abide by social-distancing measures when they’re so outraged that they want to kill somebody and trust absolutely nothing people say,” he told me. “COVID-19 outrage taps into preexisting grievances and ideologies. It’s not just about COVID-19 policies. It’s about freedom, equality, too much or too little government. It’s about the arrogance of egghead experts, left versus right, globalism versus nationalism versus federalism. And it’s endlessly, pointlessly about Donald Trump.”

Since the crisis began, Sandman has isolated three categories of grievance. He spelled them out for me, assuming the voices of the outraged:

• “In parts of the country, the response to COVID-19 was delayed and weak; officials unwisely prioritized ‘allaying panic’ instead of allaying the spread of the virus; lockdown then became necessary, not because it was inevitable but because our leaders had screwed up; and now we’re very worried about coming out of lockdown prematurely or chaotically, mishandling the next phase of the pandemic as badly as we handled the first phase.”

• “In parts of the country, the response to COVID-19 was excessive—as if the big cities on the two coasts were the whole country and flyover America didn’t need or didn’t deserve a separate set of policies. There are countless rural counties with zero confirmed cases. Much of the U.S. public-health profession assumes and even asserts without building an evidence-based case that these places, too, needed to be locked down and now need to reopen carefully, cautiously, slowly, and not until they have lots of testing and contact-tracing capacity. How dare they destroy our economy (too) just because of their mishandled outbreak!”

• “Once again the powers-that-be have done more to protect other people’s health than to protect my health. And once again the powers-that-be have done more to protect other people’s economic welfare than to protect my economic welfare!” (These claims can be made with considerable truth by healthcare workers; essential workers in low-income, high-touch occupations; residents of nursing homes; African-Americans; renters who risk eviction; the retired whose savings are threatened; and others.)

In their article for the Center for Infectious Disease Research and Policy, Sandman and Lanard point out that coping with a pandemic requires a thorough plan of communication. This is particularly important as the crisis is likely to enter a second wave of infection, when it could be more devastating. The plan starts with six core principles: 1) Don’t over-reassure, 2) Proclaim uncertainty, 3) Validate emotions—your audience’s and your own, 4) Give people things to do, 5) Admit and apologize for errors, and 6) Share dilemmas. To achieve the first three core principles, officials must immediately share what they know, even if the information may be incomplete. If officials share good news, they must be careful not to make it too hopeful. Over-reassurance is one of the biggest dangers in crisis communication. Sandman and Lanard suggest officials say things like, “Even though the number of new confirmed cases went down yesterday, I don’t want to put too much faith in one day’s good news.” 

Sandman and Lanard say a big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do. They caution against invoking “science” as a sole reason for action, as science in the midst of a crisis is “incremental, fallible, and still in its infancy.” Expressing empathy, provided it’s genuine, is important, Sandman and Lanard say. It makes the bearer more human and believable. A major tool of empathy is to acknowledge the public’s fear as well as your own. There is good reason to be terrified about this virus and its consequences on society. It’s not something to hide.

Sandman and Lanard say current grievances with politicians, health officials, and the media, about how the crisis has been portrayed, have indeed been contradictory. But that makes them no less valid. Denying the contradictions only amplifies divisions in the public and accelerates the outrage, possibly beyond control. They strongly emphasize one piece of advice. “Before we can share the dilemma of how best to manage any loosening of the lockdown, we must decisively—and apologetically—disabuse the public of the myth that, barring a miracle, the COVID-19 pandemic can possibly be nearing its end in the next few months.”

Robert Bazell is an adjunct professor of molecular, cellular, and developmental biology at Yale. For 38 years, he was chief science correspondent for NBC News.

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