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The Scientific Conversation about COVID-19 is Happening in Popular Media

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The Scientific Conversation about COVID-19 is Happening in Popular Media

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Research topics in college aren't usually a matter of life and death. Even when they are, e.g. climate change or the opiod crisis, they still seem abstract.

Most of us aren't moved to act or learn unless we are personally involved (a friend overdosed, for example). A pandemic grabs everyone's attention, for the most part.

The scholarship/ research about how to treat COVID-19  becomes compelling to everyone.  We all become "citizen journalists," a role that requires highly developed "information literacy" skills. (That's why journalists earn degrees!)  We all generally wants answers in seconds, not even minutes, but true literacy and deep knowledge requires long-form reading and also critical thinking. We may be shocked to learn that scientists and doctors disagree with each other, or even with the WHO or CDC. Who is right? Also, has science become partisan?

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Last week, FiveThirtyEight's Nate Silver teased his latest project on Covid-19 to his 3.2 million Twitter followers: "Working on something where you can model the number of detected cases of a disease as a function of the number of actual cases and various assumptions about how/how many tests are conducted."

While his attempt at Twitter epidemiology was criticized mostly by academic scientists, it was hardly offensive enough to warrant anything more than an eye roll. For all of the tweet's irony-Silver built his reputation by calling out the naivete of bad interpretations of polling data-his attempt was harmless, exploratory, and he didn't make any claim to being an expert.

C. Brandon Ogbunu (@big_data_kane) is an assistant professor at Brown University who specializes in computational biology and genetics.

That Silver appears to know his place as an outsider on the topic is more than can be said for thousands of people who have rewired their brands, credentials, industries, and research interests to become Covid-19 experts overnight. The growth curve of "experts" mirrors the exponential increase in Covid-19 cases, creating a multiverse of thousands of projections, models, ideas, recommendations, therapies, solutions, and scenarios. Much of it is ripe with dangerous misinformation and threatens to worsen the pandemic.

There are many reasons for the big bang of Covid-19 "expertise." Those wading into the pandemic forum include people who study related topics or have expertise in some scientific domain. Pleuni Pennings, an evolutionary computational biologist and assistant professor at San Francisco State University, says many academics are initially responding to demands from personal and professional circles: "Our students and friends and family members are coming to us for advice. For example, even though I work on HIV, early on, my non-science network came with many practical questions such as, 'Do you think I can still see my grandchildren?'"

For others, many of whom are not professional scientists, the motivation to participate comes from classical do-gooderism: People with resources, which include both skill sets and time, want to help in some way. And while the road to hell can be paved with good intentions, a world of overnight epidemiologists comprising only highly skilled, magnanimous polymaths would be tolerable (if still exhausting): It would be nice to know that all of these new experts were at least smart and caring.

Unfortunately, the majority of Covid-19 carpetbaggers are at the very least opportunists, and sometimes nefarious propagators of misinformation. They seize on the opportunity to use the topic that everyone is talking about to make a name for themselves, which is beneficial in whatever realm they operate in.

One story of a suspected Covid-19 opportunist involves Aaron Ginn, a Silicon Valley technologist whose five minutes of fame arrived in March after he wrote a contrarian essay proposing that evidence didn't support the "hysteria" over the consequences of the pandemic, that the problem might be sorta bad, but not really, really bad.

Ginn flaunted some unusual credentials in support of his authority on the matter: a talent for making products go viral. "I'm quite experienced at understanding virality, how things grow, and data," he wrote. The logic here would only be amusing if it weren't potentially harmful.

Ginn's story became a lightning rod for the expertise debate: After his piece was panned by critics (including one especially damning refutation by Carl Bergstrom, coauthor of the upcoming Calling Bullshit), it was removed by Medium, a decision that was criticized by The Wall Street Journal as an act of censure. The editorial is off base, of course, as Ginn's missteps were not simply a matter of a preference; poorly vetted ideas and misinformation are often propagated and promoted in digital spaces, which can influence behavior.

Read all of our coronavirus coverage here.

While Silicon Valley has been roundly criticized by the scientific community over this style of aggressive parachuting into Covid-19, tech bros aren't the only ones guilty of opportunism. In fact, some of the worst offenders are academic scientists with strong (even stellar) reputations in their own fields who suffer from a serious case of covid FOMO.

One of the most high-profile examples of a well-regarded academic jumping the Covid-19 shark would be the rise and fall of Stephen Quake, armchair epidemiologist. Notably, Quake is a professor at Stanford and a superstar biophysicist by every professional metric. He doubles as copresident of the Chan-Zuckerberg Biohub, a $600 million collaborative research initiative, a role that amplified the influence of, and backlash to, his March 22 Medium essay, "How Bad Is the Worst-Case Coronavirus Scenario?"

Based on the popular model developed by Neil Ferguson and colleagues, Quake compared the 500,000 possible Covid-19 cases to other major causes of death and seemed to suggest that, because a comparable number of Americans die of cancer, the fuss around the number of potential Covid-19 deaths is unwarranted. Quake's argument reads like a Thanos-inspired "All Lives Matter" manifesto: People die a lot anyway, and this unusual way of dying will be solved in a short while, so what's the big deal? Quake's attempt at an "I bet they've never heard this" provocation was only successful in telling us that he is either a bad person or didn't think very clearly about the problem (maybe both).

Most charitably, we might attribute misfirings like Ginn's and Quake's to outsized egos, which compels them to question whether studying Covid-19 is really more challenging than studying the market or polymers (or whatever complicated idea that they've built a reputation on). Their egos may conclude that people in the field of epidemiology can't possibly be any smarter than they are, and another flawed Medium article is born.

Elaine Nsoesie, a computational epidemiologist and assistant professor at the Boston University School of Public Health, says that people who "have not studied infectious diseases will make assumptions and inferences that are incorrect. People who already have a large following on Twitter, for example, can spread misinformation that could impact the control of the Covid-19 pandemic."

Naïve assumptions can create misinformation. This is where the ego-FOMO opportunism becomes unethical-not only are your naive ideas wrong, they are especially bad because they might be affecting the behavior and wellness of others.

The problems with Covid-19 profiteers-whether they are scientists or not-are many. And in a Covid-19 world already saturated with ideas, it can be difficult for anyone to tell real from fake. Who should we trust? And who, exactly, is an expert?

Nsoesie says she's "part of several infectious disease modeling communities, so I know people who have been working in this space for a while. Those are the people I tend to pay attention to. If I see someone I don't know, I look at the person's previous research if they are academics. If they are medical professionals, then I look at their area of expertise."

Pennings adds, "We all just need to be careful to indicate how certain we are and not pretend like we know everything. And if your opinion goes against guidelines from agencies like CDC, I think you need to be extra careful with sharing that opinion and using your 'credentials' to be critical."

That neither Nsoesie and Pennings, both well-respected academic scientists, require specific credentials in the people they listen to distinguishes them from many of their academic colleagues. Reflexive criticism of the Covid-19 outsider-expert from professional scientists too often sounds like classical gatekeeping.

Samuel Scarpino, a mathematical biologist and assistant professor at Northeastern University's Network Science Institute, is strongly critical of opinions that are too steeped in credentialism. "A lot of the frustration from academic scientists is rooted in the problematic notion that just because someone has worked on a topic for a long time that they should determine who should have an opinion on it."

Despite the fact that Scarpino has contributed to several visible and influential studies on Covid-19 epidemiology, he suggests that "there's not a single card-carrying epidemiologist who would call me an epidemiologist."

"Being an authority on Covid-19," he adds, "should not be about whether someone is an epidemiologist or not. It should be about whether you're trying to be thoughtful and are communicating effectively."

Gatekeeping is antithetical to the growth of science. Many tools of modern biology, for example, came from insights developed by computer scientists, engineers, and mathematicians. Science works best as a creative, collaborative enterprise.

Nonetheless, lines between the hacks and the wonks can appear thin. But there is a soft algorithm we can use to help us think about who to take seriously and who to ignore.

- Transparency about motivations and methods. A true Covid-19 expert provides open qualifiers for their interpretations, makes their assumptions very clear (sometimes before they've told you what they actually think), provides proper disclaimers and almost never makes rigid predictions. If they've never worked on a disease before, they should share this fact, and explain their motivations. (They do not, however, need to apologize for being interested and wanting to help.) Relatedly, a true Covid-19 expert who analyzes data and creates an algorithm or model of any kind will make their data openly and freely available, so that it can be verified and reproduced. If it is challenging to find the data used by an expert, or hard to run a model that they've produced, then the work (and its author) should be ignored. For a positive example, Harvard mathematical biologist Alison Hill created a tool for Covid-19 modeling that included full access to the available code, notably under a Creative Commons Attribution-ShareAlike 4.0 International ( CC BY-SA 4.0) License, which permits the sharing, copying, editing, and remixing of all material (even for commercial purposes).

- Transparency in contributions. A true Covid-19 expert will give proper credit to everyone that contributed to whatever model or set of findings that they've come up with. Covid-19 expertise is not Gregor Mendel, alone in a garden, counting plants en route to discovering the fundamentals of genetics. Understanding an epidemic requires the participation of talented people, often from across the globe. Anyone who proposes an idea that is supposed to be useful or original but doesn't openly and clearly acknowledge the contributions of others should be automatically ignored. This includes properly citing relevant work and data sources.

- Participation in the scientific ecosystem. A Covid-19 expert should make an attempt to participate in the existing ecosystem of science, where findings are produced and shared in the form of a scientific "preprint" or manuscript. That is, findings and results should not live only on a company website or personal blog. They should be cultivated into a scientific format. While the formalism of scientific publishing is far from perfect, it contains features that are essential for honest discourse: It allows us to observe whether an author has read and is citing the proper literature; it allows the author to communicate their methods and interpretations with rigor, in detail; and it allows the community a formal means of being able to criticize, reference and improve upon the work.

I label the three rules above a "soft" algorithm because there is no strict method for ensuring that the good ideas are filtering through. But it provides a start and is effective at identifying some of the red flags of hacks and trolls.

In some ways Covid-19 is not unlike any other paradigm where people feel compelled to become experts overnight. Many fields-ranging from basketball to criminal justice-are mired in culture wars where people who study algorithms and analytics clash with an old guard that relies on domain expertise. And the proliferation of opinions-good and bad-is, in some ways, the price of a democracy of ideas. The alternatives resemble religious authoritarianism, where knowledge is based on the incorrigible opinion of individuals, a system guaranteed to be bad at solving epidemics.

In the end, the challenge of identifying an expert is much like the science of understanding epidemics: tough to achieve with absolute certainty. But in a hyperconnected world, where words and ideas have weight, being able to identify the imposters can save lives.

WIRED Opinion publishes articles by outside contributors representing a wide range of viewpoints. Read more opinions here. Submit an op-ed at opinion@wired.com.

More From WIRED on Covid-19

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Don't Be Fooled by Covid-19 Carpetbaggers

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Last week, FiveThirtyEight's Nate Silver teased his latest project on Covid-19 to his 3.2 million Twitter followers: "Working on something where you can model the number of detected cases of a disease as a function of the number of actual cases and various assumptions about how/how many tests are conducted."

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The scientific paper-the actual form of it-was one of the enabling inventions of modernity. Before it was developed in the 1600s, results were communicated privately in letters, ephemerally in lectures, or all at once in books. There was no public forum for incremental advances. By making room for reports of single experiments or minor technical advances, journals made the chaos of science accretive. Scientists from that point forward became like the social insects: They made their progress steadily, as a buzzing mass.

The earliest papers were in some ways more readable than papers are today. They were less specialized, more direct, shorter, and far less formal. Calculus had only just been invented. Entire data sets could fit in a table on a single page. What little "computation" contributed to the results was done by hand and could be verified in the same way.

The more sophisticated science becomes, the harder it is to communicate results. Papers today are longer than ever and full of jargon and symbols. They depend on chains of computer programs that generate data, and clean up data, and plot data, and run statistical models on data. These programs tend to be both so sloppily written and so central to the results that it's contributed to a replication crisis, or put another way, a failure of the paper to perform its most basic task: to report what you've actually discovered, clearly enough that someone else can discover it for themselves.

Perhaps the paper itself is to blame. Scientific methods evolve now at the speed of software; the skill most in demand among physicists, biologists, chemists, geologists, even anthropologists and research psychologists, is facility with programming languages and "data science" packages. And yet the basic means of communicating scientific results hasn't changed for 400 years. Papers may be posted online, but they're still text and pictures on a page.

What would you get if you designed the scientific paper from scratch today? A little while ago I spoke to Bret Victor, a researcher who worked at Apple on early user-interface prototypes for the iPad and now runs his own lab in Oakland, California, that studies the future of computing. Victor has long been convinced that scientists haven't yet taken full advantage of the computer. "It's not that different than looking at the printing press, and the evolution of the book," he said. After Gutenberg, the printing press was mostly used to mimic the calligraphy in bibles. It took nearly 100 years of technical and conceptual improvements to invent the modern book. "There was this entire period where they had the new technology of printing, but they were just using it to emulate the old media."

Victor gestured at what might be possible when he redesigned a journal article by Duncan Watts and Steven Strogatz, "Collective dynamics of 'small-world' networks." He chose it both because it's one of the most highly cited papers in all of science and because it's a model of clear exposition. (Strogatz is best known for writing the beloved "Elements of Math" column for The New York Times.)

The Watts-Strogatz paper described its key findings the way most papers do, with text, pictures, and mathematical symbols. And like most papers, these findings were still hard to swallow, despite the lucid prose. The hardest parts were the ones that described procedures or algorithms, because these required the reader to "play computer" in their head, as Victor put it, that is, to strain to maintain a fragile mental picture of what was happening with each step of the algorithm.

Victor's redesign interleaved the explanatory text with little interactive diagrams that illustrated each step. In his version, you could see the algorithm at work on an example. You could even control it yourself.

Bret Victor

Strogatz admired Victor's design. He later told me that it was a shame that in mathematics it's been a tradition for hundreds of years to make papers as formal and austere as possible, often suppressing the very visual aids that mathematicians use to make their discoveries.

Strogatz studies nonlinear dynamics and chaos, systems that get into sync or self-organize: fireflies flashing, metronomes ticking, heart cells firing electrical impulses. The key is that these systems go through cycles, which Strogatz visualizes as dots running around circles: When a dot comes back to the place where it started-that's a firefly flashing or a heart cell firing. "For about 25 years now I've been making little computer animations of dots running around circles, with colors indicating their frequency," he said. "The red are the slow guys, the purple are the fast guys ... I have these colored dots swirling around on my computer. I do this all day long," he said. "I can see patterns much more readily in colored dots running, moving on the screen than I can in looking at 500 simultaneous time series. I don't see stuff very well like that. Because it's not what it really looks like ... What I'm studying is something dynamic. So the representation should be dynamic."

Software is a dynamic medium; paper isn't. When you think in those terms it does seem strange that research like Strogatz's, the study of dynamical systems, is so often being shared on paper, without the benefit of his little swirling dots-because it's the swirling dots that helped him to see what he saw, and that might help the reader see it too.

This is, of course, the whole problem of scientific communication in a nutshell: Scientific results today are as often as not found with the help of computers. That's because the ideas are complex, dynamic, hard to grab ahold of in your mind's eye. And yet by far the most popular tool we have for communicating these results is the PDF-literally a simulation of a piece of paper. Maybe we can do better.

Stephen Wolfram published his first scientific paper when he was 15. He had published 10 when he finished his undergraduate career, and by the time he was 20, in 1980, he'd finished his Ph.D. in particle physics from the California Institute of Technology. His secret weapon was his embrace of the computer at a time when most serious scientists thought computational work was beneath them. "By that point, I think I was the world's largest user of computer algebra," he said in a talk. "It was so neat, because I could just compute all this stuff so easily. I used to have fun putting incredibly ornate formulas in my physics papers."

As his research grew more ambitious, he found himself pushing existing software to its limit. He'd have to use half a dozen programming tools in the course of a single project. "A lot of my time was spent gluing all this stuff together," he said. "What I decided was that I should try to build a single system that would just do all the stuff I wanted to do-and that I could expect to keep growing forever." Instead of continuing as an academic, Wolfram decided to start a company, Wolfram Research, to build the perfect computing environment for scientists. A headline in the April 18, 1988, edition of Forbes pronounced: "Physics Whiz Goes Into Biz."

At the heart of Mathematica, as the company's flagship product became known, is a "notebook" where you type commands on one line and see the results on the next. Type "1/6 + 2/5" and it'll give you "17/30." Ask it to factor a polynomial and it will comply. Mathematica can do calculus, number theory, geometry, algebra. But it also has functions that can calculate how chemicals will react, or filter genomic data. It has in its knowledge base nearly every painting in Rembrandt's oeuvre and can give you a scatterplot of his color palette over time. It has a model of orbital mechanics built in and can tell you how far an F/A-18 Hornet will glide if its engines cut out at 32,000 feet. A Mathematica notebook is less a record of the user's calculations than a transcript of their conversation with a polymathic oracle. Wolfram calls carefully authored Mathematica notebooks "computational essays."

The notebook interface was the brainchild of Theodore Gray, who was inspired while working with an old Apple code editor. Where most programming environments either had you run code one line at a time, or all at once as a big blob, the Apple editor let you highlight any part of your code and run just that part. Gray brought the same basic concept to Mathematica, with help refining the design from none other than Steve Jobs. The notebook is designed to turn scientific programming into an interactive exercise, where individual commands were tweaked and rerun, perhaps dozens or hundreds of times, as the author learned from the results of their little computational experiments, and came to a more intimate understanding of their data.

What made Mathematica's notebook especially suited to the task was its ability to generate plots, pictures, and beautiful mathematical formulas, and to have this output respond dynamically to changes in the code. In Mathematica you can input a voice recording, run complex mathematical filters over the audio, and visualize the resulting sound wave; just by mousing through and adjusting parameters, you can warp the wave, discovering which filters work best by playing around. Mathematica's ability to fluidly handle so many different kinds of computation in a single, simple interface is the result, Gray says, of "literally man-centuries of work."

The vision driving that work, reiterated like gospel by Wolfram in his many lectures, blog posts, screencasts, and press releases, is not merely to make a good piece of software, but to create an inflection point in the enterprise of science itself. In the mid-1600s, Gottfried Leibniz devised a notation for integrals and derivatives (the familiar ∫ and dx/dt) that made difficult ideas in calculus almost mechanical. Leibniz developed the sense that a similar notation applied more broadly could create an "algebra of thought." Since then, logicians and linguists have lusted after a universal language that would eliminate ambiguity and turn complex problem-solving of all kinds into a kind of calculus.

Wolfram's career has been an ongoing effort to vacuum up the world's knowledge into Mathematica, and later, to make it accessible via Wolfram Alpha, the company's "computational knowledge engine" that powers many of Siri and Alexa's question-answering abilities. It is Wolfram's own attempt to create an Interlingua, a programming language equally understandable by humans and machines, an algebra of everything.

It is a characteristically grandiose ambition. In the 1990s, Wolfram would occasionally tease in public comments that at the same time he was building his company, he was quietly working on a revolutionary science project, years in the making. Anticipation built. And then, finally, the thing itself arrived: a gargantuan book, about as wide as a cinder block and nearly as heavy, with a title for the ages- A New Kind of Science.

It turned out to be a detailed study, carried out in Mathematica notebooks, of the surprisingly complex patterns generated by simple computational processes-called cellular automata-both for their own sake and as a way of understanding how simple rules can give rise to complex phenomena in nature, like a tornado or the pattern on a mollusk shell. These explorations, which Wolfram published without peer review, came bundled with reminders, every few pages, about how important they were.

The more of Wolfram you encounter, the more this seems to be his nature. The 1988 Forbes profile about him tried to get to the root of it: "In the words of Harry Woolf, the former director of the prestigious Institute for Advanced Study in [Princeton, New Jersey]-where Wolfram, at 23, was one of the youngest senior researchers ever-he has 'a cultivated difficulty of character added to an intrinsic sense of loneliness, isolation, and uniqueness.'"

When one of Wolfram's research assistants announced a significant mathematical discovery at a conference, which was a core part of A New Kind of Science, Wolfram threatened to sue the hosts if they published it. "You won't find any serious research group that would let a junior researcher tell what the senior researcher is doing," he said at the time. Wolfram's massive book was panned by academics for being derivative of other work and yet stingy with attribution. "He insinuates that he is largely responsible for basic ideas that have been central dogma in complex systems theory for 20 years," a fellow researcher told the Times Higher Education in 2002.

Wolfram's self-aggrandizement is especially vexing because it seems unnecessary. His achievements speak for themselves-if only he'd let them. Mathematica was a success almost as soon as it launched. Users were hungry for it; at universities, the program soon became as ubiquitous as Microsoft Word. Wolfram, in turn, used the steady revenue to hire more engineers and subject-matter experts, feeding more and more information to his insatiable program. Today Mathematica knows about the anatomy of a foot and the laws of physics; it knows about music, the taxonomy of coniferous trees, and the major battles of World War I. Wolfram himself helped teach the program an archaic Greek notation for numbers.

All of this knowledge is "computable": If you wanted, you could set "x" to be the location of the Battle of the Somme and "y" the daily precipitation, in 1916, within a 30-mile radius of that point, and use Mathematica to see whether World War I fighting was more or less deadly in the rain.

Courtesy of Stephen Wolfram Blog

"I've noticed an interesting trend," Wolfram wrote in a blog post. "Pick any field X, from archeology to zoology. There either is now a 'computational X' or there soon will be. And it's widely viewed as the future of the field." As practitioners in those fields become more literate with computation, Wolfram argues, they'll vastly expand the range of what's discoverable. The Mathematica notebook could be an accelerant for science because it could spawn a new kind of thinking. "The place where it really gets exciting," he says, "is where you have the same transition that happened in the 1600s when people started to be able to read math notation. It becomes a form of communication which has the incredibly important extra piece that you can actually run it, too."

The idea is that a "paper" of this sort would have all the dynamism Strogatz and Victor wanted-interactive diagrams interleaved within the text-with the added benefit that all the code generating those diagrams, and the data behind them, would be right there for the reader to see and play with. "Frankly, when you do something that is a nice clean Wolfram-language thing in a notebook, there's no bullshit there. It is what it is, it does what it does. You don't get to fudge your data," Wolfram says.

To write a paper in a Mathematica notebook is to reveal your results and methods at the same time; the published paper and the work that begot it. Which shouldn't just make it easier for readers to understand what you did-it should make it easier for them to replicate it (or not). With millions of scientists worldwide producing incremental contributions, the only way to have those contributions add up to something significant is if others can reliably build on them. "That's what having science presented as computational essays can achieve," Wolfram said.

Wolfram says that he's surprised the computational essay hasn't taken off. He remembers working with Elsevier, the scientific publishing giant, all the way back in the early '80s. "Elsevier hired me to do some consulting thing about 'What would the future of scientific publishing look like?'" This was before the Mathematica notebook, but he gave them a spiel along the same lines. "A few years ago I was talking to some of their upper management again. I realized in this meeting, oh my gosh, I said exactly the same things 35 years ago!"

I spoke to Theodore Gray, who has since left Wolfram Research to become a full-time writer. He said that his work on the notebook was in part motivated by the feeling, well formed already by the early 1990s, "that obviously all scientific communication, all technical papers that involve any sort of data or mathematics or modeling or graphs or plots or anything like that, obviously don't belong on paper. That was just completely obvious in, let's say, 1990," he said.

"It's been a source of ongoing bafflement and consternation for the past 29 years, that with the exception of a few people who get it, the community at large hasn't really adopted it," he said. "It's incalculable, literally ... how much is lost, and how much time is wasted, and how many results are misinterpreted or are misrepresented."

In early 2001, Fernando Pérez found himself in much the same position Wolfram had 20 years earlier: He was a young graduate student in physics running up against the limits of his tools. He'd been using a hodgepodge of systems, Mathematica among them, feeling as though every task required switching from one to the next. He remembered having six or seven different programming-language books on his desk. What he wanted was a unified environment for scientific computing.

But rather than going off and building a company, he found two far-flung scientists, a German oceanographer and a computer-science graduate student at Caltech, who had been thinking along the same lines. They'd all fallen in love with Python, an open-source, general-purpose programming language, and they'd all independently started building tools to make it work better for scientists: tools that made it easier to manage data sets and draw plots, and that encouraged a more exploratory programming style.

Pérez combined the three projects into one and took the reins. From the very beginning, the project, called IPython (the "I" for "Interactive"), was open-source: It wasn't just that the program was free, but that anyone could inspect its code and modify it, contributing their changes back to the common cause. It was a deliberate decision. "I was interested in both the ethical aspect of being able to share my work with others," Pérez told me-he came from Colombia, where access to proprietary software was harder to come by-"and there was also a more epistemological motivation." He thought that if science was to be an open enterprise, the tools that are used to do it should themselves be open. Commercial software whose source code you were legally prohibited from reading was "antithetical to the idea of science," where the very purpose is to open the black box of nature.

Hence the allure of Python. Out of the box, Python is a much less powerful language than the Wolfram Language that powers Mathematica. But where Mathematica gets its powers from an army of Wolfram Research programmers, Python's bare-bones core is supplemented by a massive library of extra features-for processing images, making music, building AIs, analyzing language, graphing data sets-built by a community of open-source contributors working for free. Python became a de facto standard for scientific computing because open-source developers like Pérez happened to build useful tools for it; and open-source developers have flocked to Python because it happens to be the de facto standard for scientific computing. Programming-language communities, like any social network, thrive-or die-on the strength of these feedback loops.

The idea for IPython's notebook interface came from Mathematica. Pérez admired the way that Mathematica notebooks encouraged an exploratory style. "You would sketch something out-because that's how you reason about a problem, that's how you understand a problem." Computational notebooks, he said, "bring that idea of live narrative out ... You can think through the process, and you're effectively using the computer, if you will, as a computational partner, and as a thinking partner."

Instead of building a specialized, stand-alone application, let alone spending man-centuries on it, the IPython team-Pérez was now joined by Brian Granger, a physics professor at California Polytechnic State University at San Luis Obispo; and Min Ragan-Kelley, a Ph.D. student at UC Berkeley working in computational physics-built their notebooks as simple web pages. The interface is missing Mathematica's Steve Jobsian polish, and its sophistication. But by latching itself to the web, IPython got what is essentially free labor: Any time Google, Apple, or a random programmer open-sourced a new plotting tool, or published better code for rendering math, the improvement would get rolled into IPython. "It has paid off handsomely," Pérez said.

The paper announcing the first confirmed detection of gravitational waves was published in the traditional way, as a PDF, but with a supplemental IPython notebook. The notebook walks through the work that generated every figure in the paper. Anyone who wants to can run the code for themselves, tweaking parts of it as they see fit, playing with the calculations to get a better handle on how each one works. At a certain point in the notebook, it gets to the part where the signal that generated the gravitational waves is processed into sound, and this you can play in your browser, hearing for yourself what the scientists heard first, the bloop of two black holes colliding.

"I think what they have is acceptance from the scientific community as a tool that is considered to be universal," Theodore Gray says of Pérez's group. "And that's the thing that Mathematica never really so far has achieved." There are now 1.3 million of these notebooks hosted publicly on Github. They're in use at Google, Bloomberg, and NASA; by musicians, teachers, and AI researchers; and in "almost every country on Earth."

At every turn, IPython chose the way that was more inclusive, to the point where it's no longer called "IPython": The project rebranded itself as "Jupyter" in 2014 to recognize the fact that it was no longer just for Python. The Jupyter notebook, as it's called, is like a Mathematica notebook but for any programming language. You can have a Python notebook, or a C notebook, or an R notebook, or Ruby, or Javascript, or Julia. Anyone can build support for their programming language in Jupyter. Today it supports more than 100 languages.

Theodore Gray, who developed the original Mathematica notebook interface, said that he once as an experiment tried to build support for other programming languages into it. "It never went anywhere," he told me. "The company had no interest in supporting this. And also because when you have to support a lot of different languages, you can't do it as deeply."

A 1997 essay by Eric S. Raymond titled "The Cathedral and the Bazaar," in some sense the founding document of the modern open-source movement, challenged the notion that complex software had to be built like a cathedral, "carefully crafted by individual wizards or small bands of mages working in splendid isolation." Raymond's experience as one of the stewards of the Linux kernel (a piece of open-source software that powers all of the world's 500 most powerful supercomputers, and the vast majority of mobile devices) taught him that the "great babbling bazaar of differing agendas and approaches" that defined open-source projects was actually a strength. "The fact that this bazaar style seemed to work, and work well, came as a distinct shock," he wrote. The essay was his attempt to reckon with why "the Linux world not only didn't fly apart in confusion but seemed to go from strength to strength at a speed barely imaginable to cathedral builders."

Mathematica was in development long before Raymond's formative experience with Linux, and has been in development long after it. It is the quintessential cathedral, and its builders are still skeptical of the bazaar. "There's always chaos," Gray said about open-source systems. "The number of moving parts is so vast, and several of them are under the control of different groups. There's no way you could ever pull it together into an integrated system in the same way as you can in a single commercial product with, you know, a single maniac in the middle."

The maniac is, of course, Stephen Wolfram. Gray pointed out that the trains ran on time under Mussolini. "It's a bad analogy," he said, but still, "I'm all in favor of there being a maniac in the middle." The Mathematica notebook is the more coherently designed, more polished product-in large part because every decision that went into building it emanated from the mind of a single, opinionated genius. "I see these Jupyter guys," Wolfram said to me, "they are about on a par with what we had in the early 1990s." They've taken shortcuts, he said. "We actually want to try and do it right."

But it's hard to sell the scientific community on a piece of commercial software. Even though Wolfram Research has given away a free Mathematica notebook viewer for years, and even though most major universities already have a site license that lets their students and faculty use Mathematica freely, it might be too much to ask publishers to abandon PDFs, an open format, for a proprietary product. "Right now if you make a Mathematica notebook and you try to send that to a journal," Gray says, "they're gonna complain: Well, we don't have Mathematica, this is an expensive product-give us something that's more of a standard."

It doesn't help that Wolfram-the man, and to some extent the company-touts Mathematica's superiority, its necessity, in a way that even Gray describes as being a bit like how CrossFit people won't shut up about CrossFit. This is, after all, the same Stephen Wolfram who titled a book about his own work on cellular automata A New Kind of Science. In his blog post about computational essays, he writes, "At the core of computational essays is the idea of expressing computational thoughts using the Wolfram Language."

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Which, who knows, might well be true-maybe computational notebooks will only take root if they're backed by a single super-language, or by a company with deep pockets and a vested interest in making them work. But it seems just as likely that the opposite is true. A federated effort, while more chaotic, might also be more robust-and the only way to win the trust of the scientific community.

Wolfram has a hard time seeing outside Wolfram, and perhaps that's the reason that the Mathematica notebook has remained relatively obscure while a rival system-derivative, yes, and simplistic, but open-looks to be taking over the world.

It'll be some time before computational notebooks replace PDFs in scientific journals, because that would mean changing the incentive structure of science itself. Until journals require scientists to submit notebooks, and until sharing your work and your data becomes the way to earn prestige, or funding, people will likely just keep doing what they're doing.

I spoke to a neuroscientist-turned-software-developer who contributed to Jupyter, who told me that the professor in charge of his former lab was originally an electrophysiologist-he actually measured neuronal activity with implanted electrodes. "This kind of data is basically so costly, so expensive, and so good," he said, that nobody would ever share it. "You collect one batch of data and you can mine it for the rest of your career."

"At this point, nobody in their sane mind challenges the fact that the praxis of scientific research is under major upheaval," Pérez, the creator of Jupyter, wrote in a blog post in 2013. As science becomes more about computation, the skills required to be a good scientist become increasingly attractive in industry. Universities lose their best people to start-ups, to Google and Microsoft. "I have seen many talented colleagues leave academia in frustration over the last decade," he wrote, "and I can't think of a single one who wasn't happier years later."

Pérez told me stories of scientists who sacrificed their academic careers to build software, because building software counted for so little in their field: The creator of matplotlib, probably the most widely used tool for generating plots in scientific papers, was a postdoc in neuroscience but had to leave academia for industry. The same thing happened to the creator of NumPy, a now-ubiquitous tool for numerical computing. Pérez himself said, "I did get straight-out blunt comments from many, many colleagues, and from senior people and mentors who said: Stop doing this, you're wasting your career, you're wasting your talent." Unabashedly, he said, they'd tell him to "go back to physics and mathematics and writing papers."

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Still, those who stay are making progress. Pérez himself recently got a faculty appointment in the stats department at Berkeley. The day after we spoke, he was slated to teach an upper-division data-science course, built entirely on Python and Jupyter notebooks. "The freshman version of that course had in the fall I think 1,200 students," he said. "It's been the fastest-growing course in the history of UC Berkeley. And it's all based on these open-source tools."

When you improve the praxis of science, the dream is that you'll improve its products, too. Leibniz's notation, by making it easier to do calculus, expanded the space of what it was possible to think. The grand scientific challenges of our day are as often as not computational puzzles: How to integrate billions of base pairs of genomic data, and 10 times that amount of proteomic data, and historical patient data, and the results of pharmacological screens into a coherent account of how somebody got sick and what to do to make them better? How to make actionable an endless stream of new temperature and precipitation data, and oceanographic and volcanic and seismic data? How to build, and make sense of, a neuron-by-neuron map of a thinking brain? Equipping scientists with computational notebooks, or some evolved form of them, might bring their minds to a level with problems now out of reach.

At one point, Pérez told me the name Jupyter honored Galileo, perhaps the first modern scientist. The Jupyter logo is an abstracted version of Galileo's original drawings of the moons of Jupiter. "Galileo couldn't go anywhere to buy a telescope," Pérez said. "He had to build his own."

James Somers is a former contributing editor at The Atlantic.

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The scientific paper-the actual form of it-was one of the enabling inventions of modernity. Before it was developed in the 1600s, results were communicated privately in letters, ephemerally in lectures, or all at once in books. There was no public forum for incremental advances.

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arl Bergstrom is uniquely suited to understanding the current moment. A professor of biology at the University of Washington, he has spent his career studying two seemingly disparate topics: emerging infectious diseases and networked misinformation. They merged into one the moment reports of a mysterious respiratory illness emerged from China in January.

The coronavirus touched off both a pandemic and an " infodemic " of hoaxes, conspiracy theories, honest misunderstandings and politicized scientific debates. Bergstrom has jumped into the fray, helping the public and the press navigate the world of epidemiological models, statistical uncertainty and the topic of his forthcoming book: bullshit.

The following interview has been edited for length and clarity.

You've been teaching a course and have co-written a book about the concept of bullshit. Explain what you mean by bullshit?

The formal definition that we use is "language, statistical figures, data, graphics and other forms of presentation that are intended to persuade by impressing and overwhelming a reader or listener with a blatant disregard for truth or logical coherence".

The idea with bullshit is that it's trying to appear authoritative and definitive in a way that's not about communicating accurately and informing a reader, but rather by overwhelming them, persuading them, impressing them. If that's done without any allegiance to truth, or accuracy, that becomes bullshit.

The idea with bullshit is that it's trying to appear authoritative and definitive in a way that's not about communicating accurately and informing a reader

We're all used to verbal bullshit. We're all used to campaign promises and weasel words, and we're pretty good at seeing through that because we've had a lot of practice. But as the world has become increasingly quantified and the currency of arguments has become statistics, facts and figures and models and such, we're increasingly confronted, even in the popular press, with numerical and statistical arguments. And this area's really ripe for bullshit, because people don't feel qualified to question information that's given to them in quantitative form.
Are there bullshit narratives about the coronavirus that you are concerned about right now?
What's happened with this pandemic that we're not accustomed to in the epidemiology community is that it's been really heavily politicized. Even when scientists are very well-intentioned and not trying to support any side of the narrative, when they do work and release a paper it gets picked up by actors with political agendas.

Whether it's talking about seroprevalence or estimating the chance that this is even going to come to the United States at all each study gets picked up and placed into this little political box and sort of used as a cudgel to beat the other side with.

So even when the material isn't being produced as bullshit, it's being picked up and used in the service of that by overstating its claims, by cherry-picking the information that's out there and so on. And I think that's kind of the biggest problem that we're facing.

One example [of intentional bullshit] might be this insistence for a while on graphing the number of cases on a per-capita basis, so that people could say the US response is so much better than the rest of the world because we have a slower rate of growth per capita. That was basically graphical malfeasance or bullshit. When a wildfire starts spreading, you're interested in how it's spreading now, not whether it's spreading in a 100-acre wood or millions of square miles of national forest.
Is there one big lesson that you think that the media should keep in mind as we communicate science to the public? What mistakes are we making?
I think the media has been adjusting really fast and doing really well. When I'm talking about how to avoid misinformation around this I'm constantly telling people to trust the professional fact-based media. Rather than looking for the latest rumor that's spreading across Facebook or Twitter so that you can have information up to the hour, recognize that it's much better to have solidly sourced, well-vetted information from yesterday.

Hyper-partisan media are making a huge mess of this, but that's on purpose. They've got a reason to promote hydroxychloroquine or whatever it is and just run with that. They're not even trying to be responsible.

Hyper-partisan media are making a huge mess of this, but that's on purpose

But one of the biggest things that people [in the media]could do to improve would be to recognize that scientific studies, especially in a fast-moving situation like this, are provisional. That's the nature of science. Anything can be corrected. There's no absolute truth there. Each model, each finding is just adding to a weight of evidence in one direction or another.
A lot of the reporting is focusing on models, and most of us probably don't have any basic training in how to read Are there mistakes that are made by people in the scientific community when it comes to communicating with the public? them or what kind of credence to put in them. What should we know? It requires a lot of discipline to argue really hard for something but also be scrupulously open about all of the weaknesses in your own argument.
The key thing, and this goes for scientists as well as non-scientists, is that people are not doing a very good job thinking about what the purpose of different models are, how the purposes of different models vary, and then what the scope of their value is. When these models get treated as if they're oracles, then people both over-rely on them and treat them too seriously - and then turn around and slam them too hard for not being perfect at everything.

We're trying to communicate as a scientific community in a new way, where people are posting their data in real time. But we weren't ready for the degree to which that stuff would be picked up and assigned meaning in this highly politically polarized environment. Work that might be fairly easy for researchers to contextualize in the field can be portrayed as something very, very different in the popular press.
It is a bit of a nightmare to put out data that is truthful, but also be aware that there are bad faith actors at the moment who might pounce on it and use it in a way you didn't intend.
The first Imperial College model in March was predicting 1.1 million to 2.2 million American deaths if the pandemic were not controlled. That's a really scary, dramatic story, and I still think that it's not unrealistic. That got promoted by one side of the partisan divide. Then Imperial came back and modeled a completely different scenario, where the disease was actually brought under control and suppressed in the US, and they released a subsequent model that said, 'If we do this, something like 50,000 deaths will occur.' That was picked up by the other side and used to try to discredit the Imperial College team entirely by saying, 'A couple of weeks ago they said a million now they're saying 50,000; they can't get anything right.' And the answer , of course, is that they were modeling two different scenarios.

We're also not doing enough of deliberately stressing the possible weaknesses of our interpretations. That varies enormously from researcher to researcher and team to team.
One area that has already become contentious and in some ways politicized is the serology surveys, which are supposed to show what percentage of the population has antibodies to the virus. What are some of the big picture contextual caveats and limitations that we should keep in mind as these surveys come out?
But it's more important than ever, right? A really good paper will lay out all the most persuasive evidence it can and then in the conclusion section or the discussion section say, 'OK, here are all the reasons that this could be wrong and here are the weaknesses.'

When you have something that's so directly policy relevant, and there's a lot of lives at stake, we're learning how to find the right balance.

There's a spectrum. You have outright bad faith actors - Russian propaganda picking up on things and bots spreading misinformation - and then you have someone like Georgia Governor Brian Kemp who I wouldn't calla bad faith actor. He's a misinformed actor.

There's so much that goes unsaid in science in terms of context and what findings mean that we don't usually write in papers. If someone does a mathematical forecasting model, you're usually not going to have a half-page discussion on the limitations of forecasting. We're used to writing for an audience of 50 people in the world, if we're lucky, who have backgrounds that are very similar to our own and have a huge set of shared assumptions and shared knowledge. And it works really well when you're writing on something that only 50 people in the world care about and all of them have comparable training, but it is a real mess when it becomes pressing, and I don't think any of us have figured out exactly what to do about that because we're also trying to work quickly and it's important to get this information out.

The seroprevalence in the US is a political issue, and so the first thing is to recognize that when anyone is reporting on that stuff, there's a political context to it. It may even be that some of the research is being done with an implicitly political context, depending on who the funders are or what the orientations and biases of some of the researchers.
Now that we've all had a crash course in models and serosurveys, what are the other areas of science where it makes sense for the public to start getting educated on the terms of the debate?

It may even be that some of the research is being done with an implicitly political context, depending on who the funders are or what the orientations and biases of some of the researchers

On the scientific side, I think there's really two things to think about. The first one is the issue of selection bias. You're trying to draw a conclusion about one population by sampling from a subset of that population and you want to know how close to random your subset is with respect to the thing you're trying to measure. The Santa Clara study recruited volunteers off of Facebook. The obvious source of sampling bias there is that people desperately want to get tested. The people that want it are, of course, people that think they've had it.

The other big piece is understanding the notion of positive predictive value and the way false positive and false negative error rates influence the estimate. And that depends on the incidence of infection in the population.
If you had the ability to arm every person with one tool - a statistical tool or scientific concept - to help them understand and contextualize scientific information as we look to the future of this pandemic, what would it be?
If you have a test that has a 3% error rate, and the incidence in the population is below 3%, then most of the positives that you get are going to be false positives. And so you're not going to get a very tight estimate about how many people have it. This has been a real problem with the Santa Clara study. From my read of the paper, their data is actually consistent with nobody being infected. A New York Citystudy on the other hand showed 21% seropositive, so even if there has a 3% error rate, the majority of those positives have to be true positives.

One that I think will come along sooner or later is interpreting studies of treatments. We've dealt with that a little bit with the hydroxychloroquine business but not in any serious way because the hydroxychloroquine work has been pretty weak and the results have not been so positive.
That's very interesting, and not what I expected you to say.
But there are ongoing tests of a large range of existing drugs. And these studies are actually pretty hard to do. There's a lot of subtle technical issues: what are you doing for controls? Is there a control arm at all? If not, how do you interpret the data? If there is a control arm, how is it structured? How do you control for the characteristics of the population on whom you're using the drug or their selection biases in terms of who's getting the drug?
That correlation does not imply causation.
Unfortunately, given what we've already seen with hydroxychloroquine, it's fairly likely that this will be politicized as well. There'll be a parallel set of issues that are going to come around with vaccination, but that's more like a year off.

I would like people to understand that there are interactions between the models we make, the science we do and the way that we behave. The models that we make influence the decisions that we take individually and as a society, which then feed back into the models and the models often don't treat that part explicitly.

Once you put a model out there that then creates changes in behavior that pull you out of the domain that the model was trying to model in the first place. We have to be very attuned to that as we try to use the models for guiding policy.

What did you expect?

That's another very good one. Seasonality is a great example there. We're trying a whole bunch of things at the same time. We're throwing all kinds of possible solutions at this and lots of things are changing. It's remarkable to me actually, that so many US states are seeing the epidemic curve decrease. And so there's a bunch of possibilities there. It could be because people's behavior is changing. There could be some seasonality there. And there are other possible explanations as well.

But what is really important is that just because the trend that you see is consistent with a story that someone's selling, there may be many other stories that are also consistent, so inferring causality is dangerous.

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'There is no absolute truth': an infectious disease expert on Covid-19, misinformation and 'bullshit'

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arl Bergstrom is uniquely suited to understanding the current moment. A professor of biology at the University of Washington, he has spent his career studying two seemingly disparate topics: emerging infectious diseases and networked misinformation. They merged into one the moment reports of a mysterious respiratory illness emerged from China in January.

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A group of high school and college students from around the world who met on a social network popular among video gamers are putting their tech skills to work against the spread of COVID-19 misinformation.

Their effort began in early April, when a Harvard University student, Lucas Chu, sent out a message to a group of people he had met on the social platform Discord. The idea was to organize students to make data visualizations to help show trends in the spread of COVID-19 and to counter false narratives about the virus.

Those students formed what they called the Coronavirus Visualization Team, and they quickly gathered hundreds of participants from high schools and colleges. The goal was to harness their personal interests in computing to help end the pandemic.

"There's not a lot of ways that we could get directly involved on the front lines-we're too young," says Scott Blender, an early member of the group who will be an incoming freshman at Temple University in the fall. "Anyone with a computer and basic [internet] can learn the skills to get involved with meaningful data visualizations. We don't have to be like 30 years old to make an impactful difference. We can be like 18." Blender is 18.

One of their most popular works so far has been the COVID-19 Risk Score Model, which shows how the pandemic is "disproportionately affecting marginalized and underserved communities."

How can these students with little formal training build trust for their infographics and visualizations?

Blender says the answer is to base their work on trusted datasets and to be very clear about their sources. The students have also formed partnerships with a number of high-profile institutions, including Harvard's Center for Geographic Analysis and the MIT Innovation Initiative. "It has really been just by cold emailing and cold contacting people," says Blender, who says that he is working 14-hour days on Zoom calls and emails to help lead the effort.

One of the established folks mentoring the group is Thara Pillai, director of alumni programs and engagement at Harvard Innovation Labs. "What's fascinating about this team is that it is made up of students from major universities from across the country," she said in an email interview. "They've managed to figure out a way to bring in data scientists, engineers, and industry experts to develop meaningful insights around coronavirus from unemployment to predictive analytics around the spread and growth of the virus in different communities. While they could easily charge for this valuable content, they've have elected to provide the content free of charge to the media, governments and social agencies to advance theses entities' understanding of this deadly virus."

Just this week, the student leaders filed papers to incorporate as a nonprofit, under the name Erevna, the Greek word for research. They hope to have a new website up under the new name soon.

One of the biggest motivations of the group is to clearly present data in ways that counter false narratives about the coronavirus, says Pauline Chane, a participant who recently graduated from the University of California at Berkeley. As she put it: "We want to fight the infodemic around COVID-19."

Chane's role is to connect students in Erevna with research projects on an open-source research platform called Just One Giant Lab, or JOGL. That platform features a long list of projects led by researchers around the world looking for volunteers to contribute their time and talents.

"There's a project I found on JOGL that was looking at Twitter data about pro-vaccine or anti-vaccine views," she says. "The project had been dormant for many many months, but I felt like it was a good fit for students. I was able to get into contact with the person who started the project in Uganda," she adds. The researcher eventually put some of the student volunteers to work, she says. "We basically revived a dead project for him."
Editor's Note: This story was updated with a quote from an official at Harvard Innovation Labs.

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New Student-Led Nonprofit Aims to Counter COVID Misinformation - EdSurge News

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A group of high school and college students from around the world who met on a social network popular among video gamers are putting their tech skills to work against the spread of COVID-19 misinformation. Their effort began in early April, when a Harvard University student, Lucas Chu, sent out a message to a group of people he had met on the social platform Discord.

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Aerosol experts, from engineers to doctors, weigh in on the ability of tiny droplets to transmit the virus that causes COVID-19

smithsonianmag.com

Over the past few months, an increasing number of scientists, clinicians, and engineers have called for greater recognition that aerosols, in addition to larger droplets can transmit the novel coronavirus that causes COVID-19. While the difference is literally miniscule, acknowledging this route of transmission would result in significant changes in how the public can bring an end to the global pandemic. In the near term, it would inform social distancing and mask wearing recommendations from local governments, and in the long term, engineers and architects will need to rethink ventilation and air filtration in the design of everything from schools to cruise ships.

Aerosols are microscopic particles that can remain airborne for hours, and carry pathogens up to dozens of meters, under the right conditions. Scientists who study airborne infection generally consider aerosols to be particles smaller in diameter than five micrometers, or 0.005 millimeters, less than one-tenth the width of a human hair. Larger droplets, commonly referred to as "droplets," expelled by sneezing or coughing tend to fall to the ground or other surfaces rather quickly, while aerosols hang around for minutes to hours. How long a virus can remain airborne depends on the size of the droplet containing it. "That determines everything about how far it can travel, how long it can stay airborne before it falls to the ground," says Linsey Marr, a professor of civil and environmental engineering at Virginia Tech.

How long aerosolized viruses, including SARS-CoV-2, the novel coronavirus, can remain infectious is still unclear, but some experiments have shown it is possible "for many hours," says Marr. In one such experiment, published in April in the New England Journal of Medicine, researchers found that aerosols of SARS-CoV-2 sprayed from a nebulizer had a half-life-the time it takes for 50 percent of virus to stop being infectious-of more than an hour. In another, published in June by the Centers for Disease Control and Prevention, aerosols containing SARS-CoV-2 stayed infectious for up to 16 hours after being similarly aerosolized.

All of the experts who spoke to Smithsonian for this article agreed that the likelihood of the virus being transmittable through aerosols only underscores the need for the public to continue their hand-washing and mask-wearing-which blocks aerosol sprays to varying degrees depending on the type of mask worn. The concentration of aerosols is heaviest near an infected person, so social distancing also remains very important for limiting the virus' spread.

Jones adds that the possibility of airborne transmission raises the issue of how to protect workers in healthcare and other settings alike. A shortage of respirators means that the devices should go to healthcare workers first, but if they become more widely available, service industry and transportation workers might benefit substantially from access to them. Surgical masks offer some protection, but it may not be enough for workers who routinely interact with the public.

For months after the pandemic began, the World Health Organization (WHO) had been hesitant to accept aerosols were a likely transmission route for the coronavirus. The agency suggested that airborne transmission was likely only during certain medical procedures such as intubation, and focused its warnings on infection risks associated with larger droplets expelled by coughing or sneezing. But evidence that the coronavirus could travel via aerosols began piling up. In a study that was published online in May before being peer reviewed, researchers found SARS-CoV-2 could be carried on a person's breath, and in June, Marr co-authored a study in that added to the evidence the novel coronavirus could be airborne. A commentary published on July 6 in Clinical Infectious Diseases and co-signed by 239 scientists, clinicians, and engineers called on health officials to recognize the possibility of airborne transmission. A day later, the WHO officially announced that the novel coronavirus SARS-CoV-2 can spread via aerosols. Benedetta Allegranzi, technical leader of the WHO task force on infection control denied that the publication of the commentary had any relation with WHO softening its position.

"Outside of health care settings, some outbreak reports suggest the possibility of airborne transmission in indoor crowded spaces with poor ventilation," says Allegranzi in an email to Smithsonian. "More (and high quality) research is needed to elucidate these kind of settings [and] outbreaks and the relative importance of different transmission routes."

Marr, who co-signed the commentary in Clinical Infectious Diseases, penned an op-ed in the New York Times that called the agency's updated position "grudging partial acceptance." She described the difficulties in determining whether a virus can be airborne to Smithsonian. First, a researcher has to sample the air in a potential area of infection with a device like vacuum cleaner, and then they have to show that any viruses collected with it are still alive and infectious, Marr explains. Finally, they have to determine whether people can get sick if they breathe in the airborne virus. "All those steps are actually very hard to demonstrate for any particular route of transmission," she says. Researchers have been able to confirm the first two steps-that the novel coronavirus can be carried on aerosols and that it can remain infectious-and so far demonstrated the third step with ferrets, but not humans.

When suggesting infection control measures, epidemiologists typically don't consider aerosols unless they are seeing transmission travel longer distances, such as between rooms. But that could be looking at airborne transmission backwards, according to Marr. "I guarantee it's more important when people are close together," because the plume of aerosols and respiratory droplets an infected person exhales gets diluted farther away.

Rachael Jones, a professor of family and preventative medicine at the University of Utah, published a framework for determining the likelihood of a virus going airborne in the Journal of Occupational and Environmental Medicine in 2015. Jones says prevailing ideas about infection control that emphasize large-droplet respiratory sprays generated by sneezing or talking don't reflect the current understanding of aerosols. "When we measure the viruses in respiratory aerosols, we find a lot of [them] in the smallest particles which can be inhaled," she says. Infection control procedures oriented around large-droplet transmission focus on controlling droplets that splash onto the face, she added, but if the coronavirus can initiate infection deep in the respiratory tract, tiny aerosols that can carry virus down into it would require procedures be implemented that prevent people from breathing them in.

Lidia Morawska, who co-authored the commentary in Clinical Infectious Diseases, was one of the first researchers to argue that the novel coronavirus could travel on aerosols. Morawska, a professor of atmospheric sciences and environmental engineering at Queensland University of Technology in Australia, published a study online in April in Environment International that argued SARS-CoV-2 was likely airborne based on previous studies of other coronaviruses. She says a small community of scientists has been advocating for health experts to acknowledge the likelihood of aerosols as an avenue of respiratory infection for decades. Most buildings are completely unprepared for an airborne respiratory disease because they lack the kind of ventilation and air filtration systems that could minimize spread, she says. "For years, we've been trying to bring this to their attention to prepare the right approaches for building interiors to be prepared against respiratory infection transmission."

Reluctance to accept the likelihood of airborne infection is not new, says Donald Milton, the other co-author of the commentary. Milton is a professor of environmental and occupational health at the University of Maryland, though he calls himself an "aerobiologist." He has been studying aerosols as infection routes for four decades. He says a book published more than a century ago by the noted public health administrator Charles Chapin, titled The Sources and Modes of Infection, still influences infection control beliefs today. At the time of its publication, the medical community was working hard to dispel the ancient fallacy that so-called miasmas, or "bad airs," were the source of all disease. "Transmission via aerosols sounded too much like miasmas and pestilential vapors," Milton says.

Tuberculosis was widespread in Chapin's day: in 1900, it was the leading single cause of death in the United States, killing 194 of every 100,000 Americans. Microbiologist Robert Koch discovered it was caused by bacteria in 1882, but many still blamed it on "bad airs," and the New York City Department of Health officially acknowledged it was infectious only in 1894. While Chapin briefly acknowledged the possibility tuberculosis could be transmitted by "dust-borne bacteria," he wrote that it was better to concentrate on contact and spray-borne (or droplet-borne) transmission. "He said we shouldn't talk about [aerosols] because it might dissuade people from washing their hands and being hygienic," Milton says.

That dogma was adhered to for decades, and Milton says it is pretty much what you hear today from a lot of infectious disease people.

It wasn't until the 1950s that scientists were able to establish that airborne transmission of tuberculosis could occur-and another half-century passed before Kevin Fennelly, a pulmonologist at the National Institutes of Health, published the first study to directly measure how much of the pathogen could be carried in aerosols. Even then, Fennelly wasn't sure it could be carried on aerosols generated by coughing. Fennelly says it wasn't until an "unexpected and kind of revolutionary" study published in 2014 in changed the thinking around pathogens and exhaled breath. "Over time, I've become a convert to the data," he says. Last month, he published a viewpoint in arguing that in light of the COVID-19 pandemic, infection-control measures need to include guidelines around masks and respirators to protect healthcare workers from aerosols.
Editors' Note, August 13, 2020: This article originally misstated the name of Donald Milton. We regret the error. August 20: This article originally misstated that Kevin Fenelly was the first to definitively demostrate that tuberculosis was airborne. He was not. He was the first to directly quantify tuberculosis aerosols in coughs. We regret the error.
Jones adds that installing effective ventilation systems in public buildings and businesses is critical, but acknowledges that doing so will be a complex endeavor. "We don't have a lot of off-the-shelf designs for those systems, so I think it's an area to look in terms of research development and major changes in how we think about the configuration of public spaces."

Morawska says old dogma around routes of transmission that ignore aerosols as a possible avenue must be updated to include them, and added that she hopes the pandemic will inspire everyone from public health officials to architects to rethink how they approach infection control. Humanity is "better prepared to deal with an incoming asteroid" than with a novel virus, she says. "We need to completely rethink the issue of design of buildings, provision of ventilation and how we operate buildings."

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What Scientists Know About Airborne Transmission of the New Coronavirus

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smithsonianmag.com Over the past few months, an increasing number of scientists, clinicians, and engineers have called for greater recognition that aerosols, in addition to larger droplets can transmit the novel coronavirus that causes COVID-19. While the difference is literally miniscule, acknowledging this route of transmission would result in significant changes in how the public can bring an end to the global pandemic.

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Now, the country is averaging nearly 65,000 new cases a day. Outbreaks in the South appear to be leveling off, but worrying trends are emerging in the Midwest. And the nationwide death toll recently topped 150,000, a once-unthinkable number that is only going to increase as the pandemic rages on.

But when Fauci and other top health officials testified before Congress on Friday, they struck a hopeful tone on the prospects for a COVID-19 vaccine, faster testing and getting the virus under control, so long as Americans are vigilant about wearing masks and avoiding crowds.

Here are five takeaways from Friday's hearing before the House Select Subcommittee on the Coronavirus Response, led by House Majority Whip James Clyburn (D-S.C.).

Fauci appears more optimistic about vaccine prospects
Fauci, who heads the National Institute of Allergy and Infectious Diseases, said he was "cautiously optimistic" a vaccine would be approved by the end of the year, and widely distributed throughout 2021.

"There's never a guarantee that you're going to get a safe and effective vaccine, but from everything we've seen now in the animal data, as well as the early human data, we feel cautiously optimistic that we will have a vaccine by the end of this year and as we go into 2021," Fauci said.

Moderna, in collaboration with the National Institutes of Health (NIH), just launched its phase three trial for a potential COVID-19 vaccine, the phase that will help determine whether it is safe and effective.

There are dozens of vaccine candidates, including five or six that the federal government is involved with, Fauci said. More than 250,000 people have expressed interest in participating in the vaccine trials, he added.

Fauci, who's a member of the White House coronavirus task force, tried to reassure Democrats that safety wouldn't be compromised in the race to find an effective vaccine.

"Historically, the [Food and Drug Administration] has based their decisions on science. They will do it this time," he said.

The federal government is paying billions of dollars to several drug companies to manufacture millions of vaccines before they are proven safe and effective, in hopes of allowing a vaccine to be quickly distributed if approved by the FDA.

Testing delays persist but improvements are coming
Commercial labs have struggled under increased demand for COVID-19 testing created by outbreaks in the South and West. Adm. Brett Giroir, the health official leading the Trump administration's testing strategy, said a two- or three-day turnaround time for all test results is not currently possible, raising concerns about the effectiveness of contact tracing.

About 75 percent of test results are coming back within five days, he said.

"If you have to wait multiple days for the result of a test, and the test is done in the context of contact tracing, that in many respects obviates the whole purpose of doing it," Fauci noted. "Because if you have to wait that long, a person has already been out in the community in that period of time."

Giroir said the "future" of testing is point-of-care tests that don't need to be sent to labs and can return results in about 15 minutes.

Expanding point-of-care testing could also reduce the strain at labs, he said.

On Friday, NIH announced it would award contracts to seven companies to scale up manufacturing of point-of-care tests that could "significantly" expand the nation's testing capacity as early as September.

Fauci pushes back on hydroxychloroquine study touted by Trump
A study that appears to show hydroxychloroquine as an effective COVID-19 treatment is "flawed," Fauci told an inquiring member of Congress.

But the study was noncontrolled and retrospective, Fauci said, opening it up to potential bias. Patients also received corticosteroids, which have been proven to reduce death in COVID-19 patients, further clouding the effects of hydroxychloroquine.

"That study is a flawed study," Fauci said.

Studies of hydroxychloroquine that have followed the gold standards in clinical research to reduce bias have shown the drug is not effective at treating COVID-19, Fauci added.

"Every randomized, placebo-controlled trial that has looked at it has shown no efficacy, so I just have to go with the data," Fauci said. "I don't have any horse in the game one way or the other. I just looked at the data."

Republicans, CDC voice support for reopening schools
"Do you think that schools should safely reopen this fall with in-person learning?" Scalise asked.

"Yes, I think it's important to realize that it's in the public health best interest of K-12 students to get back in face-to-face learning," Redfield replied. "There's really very significant public health consequences of the school closure."

Scalise added: "I hope that these school systems follow President Trump and these great medical doctors' guidance and help those kids by safely reopening."

Some public health experts have criticized the CDC for what they view as a political push by the agency to reopen schools, echoing the wishes of Trump. While experts are all on the same page regarding the importance of opening schools, some note it might not be possible if there is too much transmission in the surrounding community, and if schools do not have the necessary resources to invest in safety measures.

"Do children get infected? Yes, they do," Fauci responded, adding that "hundreds of thousands" of children have been infected.

Conservatives take combative approach toward Fauci
Jordan sought to engage Fauci on what some conservatives view as a double standard: progressives and some public health experts supporting limits on public gatherings while at the same time supporting Black Lives Matter protests that bring together thousands of people.

Fauci, however, did not address the protests directly, saying more broadly: "Avoid crowds of any type no matter where you are ... I don't judge one crowd versus another crowd."

While it is hard to know for sure the extent of the effect of the protests on the recent outbreaks, there have not been clear spikes in cases in cities that have held protests.

Jordan nonetheless pressed Fauci to address the demonstrations, asking, "So the protests don't increase the spread of the virus?"

"I didn't say that, you're putting words in my mouth," Fauci shot back.

Taking a different tack, Scalise got Fauci on record supporting some of Trump's decisions on the COVID-19 crisis, including banning travelers from China, a move Trump often cites when defending his response.

"Do you agree with that decision?" Scalise asked.

"I do," Fauci replied.

"Do you think that decision saved lives, Dr. Fauci?" Scalise said.

"Yes I do," Fauci replied.

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Now, the country is averaging nearly 65,000 new cases a day. Outbreaks in the South appear to be leveling off, but worrying trends are emerging in the Midwest. And the nationwide death toll recently topped 150,000, a once-unthinkable number that is only going to increase as the pandemic rages on.

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Professor Harvey Risch, M.D., Ph.D., is a researcher at the Yale School of Public Health with a specialty in cancer etiology, prevention and early diagnosis, and epidemiologic methods.

He recently studied the efficacy of hydroxychloroquine (used in conjunction with two other drugs) to treat people infected with COVID-19 and concluded that the approach should be "widely available" in the fight against the current pandemic.

The results of his research are published in the American Journal of Epidemiology.

Describe your findings.

HR: COVID-19 is really two different diseases. In the first few days, it is like a very bad cold. In some people, it then morphs into pneumonia which can be life-threatening. What I found is that treatments for the cold don't work well for the pneumonia, and vice versa. Most of the published studies have looked at treatments for the cold but used for the pneumonia. I just looked at how well the treatments for the cold worked for the cold. There are five studies done this way, four of hydroxychloroquine plus azithromycin and one with hydroxychloroquine plus doxycycline, and they all show that treating the cold part of COVID-19-the early part-works very well.

Do you think that these drug combinations should be used for all people with COVID-19, or only certain patients?

HR: Most people less than 60 years old who are of healthy weight and who don't have other conditions like heart disease or diabetes can get by without medications. But if anyone starts to have shortness of breath while doing normal activities like walking around at home, they should get medical care immediately.

But the use of hydroxychloroquine to treat COVID-19 remains highly controversial. Why is there so much disagreement if it is effective?

HR: I think that there has been confusion about treating the cold versus treating the pneumonia. These medications don't seem to work so well for treating the pneumonia. As early as possible is crucial, within the first five to six days of symptoms.

Are these drugs safe?

HR: The combination of hydroxychloroquine and azithromycin has been used for decades in hundreds of thousands of people with rheumatoid arthritis. There is a concern that these medications do change the heart pacing a little and could cause cardiac arrhythmias. However, these arrhythmias are still very rare in people using these medications. People who already have heart arrhythmias or are predisposed to them or have family histories of them should discuss this with their health care providers and see if using hydroxychloroquine plus doxycycline or some other medications would be a better choice.

Does hydroxychloroquine have the potential to be a "game-changer" in the fight against this pandemic?

HR: Hydroxychloroquine alone is not the whole story. It needs to be combined with azithromycin or doxycycline and probably with zinc to make it most effective. The game changer is to aggressively treat people as soon as possible, before they are hospitalized, to keep them from becoming hospitalized in the first place. Hydroxychloroquine plus the other medications is what we know about now. In a few months we may have data on other medications that also work. We just have to start with something now.

How widely is the drug currently being used to treat people infected with COVID-19? What do you recommend?

HR: Various places around the world have started using these drugs. An international survey of doctors who treat COVID-19 patients recently showed 72 percent of doctors in Spain say that they have been using them. I think that doctors need to be able to use their own clinical judgement about their patients and have objective information about drugs that can work for the early part of the infection, the cold part.

Why did you study this?

HR: This pandemic is undoubtedly the biggest public health crisis of our time. I started seeing reports of treatment benefit in France and New York and couldn't understand where the controversy was coming from. So, I did an exhaustive search of studies and data on medication use in COVID-19 outpatients and the paper I wrote just describes everything that I found. Every study has details and the details are important.

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Using Hydroxychloroquine and Other Drugs to Fight Pandemic

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Professor Harvey Risch, M.D., Ph.D., is a researcher at the Yale School of Public Health with a specialty in cancer etiology, prevention and early diagnosis, and epidemiologic methods. He recently studied the efficacy of hydroxychloroquine (used in conjunction with two other drugs) to treat people infected with COVID-19 and concluded that the approach should be "widely available" in the fight against the current pandemic.

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The U.S. could learn a lot from Britain.

By Ezekiel J. Emanuel , Cathy Zhang and

The authors work at the University of Pennsylvania's department of medical ethics and health policy.

British researchers have conducted large, rapid randomized trials of dexamethasone and other drugs. Credit... Yui Mok/Press Association, via Associated Press

Using its authority to approve treatments for emergency use, the Food and Drug Administration recently allowed convalescent plasma for hospitalized Covid-19 patients. But because the large 35,000-person study on the treatment lacked true randomization and placebos, we don't really know if antibody-rich plasma actually improves mortality.

In fact, both Dr. Francis Collins, the director of the National Institutes of Health, and Dr. Anthony Fauci, the government's top infectious disease expert, raised concerns that emerging data on the treatment was not strong enough to merit emergency approval.

Convalescent plasma illustrates more than a problem with the F.D.A.'s approval process. It points to a larger shortcoming in clinical research in the United States.

Americans and American biomedical researchers have often prided themselves on conducting the best clinical research in the world. Yet with over six million coronavirus cases and 183,000 deaths, the United States has produced little pathbreaking clinical research on treatments to reduce cases, hospitalizations and deaths. Even one of the most important U.S. studies to date, which showed that the antiviral drug remdesivir could reduce the time Covid-19 patients spent in the hospital to 11 days from about 15, had too few subjects to demonstrate a statistically significant reduction in mortality.

Progress on therapeutics research has been a very different story in Britain. In mid-March researchers there began a randomized evaluation of Covid-19 therapies, known as Recovery, that involves every hospital in the nation. The goal was to conduct large, rapid and simple randomized trials to define standard treatment. Some 12,000 patients were quickly randomized - that is, assigned by chance to receive different treatments - and within 100 days of the effort's start, researchers made three major discoveries that transformed Covid-19 care worldwide.

Researchers found no benefits from the use of hydroxychloroquine in hospitalized Covid-19 patients, nor from the lopinavir-ritonavir drug combination. On the other hand, dexamethasone, an inexpensive steroid, was found to reduce mortality by up to one-third in hospitalized patients with severe respiratory complications. Each of these results was conclusive and went against the expectations of many clinicians, guideline writers and lay advocates. The results demonstrated the critical need for randomized trials to separate drugs we hope work from treatments we know work.

In the United States, by comparison, the government-sponsored Patient-Centered Outcomes Research Institute has spent millions of dollars creating a large clinical research network but has produced no research results on Covid-19 therapeutics.

That's not to say nothing has been done. In April, the institute did begin HERO, an effort to bring together health care workers to share their clinical experiences, and a randomized trial to determine whether hydroxychloroquine can prevent coronavirus infections. But so far, that trial has enrolled only 10 percent of the health care workers necessary for the trial and as a result has reduced its enrollment targets; no useful results are likely. Only at end of July did Dr. Collins finally announce that the National Institutes of Health was preparing to begin large-scale clinical trials for Covid-19 treatments. And it wasn't until last month that the N.I.H. began two trials to test antibody treatments.

But with about 1,000 Americans and more than 5,000 others worldwide dying every day from Covid-19, taking until the end of July to begin enrolling patients in randomized trials seems a bit slow.

As the United States designs research protocols to investigate clinical therapeutics, we should ask: What has gone right in Britain that the United States can adopt to help rapidly and definitively identify Covid-19 therapeutics that really work, and just as important, those that don't?

Maybe the most important factor is an attitudinal difference: British clinical researchers have a longstanding commitment to large, simple and rapid randomized trials. American researchers prefer smaller, selective and complex trials with many restrictions on patients who can enroll.

Martin Landray, the British epidemiologist who is one of the leaders of Britain's effort, identified six other factors that contributed to its success in an interview with us. They should be applied in the United States to produce more rapid, large-scale clinical trials of Covid-19 therapeutics.

First, the Recovery trials are designed to be easy to take part in, with paperwork that is short and simple for health care providers - doctors, nurses, even medical students - to handle.

Second, the Recovery protocol was quickly approved at the national level and adopted by all hospitals in Britain.

Third, background patient data provided by the National Health Service helped to simplify the research process. Information like age, race and other health problems is already built into the hospital systems for all patients, so providers didn't have to collect it. Follow-up data is more comprehensive because post-discharge mortality can be tracked through the N.H.S. database.

Fourth, support from leaders in government health care ensured widespread cooperation by hospitals. The four chief medical officers of England, Scotland, Wales and Northern Ireland wrote to every hospital executive in their country that participating in the Recovery trial was a priority.

Fifth, Britain has a national system of research nurses who were rapidly redeployed to work on Covid-19 research at the beginning of the pandemic.

And last, the British effort was incorporated as part of everyday clinical care in hospitals. The alternative, of haphazardly trying anything and everything, which seems to have been the American way, was rejected in Britain because it neither optimizes patient care nor generates useful data. The philosophy that clinical research is the standard of care - a philosophy common in cancer treatment - was the right and ethical approach for Covid-19.

An additional feature worth noting about the Recovery effort is that it has been relatively inexpensive to conduct. Oxford's grant for the study was roughly $2.8 million. N.H.S. clinicians recruited patients as part of their job and drugs were provided as part of the N.H.S. Even with the additional cost of paying for clinical research associates, the trial was remarkably inexpensive and has proved its value.

What Britain has done is not beyond the United States. America has health networks with comprehensive patient data on a meaningfully large scale. Though networks don't encapsulate the full population, they cover enough patients to provide background data for large clinical trials.

Moreover, the United States has no shortage of researchers, who can be deployed on Covid-19 therapeutic research, especially as many have been freed up with other research having been put on hold.

Unfortunately, unlike Britain, the United States has lacked a clear, unified message from government health care leaders, major insurance companies and hospital systems to put in place large, simple randomized trials that are considered the standard of care for Covid-19 treatment. We need to change that muddled approach now and reassert the nation's clinical research excellence.

Ezekiel J. Emanuel is vice provost of global initiatives and professor of medical ethics and health policy at the University of Pennsylvania. Cathy Zhang and Amaya Diana are researchers in the department of medical ethics and health policy at the university.
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Opinion | Where Is America's Groundbreaking Covid-19 Research?

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The U.S. could learn a lot from Britain. By Ezekiel J. Emanuel , Cathy Zhang and The authors work at the University of Pennsylvania's department of medical ethics and health policy. Using its authority to approve treatments for emergency use, the Food and Drug Administration recently allowed convalescent plasma for hospitalized Covid-19 patients.