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In clearing our plates of meat, eggs and cheese (fat and protein), we ate more grains, pasta and starchy vegetables (carbohydrates). Over the past 50 years, we cut fat intake by 25 percent and increased carbohydrates by more than 30 percent, according to a new analysis of government data. Yet recent science has increasingly shown that a high-carb diet rich in sugar and refined grains increases the risk of obesity, diabetes and heart disease — much more so than a diet high in fat and cholesterol.

In 2013, government advice to reduce salt intake (which remains in the current report) was contradicted by an authoritative Institute of Medicine study. And several recent meta-analyses have cast serious doubt on whether saturated fats are linked to heart disease, as the dietary guidelines continue to assert.

Much of the epidemiological data underpinning the government’s dietary advice comes from studies run by Harvard’s school of public health. In 2011, directors of the National Institute of Statistical Sciences analyzed many of Harvard’s most important findings and found that they could not be reproduced in clinical trials.

Today, we are poised to make the same mistakes. The committee’s new report also advised eliminating “lean meat” from the list of recommended healthy foods, as well as cutting back on red and processed meats. Fewer protein choices will likely encourage Americans to eat even more carbs. It will also have policy implications: Meat could be limited in school lunches and other federal food programs.

It’s possible that a mostly meatless diet could be healthy for all Americans — but then again, it might not be. We simply do not know. There are no rigorous clinical trials on such a diet, and although epidemiological data exists for adult vegetarians, there is none for children.

We have to start looking more skeptically at epidemiological studies and rethinking nutrition policy from the ground up.

Until then, we would be wise to return to what worked better for previous generations: a diet that included fewer grains, less sugar and more animal foods like meat, full-fat dairy and eggs

SEIR models start to fall apart when you think about the underlying causes of the data. You need models that can allow for all possible states, and assess which ones matter for shaping the pandemic’s trajectory over time.

These techniques have enjoyed enormous success ever since they moved out of physics. They’ve been running your iPhone and nuclear power stations for a long time. In my field, neurobiology, we call the approach dynamic causal modelling (DCM). We can’t see brain states directly, but we can infer them given brain imaging data

Epidemiologists currently tackle the inference problem by number-crunching on a huge scale, making use of high-performance computers. Imagine you want to simulate an outbreak in Scotland. Using conventional approaches, this would take you a day or longer with today’s computing resources. And that’s just to simulate one model or hypothesis – one set of parameters and one set of starting conditions.

Using DCM, you can do the same thing in a minute. That allows you to score different hypotheses quickly and easily, and so to home in sooner on the best one.

This is like dark matter in the universe: we can’t see it, but we know it must be there to account for what we can see. Knowing it exists is useful for our preparations for any second wave, because it suggests that targeted testing of those at high risk of exposure to Covid-19 might be a better approach than non-selective testing of the whole population.

Our response as individuals – and as a society – becomes part of the epidemiological process, part of one big self-organising, self-monitoring system. That means it is possible to predict not only numbers of cases and deaths in the future, but also societal and institutional responses – and to attach precise dates to those predictions.

How well have your predictions been borne out in this first wave of infections?For London, we predicted that hospital admissions would peak on 5 April, deaths would peak five days later, and critical care unit occupancy would not exceed capacity – meaning the Nightingale hospitals would not be required. We also predicted that improvements would be seen in the capital by 8 May that might allow social distancing measures to be relaxed – which they were in the prime minister’s announcement on 10 May. To date our predictions have been accurate to within a day or two, so there is a predictive validity to our models that the conventional ones lack.

What do your models say about the risk of a second wave?The models support the idea that what happens in the next few weeks is not going to have a great impact in terms of triggering a rebound – because the population is protected to some extent by immunity acquired during the first wave. The real worry is that a second wave could erupt some months down the line when that immunity wears off.

the important message is that we have a window of opportunity now, to get test-and-trace protocols in place ahead of that putative second wave. If these are implemented coherently, we could potentially defer that wave beyond a time horizon where treatments or a vaccine become available, in a way that we weren’t able to before the first one.

We’ve been comparing the UK and Germany to try to explain the comparatively low fatality rates in Germany. The answers are sometimes counterintuitive. For example, it looks as if the low German fatality rate is not due to their superior testing capacity, but rather to the fact that the average German is less likely to get infected and die than the average Brit. Why? There are various possible explanations, but one that looks increasingly likely is that Germany has more immunological “dark matter” – people who are impervious to infection, perhaps because they are geographically isolated or have some kind of natural resistance

Any other advantages?Yes. With conventional SEIR models, interventions and surveillance are something you add to the model – tweaks or perturbations – so that you can see their effect on morbidity and mortality. But with a generative model these things are built into the model itself, along with everything else that matters.

Are generative models the future of disease modelling?That’s a question for the epidemiologists – they’re the experts. But I would be very surprised if at least some part of the epidemiological community didn’t become more committed to this approach in future, given the impact that Feynman’s ideas have had in so many other disciplines.

“Many other studies have shown an association between obesity and an increased risk of dementia. These findings demonstrate the complexity of research into risk factors for dementia and it is important to note that BMI is a crude measure – not necessarily an indicator of health. It’s also not clear whether other factors could have affected these results.”

The best protection against dementia, he added, is “eating a healthy, balanced diet, exercising regularly, not smoking, and keeping blood pressure in check”.

The C.D.C. changed its tune in April, advising all Americans above the age of 2 to wear masks to slow the spread of the coronavirus. Michael Basso, a senior health scientist at the agency who had been pushing internally to recommend masks, told me Ms. Tufekci’s public criticism of the agency was the “tipping point.”

Ms. Tufekci, a 40-something who speaks a mile a minute with a light Turkish accent, has none of the trappings of the celebrity academic or the professional pundit. But long before she became perhaps the only good amateur epidemiologist, she had quietly made a habit of being right on the big things.

In 2011, she went against the current to say the case for Twitter as a driver of broad social movements had been oversimplified. In 2012, she warned news media outlets that their coverage of school shootings could inspire more. In 2013, she argued that Facebook could fuel ethnic cleansing. In 2017, she warned that YouTube’s recommendation algorithm could be used as a tool of radicalization.

And when it came to the pandemic, she sounded the alarm early while also fighting to keep parks and beaches open.

“I’ve just been struck by how right she has been,” said Julia Marcus, an infectious disease epidemiologist at Harvard Medical School.

She told me she chalks up her habits of mind in part to a childhood she wouldn’t wish on anyone.

Mr. Goff was enthusing about the campaign’s ability to send different messages to individual voters based on the digital data it had gathered about them. Ms. Tufekci quickly objected to the practice, saying that microtargeting would more likely be used to sow division.

An international point of view she picked up while bouncing as a child between Turkey and Belgium and then working in the United States.

Knowledge that spans subject areas and academic disciplines, which she happened onto as a computer programmer who got into sociology.

A habit of complex, systems-based thinking, which led her to a tough critique in The Atlantic of America’s news media in the run-up to the pandemic

it began, she says, with growing up in an unhappy home in Istanbul. She said her alcoholic mother was liable to toss her into the street in the early hours of the morning. She found some solace in science fiction — Ursula K. Le Guin was a favorite — and in the optimistic, early internet.

Perhaps because of a kind of egalitarian nerd ideology that has served her well, she never sought to meet the rebels’ charismatic leader, known as Subcomandante Marcos.

“I have a thing that fame and charisma screws with your head,” she said. “I’ve made an enormous effort throughout my life to preserve my thinking.”

While many American thinkers were wide-eyed about the revolutionary potential of social media, she developed a more complex view, one she expressed when she found herself sitting to the left of Teddy Goff, the digital director for President Obama’s re-election campaign, at a South by Southwest panel in Austin in 2012

“A bunch of things came together, which I’m happy I survived,” she said, sitting outside a brick house she rents for $2,300 a month in Chapel Hill, N.C., where she is raising her 11-year-old son as a single parent. “But the way they came together was not super happy, when it was happening.”

“At a time when everybody was being stupidly optimistic about the potential of the internet, she didn’t buy the hype,” he told me. “She was very prescient in seeing that there would be a deeper rot to the role of data-driven politics in our world.”

Many tech journalists, entranced by the internet-fueled movements sweeping the globe, were slow to spot the ways they might fail, or how social media could be used against them. Ms. Tufekci, though, had “seen movement after movement falter because of a lack of organizational depth and experience, of tools or culture for collective decision making, and strategic, long-term action,” she wrote in her 2017 book, “Twitter and Tear Gas.”

One of the things that makes Ms. Tufekci stand out in this gloomy moment is her lack of irony or world-weariness. She is not a prophet of doom, having hung on to an early-internet optimism

Ms. Tufekci has taught epidemiology as a way to introduce her students to globalization and to make a point about human nature: Politicians and the news media often expect looting and crime when disaster strikes, as they did when Hurricane Katrina hit New Orleans in 2005. But the reality on the ground has more to do with communal acts of generosity and kindness, she believes.

Her March column on masks was among the most influential The Times has published, although — or perhaps because —  it lacked the political edge that brings wide attention to an opinion piece.

“The real question is not whether Zuck is doing what I like or not,” she said. “The real question is why he’s getting to decide what hate speech is.”

She also suggested that we may get it wrong when we focus on individuals — on chief executives, on social media activists like her. The probable answer to a media environment that amplifies false reports and hate speech, she believes, is the return of functional governments, along with the birth of a new framework, however imperfect, that will hold the digital platforms responsible for what they host.

There are more than a dozen of these risk perception factors, (see Ch. 3 of “How Risky Is It, Really? Why Our Fears Don’t Match the Facts", available online free at)

Because our perceptions rely as much as or more on feelings than simply on the facts, we sometimes get risk wrong. We’re more afraid of some risks than we need to be (child abduction, vaccines), and not as afraid of some as we ought to be (climate change, particulate air pollution), and that “Perception Gap” can be a risk in and of itself

We must understand that instinct and intellect are interwoven components of a single system that helps us perceive the world and make our judgments and choices, a system that worked fine when the risks we faced were simpler but which can make dangerous mistakes as we try to figure out some of the more complex dangers posed in our modern world.

What we can do to avoid the dangers that arise when our fears don’t match the facts—the most rational thing to do—is, first, to recognize that our risk perceptions can never be purely objectively perfectly 'rational', and that our subjective perceptions are prone to potentially dangerous mistakes.

Then we can begin to apply all the details we've discovered of how our risk perception system works, and use that knowledge and self-awareness to make wiser, more informed, healthier choices

He and his colleagues could not replicate 47 of 53 landmark papers about cancer. Some of the results could not be reproduced even with the help of the original scientists working in their own labs.

Paradoxically the hottest fields, with the most people pursuing the same questions, are most prone to error, Dr. Ioannidis argued. If one of five competing labs is alone in finding an effect, that result is the one likely to be published. But there is a four in five chance that it is wrong. Papers reporting negative conclusions are more easily ignored.

The effect is amplified by competition for a shrinking pool of grant money and also by the design of so many experiments — with small sample sizes (cells in a lab dish or people in an epidemiological pool) and weak standards for what passes as statistically significant. That makes it all the easier to fool oneself.

The fear that much published research is tainted has led to proposals to make replication easier by providing more detailed documentation, including videos of difficult procedures.

A call for the establishment of independent agencies to replicate experiments has led to a backlash, a fear that perfectly good results will be thrown out.

Scientists talk about “tacit knowledge,” the years of mastery it can take to perform a technique. The image they convey is of an experiment as unique as a Rembrandt.

Embedded in the tacit knowledge may be barely perceptible tweaks and jostles — ways of unknowingly smuggling one’s expectations into the results, like a message coaxed from a Ouija board.

Exciting new results will continue to appear. But as the quarry becomes more elusive, the trophies are bound to be fewer and fewer.

Expectations surrounding education have spun out of control. On top of a seven-hour school day, our kids march through hours of nightly homework, daily sports practices and band rehearsals, and weekend-consuming assignments and tournaments. Each activity is seen as a step on the ladder to a top college, an enviable job and a successful life. Children living in poverty who aspire to college face the same daunting admissions arms race, as well as the burden of competing for scholarships, with less support than their privileged peers.

Yet instead of empowering them to thrive, this drive for success is eroding children’s health and undermining their potential. Modern education is actually making them sick.

Nearly one in three teenagers told the American Psychological Association that stress drove them to sadness or depression — and their single biggest source of stress was school. According to the Centers for Disease Control and Prevention, a vast majority of American teenagers get at least two hours less sleep each night than recommended — and research shows the more homework they do, the fewer hours they sleep. At the university level, 94 percent of college counseling directors in a survey from last year said they were seeing rising numbers of students with severe psychological problems.

At the other end of the age spectrum, doctors increasingly see children in early elementary school suffering from migraine headaches and ulcers. Many physicians see a clear connection to performance pressure.

chosen to start making a change. Teachers are re-examining their homework demands, in some cases reviving the school district’s forgotten homework guideline — no more than 20 minutes per class per night, and none on weekends. In fact, research supports limits on homework. Students have started a task force to promote healthy habits and balanced schedules.

A growing body of medical evidence suggests that long-term childhood stress is linked not only with a higher risk of adult depression and anxiety, but with poor physical health outcomes, as well.

Paradoxically, the pressure cooker is hurting, not helping, our kids’ prospects for success. Many college students struggle with critical thinking, a fact that hasn’t escaped their professors, only 14 percent of whom believe that their students are prepared for college work, according to a 2015 report.

At Irvington, it’s too early to gauge the impact of new reforms, but educators see promising signs. Calls to school counselors to help students having emotional episodes in class have dropped from routine to nearly nonexistent. The A.P. class failure rate dropped by half. Irvington students continue to be accepted at respected colleges.

“For too long,” he writes in the preface, “the scientific community has been overly focused on the dark side of our biological heritage: our capacity for tribalism, violence, selfishness and cruelty. The bright side has been denied the attention it deserves.”

He told me that few people realize that he listened to those students for more than two hours, and that they hadn’t intercepted and surprised him: He went out to meet them, knowing how angry they were. “I felt that I had to model the principles that I believed — which is that I am committed to free and open expression,” he said. “I hardly could cower in my house.”

The book is a hefty, dazzlingly erudite synthesis of history, philosophy, anthropology, genetics, sociology, economics, epidemiology, statistics and more. It uses everything from shipwrecks to the primatologist Jane Goodall to make its pro-kindnes

Although he stayed calm — which he attributes to years of training in karate and its premium on self-control — he was rattled, deeply, by the encounter. He soon took his first sabbatical ever. He read books about equanimity in the face of injustice. “I did not want to become a different person,” he said. “I certainly did not want to become embittered.”

He rues America’s intense polarization, which perhaps makes this “an odd time for me to advance the view that there is more that unites us than divides us.”

His reasoning, oversimplified, is this: Complex societies are possible and durable only when people are emotionally invested in, and help, one another; we’d be living in smaller units and more solitary fashions if we weren’t equipped for such collaboration; and human thriving within these societies guarantees future generations suited to them.

Yes, there are hideous wars and horrid leaders. But if that were the sum of us, how to explain all the peace and progress? Christakis urges a wide angle and the long view

“To accept this belief that human beings are evil or violent or selfish or overly tribal is a kind of moral and intellectual laziness,” he told me. It also excuses that destructiveness. “The way to repair our torn social fabric is to say: Wait a minute, that’s not quite right.

“Blueprint,” he said, is sociodicy: It tries “to vindicate society despites its failures.”

He came down hard on nutrition in a pull-no-punches 2013 British Medical Journal editorial titled, “Implausible results in human nutrition research,” in which he noted, “Almost every single nutrient imaginable has peer reviewed publications associating it with almost any outcome.”

Ioannidis told me that sussing out the connection between diet and health — nutritional epidemiology — is enormously challenging, and “the tools that we’re throwing at the problem are not commensurate with the complexity and difficulty of the problem.” The biggest of those tools is observational research, in which we collect data on what people eat, and track what happens to them.

He lists plant-based foods — fruit, veg, whole grains, legumes — but acknowledges that we don’t understand enough to prescribe specific combinations or numbers of servings.

funding bias isn’t the only kind. “Fanatical opinions abound in nutrition,” Ioannidis wrote in 2013, and those have bias power too.

“Definitive solutions won’t come from another million observational papers or small randomized trials,” reads the subtitle of Ioannidis’s paper. His is a burn-down-the-house ethos.

When it comes to actual dietary recommendations, the disagreement is stark. “Ioannidis and others say we have no clue, the science is so bad that we don’t know anything,” Hu told me. “I think that’s completely bogus. We know a lot about the basic elements of a healthy diet.”

Give tens of thousands of people that FFQ, and you end up with a ginormous repository of possible correlations. You can zero in on a vitamin, macronutrient or food, and go to town. But not only are you starting with flawed data, you’ve got a zillion possible confounding variables — dietary, demographic, socioeconomic. I’ve heard statisticians call it “noise mining,” and Ioannidis is equally skeptical. “With this type of data, you can get any result you want,” he said. “You can align it to your beliefs.”

Big differences in what people eat track with other differences. Heavy plant-eaters are different from, say, heavy meat-eaters in all kinds of ways (income, education, physical activity, BMI). Red meat consumption correlates with increased risk of dying in an accident as much as dying from heart disease. The amount of faith we put in observational studies is a judgment call.

I find myself in Ioannidis’s camp. What have we learned, unequivocally enough to build a consensus in the nutrition community, about how diet affects health? Well, trans-fats are bad.

Over and over, large population studies get sliced and diced, and it’s all but impossible to figure out what’s signal and what’s noise. Researchers try to do that with controlled trials to test the connections, but those have issues too. They’re expensive, so they’re usually small and short-term. People have trouble sticking to the diet being studied. And scientists are generally looking for what they call “surrogate endpoints,” like increased cholesterol rather than death from heart disease, since it’s impractical to keep a trial going until people die.

, what do we do? Hu and Ioannidis actually have similar suggestions. For starters, they both think we should be looking at dietary patterns rather than single foods or nutrients. They also both want to look across the data sets. Ioannidis emphasizes transparency. He wants to open data to the world and analyze all the data sets in the same way to see if “any signals survive.” Hu is more cautious (partly to safeguard confidentiality

I have a suggestion. Let’s give up on evidence-based eating. It’s given us nothing but trouble and strife. Our tools can’t find any but the most obvious links between food and health, and we’ve found those already.

Instead, let’s acknowledge the uncertainty and eat to hedge against what we don’t know

We’ve got two excellent hedges: variety and foods with nutrients intact (which describes such diets as the Mediterranean, touted by researchers). If you severely limit your foods (vegan, keto), you might miss out on something. Ditto if you eat foods with little nutritional value (sugar, refined grains). Oh, and pay attention to the two things we can say with certainty: Keep your weight down, and exercise.

I used to say I could tell you everything important about diet in 60 seconds. Over the years, my spiel got shorter and shorter as truisms fell by the wayside, and my confidence waned in a field where we know less, rather than more, over time. I’m down to five seconds now: Eat a wide variety of foods with their nutrients intact, keep your weight down and get some exercise.

But these latest shifts challenge key infection control assumptions that go back a century, putting a lot of what went wrong last year in context

They may also signal one of the most important advancements in public health during this pandemic.

If the importance of aerosol transmission had been accepted early, we would have been told from the beginning that it was much safer outdoors, where these small particles disperse more easily, as long as you avoid close, prolonged contact with others.

We would have tried to make sure indoor spaces were well ventilated, with air filtered as necessary.

Instead of blanket rules on gatherings, we would have targeted conditions that can produce superspreading events: people in poorly ventilated indoor spaces, especially if engaged over time in activities that increase aerosol production, like shouting and singing

We would have started using masks more quickly, and we would have paid more attention to their fit, too. And we would have been less obsessed with cleaning surfaces.

The implications of this were illustrated when I visited New York City in late April — my first trip there in more than a year.

A giant digital billboard greeted me at Times Square, with the message “Protecting yourself and others from Covid-19. Guidance from the World Health Organization.”

That billboard neglected the clearest epidemiological pattern of this pandemic: The vast majority of transmission has been indoors, sometimes beyond a range of three or even six feet. The superspreading events that play a major role in driving the pandemic occur overwhelmingly, if not exclusively, indoors.

The billboard had not a word about ventilation, nothing about opening windows or moving activities outdoors, where transmission has been rare and usually only during prolonged and close contact. (Ireland recently reported 0.1 percent of Covid-19 cases were traced to outdoor transmission.)

Mary-Louise McLaws, an epidemiologist at the University of New South Wales in Sydney, Australia, and a member of the W.H.O. committees that craft infection prevention and control guidance, wanted all this examined but knew the stakes made it harder to overcome the resistance. She told The Times last year, “If we started revisiting airflow, we would have to be prepared to change a lot of what we do.” She said it was a very good idea, but she added, “It will cause an enormous shudder through the infection control society.”

In contrast, if the aerosols had been considered a major form of transmission, in addition to distancing and masks, advice would have centered on ventilation and airflow, as well as time spent indoors. Small particles can accumulate in enclosed spaces, since they can remain suspended in the air and travel along air currents. This means that indoors, three or even six feet, while helpful, is not completely protective, especially over time.

Meanwhile, many countries allowed their indoor workplaces to open but with inadequate aerosol protections. There was no attention to ventilation, installing air filters as necessary or even opening windows when possible, more to having people just distancing three or six feet, sometimes not requiring masks beyond that distance, or spending money on hard plastic barriers, which may be useless at best

To see this misunderstanding in action, look at what’s still happening throughout the world. In India, where hospitals have run out of supplemental oxygen and people are dying in the streets, money is being spent on fleets of drones to spray anti-coronavirus disinfectant in outdoor spaces. Parks, beaches and outdoor areas keep getting closed around the world. This year and last, organizers canceled outdoor events for the National Cherry Blossom Festival in Washington, D.C. Cambodian customs officials advised spraying disinfectant outside vehicles imported from India. The examples are many.

clear evidence doesn’t easily overturn tradition or overcome entrenched feelings and egos. John Snow, often credited as the first scientific epidemiologist, showed that a contaminated well was responsible for a 1854 London cholera epidemic by removing the suspected pump’s handle and documenting how the cases plummeted afterward. Many other scientists and officials wouldn’t believe him for 12 years, when the link to a water source showed up again and became harder to deny.

Along the way to modern public health shaped largely by the fight over germs, a theory of transmission promoted by the influential public health figure Charles Chapin took hold

Dr. Chapin asserted in the early 1900s that respiratory diseases were most likely spread at close range by people touching bodily fluids or ejecting respiratory droplets, and did not allow for the possibility that such close-range infection could occur by inhaling small floating particles others emitted

In a contemporary example of this attitude, the initial public health report on the Mount Vernon choir case said that it may have been caused by people “sitting close to one another, sharing snacks and stacking chairs at the end of the practice,” even though almost 90 percent of the people there developed symptoms of Covid-19

It was in this context in early 2020 that the W.H.O. and the C.D.C. asserted that SARS-CoV-2 was transmitted primarily via these heavier, short-range droplets, and provided guidance accordingly

Amid the growing evidence, in July, hundreds of scientists signed an open letter urging the public health agencies, especially the W.H.O., to address airborne transmission of the coronavirus.

Last October, the C.D.C. published updated guidance acknowledging airborne transmission, but as a secondary route under some circumstances, until it acknowledged airborne transmission as crucial on Friday. And the W.H.O. kept inching forward in its public statements, most recently a week ago.

Linsey Marr, a professor of engineering at Virginia Tech who made important contributions to our understanding of airborne virus transmission before the pandemic, pointed to two key scientific errors — rooted in a lot of history — that explain the resistance, and also opened a fascinating sociological window into how science can get it wrong and why.

Dr. Marr said that if you inhale a particle from the air, it’s an aerosol.

biomechanically, she said, nasal transmission faces obstacles, since nostrils point downward and the physics of particles that large makes it difficult for them to move up the nose. And in lab measurements, people emit far more of the easier-to-inhale aerosols than the droplets, she said, and even the smallest particles can be virus laden, sometimes more so than the larger ones, seemingly because of how and where they are produced in the respiratory tract.

Second, she said, proximity is conducive to transmission of aerosols as well because aerosols are more concentrated near the person emitting them. In a twist of history, modern scientists have been acting like those who equated stinky air with disease, by equating close contact, a measure of distance, only with the larger droplets, a mechanism of transmission, without examination.

Since aerosols also infect at close range, measures to prevent droplet transmission — masks and distancing — can help dampen transmission for airborne diseases as well. However, this oversight led medical people to circularly assume that if such measures worked at all, droplets must have played a big role in their transmission.

Another dynamic we’ve seen is something that is not unheard-of in the history of science: setting a higher standard of proof for theories that challenge conventional wisdom than for those that support it.

Another key problem is that, understandably, we find it harder to walk things back. It is easier to keep adding exceptions and justifications to a belief than to admit that a challenger has a better explanation.

The ancients believed that all celestial objects revolved around the earth in circular orbits. When it became clear that the observed behavior of the celestial objects did not fit this assumption, those astronomers produced ever-more-complex charts by adding epicycles — intersecting arcs and circles — to fit the heavens to their beliefs.

He was also concerned that belief in airborne transmission, which he associated with miasma theories, would make people feel helpless and drop their guard against contact transmission. This was a mistake that would haunt infection control for the next century and more.

So much of what we have done throughout the pandemic — the excessive hygiene theater and the failure to integrate ventilation and filters into our basic advice — has greatly hampered our response.

Some of it, like the way we underused or even shut down outdoor space, isn’t that different from the 19th-century Londoners who flushed the source of their foul air into the Thames and made the cholera epidemic worse.

Righting this ship cannot be a quiet process — updating a web page here, saying the right thing there. The proclamations that we now know are wrong were so persistent and so loud for so long.

the progress we’ve made might lead to an overhaul in our understanding of many other transmissible respiratory diseases that take a terrible toll around the world each year and could easily cause other pandemics.

So big proclamations require probably even bigger proclamations to correct, or the information void, unnecessary fears and misinformation will persist, damaging the W.H.O. now and in the future.

I’ve seen our paper used in India to try to reason through aerosol transmission and the necessary mitigations. I’ve heard of people in India closing their windows after hearing that the virus is airborne, likely because they were not being told how to respond

The W.H.O. needs to address these fears and concerns, treating it as a matter of profound change, so other public health agencies and governments, as well as ordinary people, can better adjust.

It needs to begin a campaign proportional to the importance of all this, announcing, “We’ve learned more, and here’s what’s changed, and here’s how we can make sure everyone understands how important this is.” That’s what credible leadership looks like. Otherwise, if a web page is updated in the forest without the requisite fanfare, how will it matter?

China rejected demands for an international investigation after the Trump administration blamed Beijing for the virus’s spread, which plunged the global economy into its deepest slump since the 1930s.

The coronavirus’s exact origin may never be traced because viruses change quickly, Woolhouse said

pinning down an outbreak’s animal reservoir is typically an exhaustive endeavor that takes years of research including taking animal samples, genetic analysis and epidemiological studies.

he incident “raises the question if the Chinese authorities were trying to interfere,”

Beijing retaliated by blocking imports of Australian beef, wine and other goods.

A year after the virus was first detected in Wuhan, the city is now bustling, with few signs that it was once the epicenter of the outbreak in China. But some residents say they’re still eager to learn about its origin.

covering closely related viruses might help explain how the disease first jumped from animals and clarify what preventive measures are needed to avoid future epidemics.

“Now is not the time to blame anyone,” Shih said. “We shouldn’t say, it’s your fault

"This is a serious matter and we have to maintain our practices even as many people are relaxing restrictions and the economy is opening back up," Reed said in a phone interview.

Social distancing and masks "really work," she said.

Most states across the country can ease some restrictions safely, but projects spikes in areas that have broadly reopened early, according to the model put together by a team at the Children's Hospital of Philadelphia and the University of Pennsylvania.

"While our models show that rising temperatures and humidity levels are having an impact on reducing the spread of Covid-19, those hot, humid days of summer are not going to eliminate the threat of virus resurgence," said PolicyLab's Dr. Gregory Tasian, senior scholar in the Center for Clinical Epidemiology and Biostatistics at the University of Pennsylvania's Perelman School of Medicine.

Jefferson County, where Birmingham is located, had 1,453 confirmed cases as of Thursday, according to the state's health department data -- which is a jump of 35% from the 1,075 cases reported two weeks ago. The county has had 85 deaths attributed to Covid-19.

As of May 11, restaurants and bars were allowed to serve customers on site, gyms and athletic facilities, hair and nail salons and barber shops were allowed to open. Groups of any size are allowed, as long as the 6 feet distance guidance is followed.

"Our findings are consistent with the notion that policies designed to improve the livelihoods of individuals with less education, who are more likely to work at lower wages and at higher risk for adverse mental health outcomes, can reduce the suicide risk in this group," the study authors said in a statement. "Our findings also suggest that the potential protective effects of a higher minimum wage are more important during times of high unemployment."

one of the leading modelers, Gabriela Gomes, suggested the entire area of research was being effectively blackballed out of fear it might encourage a relaxation of pandemic vigilance. “This is the very sad reason for the absence of more optimistic projections on the development of this pandemic in the scientific literature,” she wrote on Twitter. “Our analysis suggests that herd-immunity thresholds are being achieved despite strict social-distancing measures.”

Gomes suggested, herd immunity could happen with as little as one quarter of the population of a community exposed — or perhaps just 20 percent. “We just keep running the models, and it keeps coming back at less than 20 percent,” she told Hamblin. “It’s very striking.” Such findings, if they held up, would be very instructive, as Hamblin writes: “It would mean, for instance, that at 25 percent antibody prevalence, New York City could continue its careful reopening without fear of another major surge in cases.”

But for those hoping that 25 percent represents a true ceiling for pandemic spread in a given community, well, it almost certainly does not, considering that recent serological surveys have shown that perhaps 93 percent of the population of Iquitos, Peru, has contracted the disease; as have more than half of those living in Indian slums; and as many as 68 percent in particular neighborhoods of New York City

overshoot of that scale would seem unlikely if the “true” threshold were as low as 20 or 25 percent.

But, of course, that threshold may not be the same in all places, across all populations, and is surely affected, to some degree, by the social behavior taken to protect against the spread of the disease.

we probably err when we conceive of group immunity in simplistically binary terms. While herd immunity is a technical term referring to a particular threshold at which point the disease can no longer spread, some amount of community protection against that spread begins almost as soon as the first people are exposed, with each case reducing the number of unexposed and vulnerable potential cases in the community by one

you would not expect a disease to spread in a purely exponential way until the point of herd immunity, at which time the spread would suddenly stop. Instead, you would expect that growth to slow as more people in the community were exposed to the disease, with most of them emerging relatively quickly with some immune response. Add to that the effects of even modest, commonplace protections — intuitive social distancing, some amount of mask-wearing — and you could expect to get an infection curve that tapers off well shy of 60 percent exposure.

Looking at the data, we see that transmissions in many severely impacted states began to slow down in July, despite limited interventions. This is especially notable in states like Arizona, Florida, and Texas. While we believe that changes in human behavior and changes in policy (such as mask mandates and closing of bars/nightclubs) certainly contributed to the decrease in transmission, it seems unlikely that these were the primary drivers behind the decrease. We believe that many regions obtained a certain degree of temporary herd immunity after reaching 10-35 percent prevalence under the current conditions. We call this 10-35 percent threshold the effective herd immunity threshold.

Indeed, that is more or less what was recently found by Youyang Gu, to date the best modeler of pandemic spread in the U.S

he cautioned again that he did not mean to imply that the natural herd-immunity level was as low as 10 percent, or even 35 percent. Instead, he suggested it was a plateau determined in part by better collective understanding of the disease and what precautions to take

Gu estimates national prevalence as just below 20 percent (i.e., right in the middle of his range of effective herd immunity), it still counts, I think, as encouraging — even if people in hard-hit communities won’t truly breathe a sigh of relief until vaccines arrive.

If you can get real protection starting at 35 percent, it means that even a mediocre vaccine, administered much more haphazardly to a population with some meaningful share of vaccination skeptics, could still achieve community protection pretty quickly. And that is really significant — making both the total lack of national coordination on rollout and the likely “vaccine wars” much less consequential.

At least 20 percent of the public, and perhaps 50 percent, had some preexisting, cross-protective T-cell response to SARS-CoV-2, according to one much-discussed recent paper. An earlier paper had put the figure at between 40 and 60 percent. And a third had found an even higher prevalence: 81 percent.

The T-cell story is similarly encouraging in its big-picture implications without being necessarily paradigm-changing

These numbers suggest their own heterogeneity — that different populations, with different demographics, would likely exhibit different levels of cross-reactive T-cell immune response

The most optimistic interpretation of the data was given to me by Francois Balloux, a somewhat contrarian disease geneticist and the director of the University College of London’s Genetics Institute

According to him, a cross-reactive T-cell response wouldn’t prevent infection, but would probably mean a faster immune response, a shorter period of infection, and a “massively” reduced risk of severe illness — meaning, he guessed, that somewhere between a third and three-quarters of the population carried into the epidemic significant protection against its scariest outcomes

the distribution of this T-cell response could explain at least some, and perhaps quite a lot, of COVID-19’s age skew when it comes to disease severity and mortality, since the young are the most exposed to other coronaviruses, and the protection tapers as you get older and spend less time in environments, like schools, where these viruses spread so promiscuously.

Balloux told me he believed it was also possible that the heterogeneous distribution of T-cell protection also explains some amount of the apparent decline in disease severity over time within countries on different pandemic timelines — a phenomenon that is more conventionally attributed to infection spreading more among the young, better treatment, and more effective protection of the most vulnerable (especially the old).

Going back to Youyang Gu’s analysis, what he calls the “implied infection fatality rate” — essentially an estimated ratio based on his modeling of untested cases — has fallen for the country as a whole from about one percent in March to about 0.8 percent in mid-April, 0.6 percent in May, and down to about 0.25 percent today.

even as we have seemed to reach a second peak of coronavirus deaths, the rate of death from COVID-19 infection has continued to decline — total deaths have gone up, but much less than the number of cases

In other words, at the population level, the lethality of the disease in America has fallen by about three-quarters since its peak. This is, despite everything that is genuinely horrible about the pandemic and the American response to it, rather fantastic.

there may be some possible “mortality displacement,” whereby the most severe cases show up first, in the most susceptible people, leaving behind a relatively protected population whose experience overall would be more mild, and that T-cell response may play a significant role in determining that susceptibility.

That, again, is Balloux’s interpretation — the most expansive assessment of the T-cell data offered to me

The most conservative assessment came from Sarah Fortune, the chair of Harvard’s Department of Immunology

Fortune cautioned not to assume that cross-protection was playing a significant role in determining severity of illness in a given patient. Those with such a T-cell response, she told me, would likely see a faster onset of robust response, yes, but that may or may not yield a shorter period of infection and viral shedding

Most of the scientists, doctors, epidemiologists, and immunologists I spoke to fell between those two poles, suggesting the T-cell cross-immunity findings were significant without necessarily being determinative — that they may help explain some of the shape of pandemic spread through particular populations, but only some of the dynamics of that spread.

he told me he believed, in the absence of that data, that T-cell cross-immunity from exposure to previous coronaviruses “might explain different disease severity in different people,” and “could certainly be part of the explanation for the age skew, especially for why the very young fare so well.”

the headline finding was quite clear and explicitly stated: that preexisting T-cell response came primarily via the variety of T-cells called CD4 T-cells, and that this dynamic was consistent with the hypothesis that the mechanism was inherited from previous exposure to a few different “common cold” coronaviruses

“This potential preexisting cross-reactive T-cell immunity to SARS-CoV-2 has broad implications,” the authors wrote, “as it could explain aspects of differential COVID-19 clinical outcomes, influence epidemiological models of herd immunity, or affect the performance of COVID-19 candidate vaccines.”

“This is at present highly speculative,” they cautioned.

Perhaps the best example of what Twitter offers now—and what we stand to gain or lose from its demise—is illustrated by the path charted by public-health officials, epidemiologists, doctors, and nurses over the past three years.

They offered guidance that a flailing government response was too slow to provide, and helped cobble together an epidemiological picture of infections and case counts. At a moment when people were terrified and looking for any information at all, Twitter seemed to offer a steady stream of knowledgeable, diligent experts.

But Twitter does another thing quite well, and that’s crushing users with the pressures of algorithmic rewards and all of the risks, exposure, and toxicity that come with virality

t imagining a world without it can feel impossible. What do our politics look like without the strange feedback loop of a Twitter-addled political press and a class of lawmakers that seems to govern more via shitposting than by legislation

What happens if the media lose what the writer Max Read recently described as a “way of representing reality, and locating yourself within it”? The answer is probably messy.

here’s the worry that, absent a distributed central nervous system like Twitter, “the collective worldview of the ‘media’ would instead be over-shaped, from the top down, by the experiences and biases of wealthy publishers, careerist editors, self-loathing journalists, and canny operators operating in relatively closed social and professional circles.”

Twitter is, by some standards, a niche platform, far smaller than Facebook or Instagram or TikTok. The internet will evolve or mutate around a need for it. I am aware that all of us who can’t quit the site will simply move on when we have to.

Many of the past decade’s most polarizing and influential figures—people such as Donald Trump and Musk himself, who captured attention, accumulated power, and fractured parts of our public consciousness—were also the ones who were thought to be “good” at using the website.

the effects of Twitter’s chief innovation—its character limit—on our understanding of language, nuance, and even truth.

“These days, it seems like we are having languages imposed on us,” he said. “The fact that you have a social media that tells you how many characters to use, this is language imposition. You have to wonder about the agenda there. Why does anyone want to restrict the full range of my language? What’s the game there?

in McLuhanian fashion, the constraints and the architecture change not only what messages we receive but how we choose to respond. Often that choice is to behave like the platform itself: We are quicker to respond and more aggressive than we might be elsewhere, with a mindset toward engagement and visibility

it’s easy to argue that we stand to gain something essential and human if we lose Twitter. But there is plenty about Twitter that is also essential and human.

No other tool has connected me to the world—to random bits of news, knowledge, absurdist humor, activism, and expertise, and to scores of real personal interactions—like Twitter has

What makes evaluating a life beyond Twitter so hard is that everything that makes the service truly special is also what makes it interminable and toxic.

the worst experience you can have on the platform is to “win” and go viral. Generally, it seems that the more successful a person is at using Twitter, the more they refer to it as a hellsite.