Gain-of-function mutation research may help predict the next pandemic — or, critics argue, cause one.
This article was originally published on our sister site, Freethink.
"I was intrigued," says Ron Fouchier, in his rich, Dutch-accented English, "in how little things could kill large animals and humans."
It's late evening in Rotterdam as darkness slowly drapes our Skype conversation.
This fascination led the silver-haired virologist to venture into controversial gain-of-function mutation research — work by scientists that adds abilities to pathogens, including experiments that focus on SARS and MERS, the coronavirus cousins of the COVID-19 agent.
If we are to avoid another influenza pandemic, we will need to understand the kinds of flu viruses that could cause it. Gain-of-function mutation research can help us with that, says Fouchier, by telling us what kind of mutations might allow a virus to jump across species or evolve into more virulent strains. It could help us prepare and, in doing so, save lives.
Many of his scientific peers, however, disagree; they say his experiments are not worth the risks they pose to society.
A virus and a firestorm
The Dutch virologist, based at Erasmus Medical Center in Rotterdam, caused a firestorm of controversy about a decade ago, when he and Yoshihiro Kawaoka at the University of Wisconsin-Madison announced that they had successfully mutated H5N1, a strain of bird flu, to pass through the air between ferrets, in two separate experiments. Ferrets are considered the best flu models because their respiratory systems react to the flu much like humans.
The mutations that gave the virus its ability to be airborne transmissible are gain-of-function (GOF) mutations. GOF research is when scientists purposefully cause mutations that give viruses new abilities in an attempt to better understand the pathogen. In Fouchier's experiments, they wanted to see if it could be made airborne transmissible so that they could catch potentially dangerous strains early and develop new treatments and vaccines ahead of time.
The problem is: their mutated H5N1 could also cause a pandemic if it ever left the lab. In Science magazine, Fouchier himself called it "probably one of the most dangerous viruses you can make."
Just three special traits
Recreated 1918 influenza virionsCredit: Cynthia Goldsmith / CDC / Dr. Terrence Tumpey / Public domain via Wikipedia
For H5N1, Fouchier identified five mutations that could cause three special traits needed to trigger an avian flu to become airborne in mammals. Those traits are (1) the ability to attach to cells of the throat and nose, (2) the ability to survive the colder temperatures found in those places, and (3) the ability to survive in adverse environments.
A minimum of three mutations may be all that's needed for a virus in the wild to make the leap through the air in mammals. If it does, it could spread. Fast.
Fouchier calculates the odds of this happening to be fairly low, for any given virus. Each mutation has the potential to cripple the virus on its own. They need to be perfectly aligned for the flu to jump. But these mutations can — and do — happen.
"In 2013, a new virus popped up in China," says Fouchier. "H7N9."
H7N9 is another kind of avian flu, like H5N1. The CDC considers it the most likely flu strain to cause a pandemic. In the human outbreaks that occurred between 2013 and 2015, it killed a staggering 39% of known cases; if H7N9 were to have all five of the gain-of-function mutations Fouchier had identified in his work with H5N1, it could make COVID-19 look like a kitten in comparison.
H7N9 had three of those mutations in 2013.
Gain-of-function mutation: creating our fears to (possibly) prevent them
Flu viruses are basically eight pieces of RNA wrapped up in a ball. To create the gain-of-function mutations, the research used a DNA template for each piece, called a plasmid. Making a single mutation in the plasmid is easy, Fouchier says, and it's commonly done in genetics labs.
If you insert all eight plasmids into a mammalian cell, they hijack the cell's machinery to create flu virus RNA.
"Now you can start to assemble a new virus particle in that cell," Fouchier says.
One infected cell is enough to grow many new virus particles — from one to a thousand to a million; viruses are replication machines. And because they mutate so readily during their replication, the new viruses have to be checked to make sure it only has the mutations the lab caused.
The virus then goes into the ferrets, passing through them to generate new viruses until, on the 10th generation, it infected ferrets through the air. By analyzing the virus's genes in each generation, they can figure out what exact five mutations lead to H5N1 bird flu being airborne between ferrets.
And, potentially, people.
"This work should never have been done"
The potential for the modified H5N1 strain to cause a human pandemic if it ever slipped out of containment has sparked sharp criticism and no shortage of controversy. Rutgers molecular biologist Richard Ebright summed up the far end of the opposition when he told Science that the research "should never have been done."
"When I first heard about the experiments that make highly pathogenic avian influenza transmissible," says Philip Dormitzer, vice president and chief scientific officer of viral vaccines at Pfizer, "I was interested in the science but concerned about the risks of both the viruses themselves and of the consequences of the reaction to the experiments."
In 2014, in response to researchers' fears and some lab incidents, the federal government imposed a moratorium on all GOF research, freezing the work.
Some scientists believe gain-of-function mutation experiments could be extremely valuable in understanding the potential risks we face from wild influenza strains, but only if they are done right. Dormitzer says that a careful and thoughtful examination of the issue could lead to processes that make gain-of-function mutation research with viruses safer.
But in the meantime, the moratorium stifled some research into influenzas — and coronaviruses.
The National Academy of Science whipped up some new guidelines, and in December of 2017, the call went out: GOF studies could apply to be funded again. A panel formed by Health and Human Services (HHS) would review applications and make the decision of which studies to fund.
As of right now, only Kawaoka and Fouchier's studies have been approved, getting the green light last winter. They are resuming where they left off.
Pandora's locks: how to contain gain-of-function flu
Here's the thing: the work is indeed potentially dangerous. But there are layers upon layers of safety measures at both Fouchier's and Kawaoka's labs.
"You really need to think about it like an onion," says Rebecca Moritz of the University of Wisconsin-Madison. Moritz is the select agent responsible for Kawaoka's lab. Her job is to ensure that all safety standards are met and that protocols are created and drilled; basically, she's there to prevent viruses from escaping. And this virus has some extra-special considerations.
The specific H5N1 strain Kawaoka's lab uses is on a list called the Federal Select Agent Program. Pathogens on this list need to meet special safety considerations. The GOF experiments have even more stringent guidelines because the research is deemed "dual-use research of concern."
There was debate over whether Fouchier and Kawaoka's work should even be published.
"Dual-use research of concern is legitimate research that could potentially be used for nefarious purposes," Moritz says. At one time, there was debate over whether Fouchier and Kawaoka's work should even be published.
While the insights they found would help scientists, they could also be used to create bioweapons. The papers had to pass through a review by the U.S. National Science Board for Biosecurity, but they were eventually published.
Intentional biowarfare and terrorism aside, the gain-of-function mutation flu must be contained even from accidents. At Wisconsin, that begins with the building itself. The labs are specially designed to be able to contain pathogens (BSL-3 agricultural, for you Inside Baseball types).
They are essentially an airtight cement bunker, negatively pressurized so that air will only flow into the lab in case of any breach — keeping the viruses pushed in. And all air in and out of the lap passes through multiple HEPA filters.
Inside the lab, researchers wear special protective equipment, including respirators. Anyone coming or going into the lab must go through an intricate dance involving stripping and putting on various articles of clothing and passing through showers and decontamination.
And the most dangerous parts of the experiment are performed inside primary containment. For example, a biocontainment cabinet, which acts like an extra high-security box, inside the already highly-secure lab (kind of like the radiation glove box Homer Simpson is working in during the opening credits).
"Many people behind the institution are working to make sure this research can be done safely and securely." — REBECCA MORITZ
The Federal Select Agent program can come and inspect you at any time with no warning, Moritz says. At the bare minimum, the whole thing gets shaken down every three years.
There are numerous potential dangers — a vial of virus gets dropped; a needle prick; a ferret bite — but Moritz is confident that the safety measures and guidelines will prevent any catastrophe.
"The institution and many people behind the institution are working to make sure this research can be done safely and securely," Moritz says.
No human harm has come of the work yet, but the potential for it is real.
"Nature will continue to do this"
They were dead on the beaches.
In the spring of 2014, another type of bird flu, H10N7, swept through the harbor seal population of northern Europe. Starting in Sweden, the virus moved south and west, across Denmark, Germany, and the Netherlands. It is estimated that 10% of the entire seal population was killed.
The virus's evolution could be tracked through time and space, Fouchier says, as it progressed down the coast. Natural selection pushed through gain-of-function mutations in the seals, similarly to how H5N1 evolved to better jump between ferrets in his lab — his lab which, at the time, was shuttered.
"We did our work in the lab," Fouchier says, with a high level of safety and security. "But the same thing was happening on the beach here in the Netherlands. And so you can tell me to stop doing this research, but nature will continue to do this day in, day out."
Critics argue that the knowledge gained from the experiments is either non-existent or not worth the risk; Fouchier argues that GOF experiments are the only way to learn crucial information on what makes a flu virus a pandemic candidate.
"If these three traits could be caused by hundreds of combinations of five mutations, then that increases the risk of these things happening in nature immensely," Fouchier says.
"With something as crucial as flu, we need to investigate everything that we can," Fouchier says, hoping to find "a new Achilles' heel of the flu that we can use to stop the impact of it."
Some of these trends may be due, in part, to the lockdown.
- Mortality rates from drug overdoses, homicides, and unintentional injuries increased since the pandemic began.
- Surprisingly, the suicide rate was below expectations.
- Cancer deaths may increase in coming years due to delayed diagnosis and reduced treatment.
In the U.S. the COVID pandemic cost hundreds of thousands of lives. Most deaths were directly attributable to the virus, but a substantial number were caused through the exacerbation of chronic social problems.
A catastrophic increase in drug overdoses
For instance, the CDC recently announced that drug overdose deaths last year jumped 30 percent from 2019, the worst single-year increase ever recorded. In 2020, there were roughly 93,000 overdose deaths, with 48 out of 50 states experiencing an increase. The New York Times reported:
"Several grim records were set: the most drug overdose deaths in a year; the most deaths from opioid overdoses; the most overdose deaths from stimulants like methamphetamine; the most deaths from the deadly class of synthetic opioids known as fentanyls."
While drug overdose deaths — particularly from fentanyl — have been a problem for several years, the lockdown worsened drug use nationwide.
Photo: Igor Normann / Adobe Stock
Homicides and accidents
Unfortunately, there was a notable increase in other causes of death, as well. A new paper in JAMA shows that from March to August 2020, homicides and unintentional injuries were higher than expected. The only good news is that deaths by suicide were lower than expected, a particularly surprising finding given that mental health issues skyrocketed during the pandemic.
To arrive at their conclusions, the authors investigated cause-specific mortality rates from January 2015 to February 2020. This allowed them to calculate an "expected" number of deaths from March to August 2020, which were then compared to the observed number of deaths during the first six months of the pandemic.
If the COVID pandemic had not occurred, the authors expected 1,404,634 Americans to die in the six months from March to August 2020. In reality, 1,661,271 died, an excess of 256,637 deaths. Of these, 174,334 were due to COVID-19, leaving 82,303 excess deaths in need of an explanation. Drug overdoses, homicides, and unintentional injuries accounted for many of them.
The authors speculated that drug overdose deaths and homicides may have increased due to economic stress. Also, treatment programs for substance abuse may have been disrupted.
Blame COVID for cancer, too
The pandemic will continue to shape cause-specific mortality in unexpected ways. According to new research in the Journal of the National Cancer Institute, there will be 2,487 excess breast cancer deaths by 2030 due to decreased screening, delayed diagnosis, and reduced treatment, representing a 0.52 percent cumulative increase over the number of expected breast cancer deaths.
Hospitals have also reported an increase in admissions for alcohol-related liver disease. USC has experienced a 30 percent uptick since March 2020.
Society will be dealing with the fallout of COVID for years to come.
Stay in touch with Derek on Twitter. His most recent book is "Hero's Dose: The Case For Psychedelics in Ritual and Therapy."
According to this research, eight percent of Americans always refuse vaccines. Why?
- New research found that 22 percent of Americans identify as somewhat or fully resistant to vaccination.
- Researchers used two social psychology theories to explore the causes of vaccine resistance.
- The more one identifies with an anti-vaccine group, the harder it is to dissuade them from their ideas.
Vaccine hesitancy is top of mind for global public health officials, and the reasons for this resistance are manifold. A group of American researchers recently focused on social identity as a motivating factor. Their study, published in the journal Politics, Groups, and Identities, found that group identification was an important factor for just over one-fifth of citizens.
Anti-vaxx social identification (AVSID) includes 22 percent of Americans — 14 percent of whom identify as "sometimes" resistant, while eight percent claim to "always" refuse vaccines. While on its face this appears to be a medical decision, the research team, led by Oklahoma State University political scientist Matt Motta, sought to discover the relevance of group acceptance.
Social psychology really matters
Previous research has found that anti-vaxxers conform to in-group norms by expressing skepticism against anyone that questions their autonomy and rejecting broader public health recommendations by out-group experts. Such resistance, they write, may result from identity protective cognition, that is, the avoidance of anything that challenges deeply held beliefs.
For this study, the team relied on the following two psychological theories:
- Social identity theory (SIT). Coined by social psychologists Henri Tajfel and John Turner, this theory predicts in-group behavior is due to perceived status differences as well as the legitimacy and stability of such differences. SIT predominantly focuses on the psychological motivations for group membership and attachment.
- Self-categorization theory (SCT). This social psychology theory is focused on the cognitive motivations for defining group membership. Also developed by John Turner, SCT investigates the consequences of perceiving people in group terms.
SIT argues that categorization can lead to identification depending on how personally each individual takes the content matter. In this case, when vaccine resistance provides self-esteem and personal meaning, then heightened group identification will merge with their identity. SCT steps in to cement the individual relationship to the content (vaccine resistance) and provides context for the group to flourish.
"Upon socially identifying with a group, people come to understand group membership in comparison to those not in the group, or to those in opposing groups. People then tend to favor members of the in-group and imbue positive characteristics onto them, whereas members of the out-group are viewed with suspicion and oftentimes are seen negatively."Rally goers protest vaccines and the current administration during the "World Wide Rally for Freedom", an anti-mask and anti-vaccine rally, at the State House in Concord, New Hampshire, May 15, 2021.
Rally goers protest vaccines and the current administration during the "World Wide Rally for Freedom", an anti-mask and anti-vaccine rally, at the State House in Concord, New Hampshire, May 15, 2021. Photo by Joseph Prezioso / AFP via Getty Images
Following the herd, but not the immune kind
This mindset has profound social implications. While the U.S. has a goal to vaccinate 70 percent of American adults by July 4, public health officials are still concerned that another wave of COVID-19 will hit this summer due to millions of Americans refusing the jab.
While social psychology theories cannot explain all 22 percent of vaccine-hesitant individuals, the researchers are confident that they provide meaning for at least part of that population. People in this group often refuse to have their children vaccinated and also are more likely to express interest in "intuitive" thinking around health and medicine rather than accept empirical data offered by professionals.
Surveying over 5,000 Americans, the team discovered that full-blown anti-vaxxers (8 percent) were more likely to identify as a group than vaccine-hesitant respondents (14 percent). They also found that such respondents were more likely to engage in conspiratorial thinking. They write:
"People who embrace folk theories about medicine — i.e., inter-generationally transmitted beliefs about medicine that are widely held, but factually inaccurate — have been shown to be more likely to think about the world in conspiratorial ways, and less knowledgeable about basic scientific facts."
The power of tribalism
The team notes that this is more than a barrier to herd immunity. Individuals that score high on the AVSID scale are more likely to share misinformation about vaccines and disrupt important public health communications. The challenge of combating such trends, they note, is especially difficult when anti-vaxx identity is bound to the group.
Reaching the 14 percent of vaccine-hesitant individuals will prove easier than trying to convince the 8 percent of anti-vaxxers. As long as their identity is tied with the group, changing their minds will be nearly impossible.
Stay in touch with Derek on Twitter. His most recent book is "Hero's Dose: The Case For Psychedelics in Ritual and Therapy."
Contact-tracing apps can be a useful tool for public health, but they have considerable false positive and false negative rates.
- The COVID-19 pandemic witnessed the widespread adoption of contact-tracing apps.
- Research shows that these apps aren't as accurate as we might think.
- There are several physical and biological factors than can interfere with the accuracy of contact-tracing apps.
The following is an excerpt adapted from People Count: Contact-Tracing Apps and Public Health.
To stop an epidemic, public health authorities focus on lowering R0. Even for a given illness, this number varies tremendously according to the protective measures a society takes (wearing masks, practicing social distancing, and other measures). In early March 2020, COVID-19's R0 was just below 4 in New York State. Once the state instituted a shelter-in-place order and virtually no one was on the streets, R0 dropped below 1. It continued to hover quite close to 1 for the summer and into the fall, even after the state came back to life and began to open bars and restaurants.
By helping to keep spread in check, could contact-tracing apps have lowered R0 enough to allow people to safely work, participate in social life, and be with their families? Lacking a real-life human experiment to answer this question, epidemiologists turn to models; these are in turn based on existing data. In the case of COVID-19, some of the best data comes from a citizen-science app developed by the BBC to accompany its 2018 documentary on the Spanish flu, Contagion! The BBC4 Pandemic. Participants agreed to provide a twenty-four-hour snapshot of their locations and self-reported contacts, which epidemiologists then used to model how a similar epidemic would spread in twenty-first-century Britain.
The BBC database ultimately included the locations and contacts of 36,000 people. It showed their movements over the course of a day, including how many people they saw at work, at school, and elsewhere. The data allowed researchers to develop a model that could simulate various interventions at the population level, from isolation, testing, contact tracing, and social distancing to app usage.
The resulting model showed that if 90 percent of ill people self-isolated and their household quarantined upon learning of their infection, 35 percent of cases would have already spread the disease to another person. If 90 percent of the contacts of those infected also isolated upon learning of the previous person's infection, only 26 percent of cases would have infected someone else. The contact tracers, in other words, bought time. By having potentially infected people isolate, contact tracing prevented new rounds of infections. In another iteration, the researchers added apps to the mix and assumed that 53 percent of the population would use them. By notifying people of potential infections faster than a contact tracer could, the apps lowered the infection rate further, so that only 23 percent of cases infected another person. At that high adoption rate, the disease doesn't disappear, but it also doesn't cause a pandemic.
Models, of course, are only as good as the assumptions on which they're based. The idea that 53 percent of any given population would voluntarily use a contact-tracing app and that anyone receiving an exposure notification would isolate is doubtful, at best. Still, because the apps appear to help lower R0, governments and public health officials have jumped to add them to the mix of public health tools available to combat COVID-19's spread.
Signal strength varied depending on whether a person carried their phone in their back pocket, their front pocket, or in a backpack or handbag. The signal strength varied by device model, by the shape of the room, even by the construction materials.
Given the high stakes involved, we need to look at how apps are deployed in real life. How well do apps actually work? Are they more effective than more traditional, and less invasive, public health tools? Can they usefully supplement manual contact-tracing efforts? COVID-19 has hit low-income and Black, Latinx, and indigenous communities particularly hard. The possibility of public health organizations embracing contact-tracing apps as a line of defense against epidemics raises new questions about equity and the balance of individual privacy and public safety. Will contact-tracing apps exacerbate inequities already present in society?
A robust public debate about the implications of deploying what is effectively a public surveillance system didn't occur; instead, many officials deployed these apps essentially overnight. We need that debate, but first we must look at efficacy. If the apps aren't efficacious, then there is no reason to consider them further.
The many problems with contact-tracing apps
Following advice from the WHO, most public health agencies have promoted the idea that "social distancing" is the safest way to guard against exposure to the coronavirus. For the CDC, the magic number is six feet (in metric-based nations, it's usually two meters). Stay at least that far away from other people, so the theory goes, and you're safe. Since the BLE [Bluetooth Low Energy] technology on which contact-tracing apps run depends on proximity, engineers hoped that phone-to-phone contacts could serve as a reasonable proxy for risky exposures. In practice, this has turned out to be not entirely straightforward.
In theory, the strength of the BLE signal that a phone receives from another indicates the distance of the device emitting it. To test the accuracy of this assumption, researchers at Germany's Fraunhofer-Gesellschaft simulated the experiences of people sitting on a train, waiting on line, being served by a waiter in a restaurant, and attending a cocktail party. Over 139 tests, the phones correctly determined time and distance exposure 70 percent of the time. This information seems encouraging, but the simulation took place in a test facility that lacked walls. The "train car" had no metal sides, the people waiting on line encountered no checkout counters or supermarket shelves, and neither the restaurant nor the cocktail party included walls or serving stations. This matters because radio waves often reflect off surfaces.
When researchers from the University of Dublin tried these tests in actual train cars, they obtained different results. Seven volunteers with phones running GAEN [(Google/Apple) Exposure Notification]-based apps distributed themselves around a train car and measured the signals their phones received over a fifteen-minute period. Radio waves are supposed to vary inversely according to the square of distance, so the researchers were surprised to find that the signals stayed constant at a distance of 1.5–2.5 meters and began to increase after that. Apparently, a flexible metal joint between train carriages concentrated the signal.
As they looked more closely at the results, the researchers found more surprises. Signal strength varied depending on whether a person carried their phone in their back pocket, their front pocket, or in a backpack or handbag. The signal strength varied by device model, by the shape of the room, even by the construction materials. Depending on the construction material, BLE signals can indicate that people are near each other when they are actually in neighboring apartments.
Epidemiologists understand that the six-foot measure is somewhat arbitrary; engineers know that BLE signals don't measure distances precisely. If the rest of us come to use these systems, we also need to understand their limitations.
Apps don't account for real-life circumstances
Credit: Jeff J Mitchell via Getty Images
Measurement imprecision isn't the only problem for contact-tracing and exposure-notification apps. The apps are not built to record the real-life circumstances that affect the likelihood of transmission in any given case. If Alyssa is six feet away from Ben in a small room for fifteen minutes, there's likely risk of exposure. But if Alyssa is four feet from Ben, outside, and wearing a mask, she's likely to be safe. Large gatherings of people indoors carry risks of spread, while similarly sized groups of masked people outdoors are less dangerous. Apps can't distinguish between these situations. Nor do apps know if the person standing eight feet away from you is belting out a song — dangerous if they're infected — or just standing quietly.
The apps are also ignorant of a room's ventilation, an important factor in how the virus spreads. When an infected person breathes — or speaks, sings, coughs, or sneezes — they emit viral particles packaged in a mixture of mucus, saliva, and water. The smallest of these, aerosols, evaporate as they travel, losing some of their potency. The bigger ones, droplets, typically fall to the ground within three feet. Sometimes, though, air flow, particularly air conditioning, can push these along, putting people at further distances at risk of infection. This is apparently what happened in a restaurant in Guangzhou, China, when two people sitting well beyond the six-foot measure — and on different sides of the ill person — were infected. One was at a table more than a dozen feet away.
Biology also confuses apps. A review of published reports indicates that as many as 30–40 percent of people never show symptoms. While these studies are not based on random samples, a single study based on a large random sample of Icelanders showed a similar result: a startling 43 percent of participants tested positive without showing symptoms. Even if one assumes that only 30 percent of cases are asymptomatic — a not unreasonable assumption — then epidemiologists believe that 7 percent of transmission will arise from asymptomatic cases. This matters for the apps' effectiveness. Asymptomatic people are less likely to get tested than those who are sick — and if there's no test, there's no trigger for exposure notifications.
Contact-tracing and exposure-notification apps nevertheless do have value. They pick up cases that people, including contact tracers, wouldn't. Aliyah might not remember a chance hallway encounter with Bobby, but her app will. And the app will be ready to notify Aliyah if Bobby's phone reports a positive COVID-19 test. Perhaps even more critically, Aliyah's app will register encounters with nearby strangers in the bar or theater lobby — as long as they are also using the app. If those strangers later test positive, Aliyah will learn she's been exposed. Without a phone app, she'd have little chance of discovering this.
False positives and false negatives
These technical and practical limitations of contact-tracing apps mean that they can produce both false positives and false negatives. (Note that these are false positives and false negatives of exposure, not false positive and false negatives of having COVID-19.) Virginia's website for the state's GAEN-based app, for example, warns that students in adjacent dorm rooms might receive exposure notifications of close contact while being in different rooms. When tested in August 2020, the UK exposure-notification app had a 45 percent false positive rate and 31 percent false negative rate.
These numbers sound bad, but the false positives aren't entirely "false" — most of them represented exposures at 2.5–4 meters away rather than 2 meters. Depending on the circumstances, a person might well have been exposed at 3 meters. In the case of false negatives, however, users received no notification whatsoever that they had been in the presence of someone infected with COVID-19.
The apps are not built to record the real-life circumstances that affect the likelihood of transmission in any given case.
Both types of inaccuracies present challenges for users and public health agencies — some more obvious than others. If Aliyah receives a false positive notification, she might quarantine unnecessarily, losing a paycheck. If she's following the rules, she should also urge her roommates and family members she's in close contact with to do so, causing more disruption. Alternatively, if this is the second time that the app warns Aliyah that she's been exposed without her developing any symptoms, she might just ignore the notification and disable the app.
False negatives place the public's health at risk. If Bobby was asymptomatic and never tested, Aliyah will not receive a notification even though she may have spent fifty minutes sitting six feet away from Bobby in a classroom. False negatives can also be produced by circumstance: from an air conditioner dispersing aerosols farther than expected or an infected singer who propels droplets farther than six feet.
Some communities are at higher risk for false positives than others. Many low-income people, for instance, hold jobs that bring them in constant contact with a stream of strangers (e.g., grocery store clerks, health care workers, workers in food service and production). For these workers, a small variation in the proximity measurement (say, nine feet instead of six) can multiply into a high risk of false positives from contact-tracing apps. What's more, many of these workers routinely wear protective gear or work behind barriers that reduce their risk from even four-foot interactions. Similarly, people who live in high-density housing situations, whether multifamily housing units or apartment complexes, are more likely to receive false positives than people who live in stand-alone suburban or rural houses.
Hourly workers living paycheck to paycheck can't afford to take time off unless it's absolutely necessary. A false positive keeps them from clocking in. Alyssa, in Singapore, or Amelie, in Switzerland, can each expect to receive financial support from the government if they isolate after an exposure notification. But in the United States, few low-income or gig workers receive paid time off, even for isolating during a pandemic. The privilege of staying at home is not evenly distributed. Workers who realize that the apps consistently generate false positives are less likely to use them voluntarily — or to heed them when they provide alerts.
False negatives, too, have a differential impact. White-collar workers who already work from home and who drive their own vehicles on necessary errands have fewer contacts than those who take public transportation to jobs that have been deemed "essential." The fewer contacts each of us has with other people, the less chance we have of spreading COVID-19. A false negative of exposure for someone who works outside the home and uses public transit carries greater risk of infecting others than the same false negative for someone who works at home and uses their own transportation.
Contact-tracing apps were supposed to resolve this problem, allowing people to emerge from lockdowns with the ability to interact with friends, family, and strangers. It's not clear that they will.
Adapted excerpt from People Count: Contact-Tracing Apps and Public Health by Susan Landau. Reprinted with Permission from The MIT PRESS. Copyright 2021.
Is working from home the ultimate liberation or the first step toward an even unhappier "new normal"?
- The Great Resignation is an idea proposed by Professor Anthony Klotz that predicts a large number of people leaving their jobs after the COVID pandemic ends and life returns to "normal."
- French philosopher Michel Foucault argued that by establishing what is and is not "normal," we are exerting a kind of power by making people behave a certain way.
- If working from home becomes the new normal, we must be careful that it doesn't give way to a new lifestyle that we hate even more than the office.
You wake up, you put on your work clothes, and you go to the office. You sit behind a desk, or in some designated space, and you work until the clock says it's over. This is what life is like for the vast majority of people. That is, until COVID came along. Then, everything changed.
Recently, an interesting idea has emerged called the "Great Resignation." This is a phenomenon that Professor Anthony Klotz of Texas A&M University has predicted will happen when people are asked, or told, to return to their offices. Klotz argues that, when we're all forced back into the old reality of the commute, a nine-to-five job, and cubicle life, there will be a "Great Resignation" among the workforce.
The argument is that in times of uncertainty and insecurity — like during a global pandemic — people behave conservatively. They'll stay put. But once things "normalize" again, we ought to expect employees to head for the exits.
But why? What has changed? Why has working from home made us so dissatisfied with our previously normal lives? Other than the comfort and convenience of working from home, one explanation might involve the concept of "normalization," a topic that fascinated French philosopher Michel Foucault.
The power of normal people
Foucault argued that we often spend an inordinate amount of time trying to be normal. We must dress the same way as everyone else. We must talk about the same things. We must work just like everyone else works. It's hugely important that things are normal. But, behind all of this, is a power dynamic that many of us are simply unaware of — and unconsciously unhappy about.
Someone, somewhere, must define what is "normal." It is then for the rest of us to bend over backward to fit into this narrow mold. To be powerful, then, is to say, "Do this, otherwise everyone will call you weird." Power is to hold the hoops everyone else must jump through. It's what Foucault describes as "normalizing power."
COVID was a wake-up call to the abnormality of modern work
Let's apply Focault's normalization concept to the modern workplace. Accepted wisdom had it that the best — and really, the only way — to work was in an office, usually downtown, far away from where we live. We were told this is where collaboration and creativity occur. Largely unchallenged, this "normal" functioned for decades, and we all obeyed.
We had to wake up at the crack of dawn to get ready for work. We had to travel in clogged and joyless commutes. We had to eat ready-packaged lunches behind our too-small desks. We had to sit through meetings in "good posture" ergonomic chairs that wouldn't be out of place in the Spanish Inquisition. Then we had to travel back home in yet another clogged and joyless commute. And we did this day after day after day.
Then COVID came along and revealed just how artificial, unnecessary, and abnormal it all is. It's as if someone ripped a blindfold off of society. We have laptops, wi-fi, and 5G (at least when people aren't burning the towers down). Many of us were just as productive — if not more so — than during the "normal" pre-COVID era. We don't need to be in an office. We don't need to waste countless hours of our lives sitting in traffic.
While the idea of a Great Resignation is quite appealing right now, we should be careful the "new normal" isn't so much worse.
Even better, people got to spend more time with their families, enjoy long and restful breaks, and have space to pursue their hobbies. In short, people like not going to an office. And, as Klotz argues, when companies see this dissatisfaction — this Great Resignation — they're going to ask some revolutionary questions, like, "Do you want to come back full time? Work remotely? In-office three days a week? Four days? One day?"
The silver lining to the COVID pandemic is that it has made us re-examine what "normal" is.
Beware the new normal
Of course, the idea of a nine-to-five office job was not established by some moustache-twirling villain just to satisfy his sadistic whims. It came about because people thought that was the most effective and productive way to operate.
People do need direct human contact, and it's often easier and more productive to speak to a colleague next to you or walk across an office to ask for some help. Remote-working software like Zoom is indeed convenient, but can a company honestly say that it's as efficient as working in an office?
What's more, there's a particularly pernicious sting in what Foucault argued. It's something that ought to slow any would-be Great Resignation. This is the idea that there likely will always be some kind of normal.
While COVID has revealed the office for the normalized power play that it is, what's to say what the next "normal" will be? Let's say that working from home becomes the new normal. Will we be expected to attend Zoom meetings at any hour of the day or answer text messages at midnight? Might cameras be used to monitor our every movement? Might software check that we're working at the right pace and in the right way?
While the idea of a Great Resignation is quite appealing right now, we should be careful the "new normal" isn't so much worse.