It is "too early" to begin easing any lockdown measures "in any meaningful way", Nicola Sturgeon has said.

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Nigel Farage has become the butt of mocking jokes online after sharing his Clap for Carers effort.

Too many workers are still falling through cracks in the Covid rescue package, a former cabinet minister warned today as he called for the lockdown to be eased "as quickly as possible".

The UK will begin the first round of post-Brexit trade deal talks with the US this week.

Boris Johnson has said he feared he would not live to see his son born as he battled coronavirus in hospital last month.

One of Boris Johnson's closest allies quit as a minister today after being found to have breached the MPs' code of conduct by trying to "intimidate" a company chairman involved in a loan row with his father.

Cabinet Office minister says Government does not want the UK to continue with its 'European Union-lite membership' beyond December 2020

Sir Keir Starmer has urged the Prime Minister to form a "national consensus" on the next phase of the Government's coronavirus response as ministers work on plans to ease the lockdown.

Downing Street has replaced a trade minister who resigned when an investigation found he threatened a company chairman over a financial dispute with his father.

Theresa May has hit out at world leaders for failing "to forge a coherent international response" to the coronavirus pandemic.

Admission that faster testing might have helped as UK hit by top death toll in Europe

Matt Hancock has slammed Professor Neil Ferguson for his "extraordinary" breach of coronavirus lockdown rules, adding he was left "speechless" by his actions.

EXCLUSIVE: Independent candidate withdraws after difficult decision over job 'I really, really dreamed of'

Scotland Yard has said Professor Neil Ferguson's behaviour is "plainly disappointing" but officers do not intend to take any further action.

He also said he "bitterly regrets" the crisis in care homes, where staff have hit out at a lack of testing and PPE. The latest figures show that nursing home fatalities are continuing to rise, standing at 2,794 in the week to April 24, despite deaths in all settings beginning to fall.

It should have been the first litmus test of Sir Keir Starmer's appeal - as well as a verdict on whether Boris Johnson's general election earthquake in former Red Wall regions translated into long term local success

In a video that first surfaced on Reddit over the weekend, Ezra Miller appears to choke a female fan who approaches the artist on the street.

Netflix has capitalized on the huge success of their docuseries Tiger King by releasing an “aftershow” special. Here are 5 things we learned.

Cardi B invited Bernie Sanders to join her on Instagram Live last night to talk politics, coronavirus and manicures.

Celebrities from across the globe came together Saturday night to lift their fans’ spirits as the world continues to cope with the coronavirus pandemic.

Tiffany Haddish, who is a spokesperson for Bumble, recently attended a virtual date with Common.

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.

Adele posted a rare photo of herself to celebrate her birthday, unveiling a dramatically altered appearance.

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.

I’m no stranger to forced isolation. For the better part of my 20s, I served as a nuclear submarine officer running secret missions for the United States Navy. I deployed across the vast Pacific Ocean with a hundred other sailors on the USS Connecticut, a Seawolf-class ship engineered in the bygone Cold War era to be one of the fastest, quietest, and deepest-diving submersibles ever constructed. The advanced reactor was loaded with decades of enriched uranium fuel that made steam for propulsion and electrical power so we could disappear under the waves indefinitely without returning to port. My longest stint was for two months, when I traveled under the polar ice cap to the North Pole with a team of scientists studying the Arctic environment and testing high frequency sonar and acoustic communications for under-ice operations. During deployments, critical-life events occur without you: holidays with loved ones, the birth of a child, or in my case, the New York Giants 2011-2012 playoff run to beat Tom Brady’s Patriots in the Super Bowl for the second time. On the bright side, being cut off from the outside world was a great first job for an introvert.

It’s been a month since COVID-19 involuntarily drafted me into another period of isolation far away from home. I’m in Turkey, where a two-week trip with my partner to meet her family has been extended indefinitely. There were no reported cases here and only a few in California in early March when we left San Francisco, where I run a business design studio. I had a lot of anticipation about Turkey because I’d never been here. Now I’m sheltering in a coastal town outside of Izmir with my partner, her parents, their seven cats, and a new puppy.

Shuttered in a house on foreign soil where I don’t speak the language, I have found myself snapping back into submarine deployment mode. Each day I dutifully monitor online dashboards of data and report the status of the spread at the breakfast table to no one in particular. I stay in touch with friends and family all over the world who tell me they’re going stir crazy and their homes are getting claustrophobic. But if there is one thing my experience as a submarine officer taught me, it’s that you get comfortable being uncomfortable.

My training began with psychological testing, although it may not be what you think. Evaluating mental readiness for underwater isolation isn’t conducted in a laboratory by clipboard-toting, spectacled scientists. The process to select officers was created by Admiral Hyman Rickover—the engineering visionary and noted madman who put the first nuclear reactor in a submarine—to assess both technical acumen and composure under stress. For three decades as the director of the Navy’s nuclear propulsion program, Rickover tediously interviewed every officer, and the recruiting folklore is a true HR nightmare: locking candidates in closets for hours, asking obtuse questions such as “Do something to make me mad,” and sawing down chair legs to literally keep one off balance.

Rickover retired from the Navy as its longest-serving officer and his successors carried on the tradition of screening each officer candidate, but with a slightly more dignified approach. Rickover’s ghost, though, seemed to preside over my interview process when I applied to be a submariner as a junior at the U.S. Naval Academy in Annapolis, Maryland. I was warned by other midshipmen that I would fail on the spot if I initiated a handshake. So, dressed in my formal navy blue uniform and doing my best to avoid tripping into accidental human contact, I rigidly marched into the Admiral’s office, staring straight ahead while barking my resume. When I took a seat on the unaltered and perfectly level chair in front of his desk, the Admiral asked me bluntly why I took so many philosophy classes and if I thought I could handle the technical rigors of nuclear power school. My response was a rote quip from John Paul Jones’ “Qualifications of a Naval Officer.” “Admiral, an officer should be a gentleman of liberal education, refined manners, punctilious courtesy, and the nicest sense of personal honor.” My future boss looked at me, shook his head like he thought I’d be a handful, and told me I got the job.

Confinement opened something up in my psyche and I gave myself permission to let go of my anxieties.

Nuclear power training is an academic kick in the face every day for over a year. The curriculum is highly technical and the pedagogy resembles a cyborg assembly-line without even a hint of the Socratic method. Our grades were conspicuously posted on the classroom wall and a line was drawn between those who passed and those who failed. I was below the line enough to earn the distinguished dishonor of 25 additional study hours each week, which meant I was at school at 5 a.m. and every weekend. This is how the Nuclear Navy builds the appropriate level of knowledge and right temperament to deal with shipboard reactor operations.

I finally sat down for a formal psychological evaluation a few months before my first deployment. I was ushered into a room no bigger than a broom closet and instructed to click through a computer-based questionnaire with multiple-choice questions about my emotions. I never did  learn the results, so I assume my responses didn’t raise too many red flags.

During my first year onboard, I spent all my waking hours either supervising reactor operations or learning the intricacies of every inch of the 350-foot tube and the science behind how it all worked. The electrolysis machine that split water molecules to generate oxygen was almost always out of commission, so instead we burned chlorate candles that produced breathable air. Seawater was distilled each day for drinking and shower water. Our satellite communications link had less bandwidth than my dial-up modem in the 1990s and we were permitted to send text-only emails to friends and family at certain times and in certain locations so as not to risk being detected. I took tests every month to demonstrate proficiency in nuclear engineering, navigation, and the battle capabilities of the ship. When I earned my submarine warfare qualification, the Captain pinned the gold dolphins insignia on my uniform and gave me the proverbial keys to the $4 billion warship. At that point, I was responsible for coordinating missions and navigating the ship as the Officer of the Deck.

Modern submarines are hydrodynamically shaped to have the most efficient laminar flow underwater, so that’s where we operated 99 percent of the time. The rare exception to being submerged is when we’d go in and out of port. The most unfortunate times were long transits tossing about in heavy swells, which made for a particularly nauseated cruise. To this day, conjuring the memory of some such sails causes a reflux flashback. A submariner’s true comfort zone is beneath the waves so as soon as we broke ties with the pier we navigated toward water that was deep enough for us to dive.

It’s unnatural to stuff humans, torpedoes, and a nuclear reactor into a steel boat that’s intentionally meant to sink. This engineering marvel ranks among the most complex, and before we’d proceed below and subject the ship and its inhabitants to extreme sea pressures, the officers would visually inspect thousands of valves to verify the proper lineup of systems that would propel us to the surface if we started flooding uncontrollably and sinking—a no-mistakes procedure called rigging for dive. Once we’d slip beneath the waves, the entire crew would walk around to check for leaks before we’d settle into a rotation of standing watch, practicing our casualty drills, engineering training, eating, showering (sometimes), and sleeping (rarely). The full cycle was 18 hours, which meant the timing of our circadian cycles were constantly changing. Regardless of the amount of government-issued Folger’s coffee I’d pour down my throat, I’d pass out upon immediate contact with my rack (the colloquialism for a submarine bunk in which your modicum of privacy was symbolized by a cloth curtain).

As an officer, I lived luxuriously with only two other grown men in a stateroom no bigger than a walk-in closet. Most of the crew slept stacked like lumber in an 18-person bunk room and they all took turns in the rack. This alternative lifestyle is known as hot-racking, because of the sensation you get when you crawl into bedding that’s been recently occupied. The bunk rooms are sanctuaries where silence is observed with monastic intensity. Slamming the door or setting an alarm clock was a cardinal sin so wakeups were conducted by a junior sailor who gently coaxed you awake when it was time to stand watch. Lieutenant Weiner, it’s time to wake up. You’ve got the midnight watch, sir. Words that haunt my dreams.

The electrolysis machine was out of commission, so we burned chlorate candles that produced breathable air.

I maintained some semblance of sanity and physical fitness by sneaking a workout on a rowing erg in the engine room or a stationary bike squeezed between electronics cabinets. The rhythmic beating of footsteps on a treadmill was a noise offender—the sound could be detected on sonar from miles away—so we shut it off unless we were in friendly waters where we weren’t concerned with counter-detection.

Like a heavily watered-down version of a Buddhist monk taking solitary retreat in a cave, my extended submarine confinements opened something up in my psyche and I gave myself permission to let go of my anxieties. Transiting underneath a vast ocean in a vessel with a few inches of steel preventing us from drowning helps put things into perspective. Now that I’m out of the Navy, I have more appreciation for the freedoms of personal choice, a fresh piece of fruit, and 24 hours in a day. My only regrets are not keeping a journal or having the wherewithal to discover the practice of meditation under the sea.

Today, I’m learning Turkish so I can understand more about what’s happening around me. I’m doing Kundalini yoga (a moving meditation that focuses on breathwork) and running on the treadmill (since I’m no longer concerned about my footsteps being detected on sonar). On my submarine, I looked at photos to stay connected to the world I left behind, knowing that I’d return soon enough. Now our friend who is isolating in our apartment in San Francisco sends us pictures of our cat and gives us reports about how the neighborhood has changed.

It’s hard to imagine that we’ll resume our lifestyles exactly as they were. But the submariner in me is optimistic that we have it in us to adapt to whatever conditions are waiting for us when it’s safe to ascend from the depths and return to the surface.

Steve Weiner is the founder of Very Scarce, a business design studio. He used to lead portfolio companies at Expa and drive nuclear submarines in the U.S. Navy. He has an MBA from The Wharton School and a BS from the U.S. Naval Academy. Instagram: @steve Twitter: @weenpeace

Lead image: Mike H. / Shutterstock

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In the 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.

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

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.

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

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.”

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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Visitors' health status will be checked on a smartphone app before they enter the park. Once inside, they will be required to wear face masks at all times unless they are eating.

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