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This is how America gets its vaccines

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This is how America gets its vaccines


Biden’s newly released pandemic strategy is organized around a central goal: to oversee administration of 100 million vaccines in 100 days. To do it, he’ll have to fix the mess.

Some critics have called his plan too ambitious; others have said it’s not ambitious enough. It’s guaranteed to be an uphill battle. But before we get to the solutions, we need to understand how the system operates at the moment—and which aspects of it should be ditched, replaced, or retained.

From manufacturer to patient

At the federal level, two core systems sit between the vaccine factories and the clinics that will administer the shots: Tiberius, the Department of Health and Human Services’ vaccine allocation planning system, and VTrckS, the Centers for Disease Control and Prevention’s vaccine ordering portal. 

Tiberius takes data from dozens of mismatched sources and turns it into usable information to help state and federal agencies plan distribution. VTrckS is where states actually order and distribute shots.

The two are eons apart technologically. Whereas Palantir built Tiberius last summer using the latest available technology, VTrckS is a legacy system that has passed through multiple vendors over its 10-year existence. The two are largely tied together by people downloading files from one and uploading them to the other.

Dozens of other private, local, state, and federal systems are involved in allocating, distributing, tracking, and administering vaccines. Here’s a step-by-step explanation of the process.

Step one: Manufacturers produce the vaccine

HHS receives regular production updates from Pfizer and Moderna. The manufacturers communicate estimated volumes in advance to help HHS plan before confirming real production numbers, which are piped into Tiberius.

Both vaccines are made of messenger RNA, a biotechnology that’s never been produced at scale before, and they need to be kept extremely cold until just before they go into a needle: Moderna’s must be kept at -25 to 15 °C, while Pfizer’s requires even lower temperatures of -80 to -60 °C. In the fall, it became clear that manufacturers had overestimated how quickly they could distribute doses, according to Deacon Maddox, Operation Warp Speed’s chief of plans, operations, and analytics and a former MIT fellow.

“Manufacturing, especially of a nascent biological product, is very difficult to predict,” he says. “You can try, and of course everybody wants to you try, because everybody wants to know exactly how much they’re going to get. But it’s impossible.”

PFIZER

This led to some of the first stumbles in the rollout. While training the states on how to use Tiberius, Operation Warp Speed entered those inflated estimates into a “sandbox” version of the software so states could model different distribution strategies for planning purposes. When those numbers didn’t pan out in reality, there was confusion and anger.

“At the end of December, people were saying, ‘We were told we were going to get this and they cut it back.’ That was all because we put notional numbers into the exercise side, and folks assumed that was what they were going to get,” says Maddox. “Allocation numbers are highly charged. People get very emotional.”

Step two: The federal government sets vaccine allocations

Every week, HHS officials look at production estimates and inventory numbers and decide on the “big number”—how many doses of each vaccine will go out to states and territories in total. Lately, they’ve been sticking to roughly 4.3 million per week, which they’ve found “allows us to get through lows in manufacturing, and save through highs,” Maddox says.

That number goes into Tiberius, which divvies up vaccines on the basis of Census data. Both HHS and media reports have sometimes described this step as using an algorithm in Tiberius. This should not be confused with any kind of machine learning. It’s just simple math based on the allocation policy, Maddox says.

Thus far, the policy has been to distribute vaccines according to each jurisdiction’s adult (18+) population. Maddox says the logic in Tiberius could easily be updated should Biden decide to do it on another basis, such as elderly (65+) population.

Once Operation Warp Speed analysts confirm the official allocation numbers, Tiberius pushes the figures to jurisdictions within their version of the software. An HHS employee then downloads the same numbers in a file and sends them to the CDC, where a technician manually uploads it to set order limits in VTrckS. (You can think of VTrckS as something like an online store: when health departments go to order vaccines, they can only add so many to their cart.)

Even that hasn’t been an exact science. Shortly before the inauguration, in a phone call with Connecticut governor Ned Lamont, outgoing HHS secretary Alex Azar promised to send the state 50,000 extra doses as a reward for administering vaccines efficiently. The doses arrived the next week.

The deal was representative of “the rather loose nature of the vaccine distribution process from the federal level,” Lamont’s press secretary, Max Reiss, told us in an email. 

Step three: States and territories distribute the vaccine locally

State and territory officials learn how many vaccines they’ve been allotted through their own version of Tiberius, where they can model different distribution strategies.

Tiberius lets officials put data overlays on a map of their jurisdiction to help them plan, including Census data on where elderly people and health-care workers are clustered; the CDC’s so-called social vulnerability index of different zip codes, which estimates disaster preparedness on the basis of factors like poverty and transportation access; and data on hospitalizations and other case metrics from Palantir’s covid surveillance system, HHS Protect. They can also enter and view their own data to see where vaccination clinics and ultra-cold freezers are located, how many doses different sites have requested, and where vaccines have already gone.

Once states decide how many doses of each vaccine they want to send to each site, they download a file with addresses and dose numbers. They upload it into VTrckS, which transmits it to the CDC, which sends it to manufacturers.

A Pfizer shipment

PFIZER

Last week, Palantir rolled out a new “marketplace exchange” feature, effectively giving states the option to barter vaccines. Since the feds divvy up both Moderna and Pfizer vaccines without regard to how many ultra-cold freezers states have, rural states may need to trade their Pfizer allotment for another state’s Moderna shots, Maddox says.

When thinking about the utility of the system, it’s worth noting that many health departments have a shallow bench of tech-savvy employees who can easily navigate data-heavy systems.

“It’s a rare person who knows technology and the health side,” says Craig Newman, who researches health system interoperability at the Altarum Institute. “Now you throw in large-scale epidemiology…it’s really hard to see the entire thing from A to Z.”

Step four: Manufacturers ship the vaccines

Somehow, shipping millions of vaccines to 64 different jurisdictions at -70 °C is the easy part.

The CDC sends states’ orders to Pfizer and to Moderna’s distribution partner McKesson. Pfizer ships orders directly to sites by FedEx and UPS; Moderna’s vaccines go first to McKesson hubs, which then hand them off to FedEx and UPS for shipping.

Tracking information is sent to Tiberius for every shipment so HHS can keep tabs on how deliveries are going.

Step five: Local pharmacies and clinics administer the vaccine

At this point, things really start to break down. 

With little federal guidance or money, jurisdictions are struggling with even the most basic requirements of mass immunization, including scheduling and keeping track of who’s been vaccinated.

Getting people into the clinic may intuitively seem easy, but it’s been a nightmare almost everywhere. Many hospital-based clinics are using their own systems; county and state clinics are using any number of public and private options, including Salesforce and Eventbrite. Online systems have become a huge stumbling block, especially for elderly people. Whenever jurisdictions set up hot lines for the technologically unsavvy, their call centers are immediately overwhelmed. 

Even within states, different vaccination sites are all piecing together their own hodgepodge solutions. To record who’s getting vaccines, many states have retrofitted existing systems for tracking children’s immunizations. Agencies managing those systems were already stretched thin trying to piece together messy data sources.

FedEx and UPS trucks depart from Pfizer.

PFIZER

It may not even be clear who’s in charge of allocating doses. Maddox described incidents when state officials contacted HHS to say their caps were too low in VTrckS, only to realize that someone else within their office had transferred doses to a federal program that distributes vaccines to long-term care homes, without telling other decision makers.

“Operation Warp Speed was an incredible effort to bring the vaccine to market quickly,” and get it to all 50 states, says Hana Schank, the director of strategy for public interest technology at the think tank New America. “All of that was done beautifully.” But, she says, the program paid little attention to how the vaccines would actually get to people.

Many doctors, frustrated by the rollout, agree with that sentiment. 

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NASA selects SpaceX’s Starship as the lander to take astronauts to the moon

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NASA selects SpaceX’s Starship as the lander to take astronauts to the moon


Surprising selection: Last year, NASA awarded three different groups contracts to further develop their own proposals for lunar landers: $135 million to SpaceX, $253 million to defense company Dynetics (which was working with Sierra Nevada Corporation), and $579 million to a four-company team led by Blue Origin (working with Northrop Grumman, Lockheed Martin, and Draper). 

SpaceX didn’t just receive the least amount of money—its proposal also earned the worst technical and management ratings. NASA’s associate administrator (now acting administrator) Steve Jurczyk wrote (pdf) that Starship’s propulsion system was “notably complex and comprised of likewise complex individual subsystems that have yet to be developed, tested, and certified with very little schedule margin to accommodate delays.” The uncertainties were only exacerbated by SpaceX’s notoriously poor track record with meeting deadlines.

What changed: Since then, SpaceX has gone through a number of different flight tests of several full-scale Starship prototypes, including a 10-kilometer high-altitude flight and safe landing in March. (It also exploded a few times.) According to the Washington Post, documents suggest NASA was enamored with Starship’s ability to ferry a lot of cargo to the moon (up to 100 tons), not to mention its $2.9 billion bid for the contract, which was far lower than its rivals’. 

“This innovative human landing system will be a hallmark in spaceflight history,” says Lisa Watson-Morgan, NASA’s program manager for the lunar lander system. “We’re confident in NASA’s partnership with SpaceX.”

What this means: For SpaceX’s rivals, it’s a devastating blow—especially to Blue Origin. The company, founded by Jeff Bezos, had unveiled its Blue Moon lander concept in 2019 and has publicly campaigned for NASA to select it for future lunar missions. Blue Moon was arguably the most well-developed of the three proposals when NASA awarded its first round of contracts.

For SpaceX, it’s a big vote of confidence in Starship as a crucial piece of technology for the next generation of space exploration. It comes less than a year after the company’s Crew Dragon vehicle was certified as the only American spacecraft capable of taking NASA astronauts to space. And it seems to confirm that the SpaceX is now NASA’s biggest private partner, supplanting veteran firms like Northrop Grumman and shunting newer ones like Blue Origin further to the sidelines. However, there’s at least one major hurdle: Starship needs to launch using a Super Heavy rocket—a design that SpaceX has yet to fly.

For NASA, the biggest implication is that SpaceX’s vehicles will only continue to play a bigger role for Artemis, the lunar exploration program being touted as the successor to Apollo. Former president Donald Trump’s directive for NASA to return astronauts to the moon by 2024 was never actually going to be realized, but the selection of a single human lander concept suggests NASA may not miss that deadline by much. The first Artemis missions will use Orion, and the long-delayed Space Launch System rocket is expected to be ready soon. 

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Geoffrey Hinton has a hunch about what’s next for AI

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Hinton face grid


Deep learning set off the latest AI revolution, transforming computer vision and the field as a whole. Hinton believes deep learning should be almost all that’s needed to fully replicate human intelligence.

But despite rapid progress, there are still major challenges. Expose a neural net to an unfamiliar data set or a foreign environment, and it reveals itself to be brittle and inflexible. Self-driving cars and essay-writing language generators impress, but things can go awry. AI visual systems can be easily confused: a coffee mug recognized from the side would be an unknown from above if the system had not been trained on that view; and with the manipulation of a few pixels, a panda can be mistaken for an ostrich, or even a school bus.

GLOM addresses two of the most difficult problems for visual perception systems: understanding a whole scene in terms of objects and their natural parts; and recognizing objects when seen from a new viewpoint.(GLOM’s focus is on vision, but Hinton expects the idea could be applied to language as well.)

An object such as Hinton’s face, for instance, is made up of his lively if dog-tired eyes (too many people asking questions; too little sleep), his mouth and ears, and a prominent nose, all topped by a not-too-untidy tousle of mostly gray. And given his nose, he is easily recognized even on first sight in profile view.

Both of these factors—the part-whole relationship and the viewpoint—are, from Hinton’s perspective, crucial to how humans do vision. “If GLOM ever works,” he says, “it’s going to do perception in a way that’s much more human-like than current neural nets.”

Grouping parts into wholes, however, can be a hard problem for computers, since parts are sometimes ambiguous. A circle could be an eye, or a doughnut, or a wheel. As Hinton explains it, the first generation of AI vision systems tried to recognize objects by relying mostly on the geometry of the part-whole-relationship—the spatial orientation among the parts and between the parts and the whole. The second generation instead relied mostly on deep learning—letting the neural net train on large amounts of data. With GLOM, Hinton combines the best aspects of both approaches.

“There’s a certain intellectual humility that I like about it,” says Gary Marcus, founder and CEO of Robust.AI and a well-known critic of the heavy reliance on deep learning. Marcus admires Hinton’s willingness to challenge something that brought him fame, to admit it’s not quite working. “It’s brave,” he says. “And it’s a great corrective to say, ‘I’m trying to think outside the box.’”

The GLOM architecture

In crafting GLOM, Hinton tried to model some of the mental shortcuts—intuitive strategies, or heuristics—that people use in making sense of the world. “GLOM, and indeed much of Geoff’s work, is about looking at heuristics that people seem to have, building neural nets that could themselves have those heuristics, and then showing that the nets do better at vision as a result,” says Nick Frosst, a computer scientist at a language startup in Toronto who worked with Hinton at Google Brain.

With visual perception, one strategy is to parse parts of an object—such as different facial features—and thereby understand the whole. If you see a certain nose, you might recognize it as part of Hinton’s face; it’s a part-whole hierarchy. To build a better vision system, Hinton says, “I have a strong intuition that we need to use part-whole hierarchies.” Human brains understand this part-whole composition by creating what’s called a “parse tree”—a branching diagram demonstrating the hierarchical relationship between the whole, its parts and subparts. The face itself is at the top of the tree, and the component eyes, nose, ears, and mouth form the branches below.

One of Hinton’s main goals with GLOM is to replicate the parse tree in a neural net—this would distinguish it from neural nets that came before. For technical reasons, it’s hard to do. “It’s difficult because each individual image would be parsed by a person into a unique parse tree, so we would want a neural net to do the same,” says Frosst. “It’s hard to get something with a static architecture—a neural net—to take on a new structure—a parse tree—for each new image it sees.” Hinton has made various attempts. GLOM is a major revision of his previous attempt in 2017, combined with other related advances in the field.

“I’m part of a nose!”

GLOM vector

MS TECH | EVIATAR BACH VIA WIKIMEDIA

A generalized way of thinking about the GLOM architecture is as follows: The image of interest (say, a photograph of Hinton’s face) is divided into a grid. Each region of the grid is a “location” on the image—one location might contain the iris of an eye, while another might contain the tip of his nose. For each location in the net there are about five layers, or levels. And level by level, the system makes a prediction, with a vector representing the content or information. At a level near the bottom, the vector representing the tip-of-the-nose location might predict: “I’m part of a nose!” And at the next level up, in building a more coherent representation of what it’s seeing, the vector might predict: “I’m part of a face at side-angle view!”

But then the question is, do neighboring vectors at the same level agree? When in agreement, vectors point in the same direction, toward the same conclusion: “Yes, we both belong to the same nose.” Or further up the parse tree. “Yes, we both belong to the same face.”

Seeking consensus about the nature of an object—about what precisely the object is, ultimately—GLOM’s vectors iteratively, location-by-location and layer-upon-layer, average with neighbouring vectors beside, as well as predicted vectors from levels above and below.

However, the net doesn’t “willy-nilly average” with just anything nearby, says Hinton. It averages selectively, with neighboring predictions that display similarities. “This is kind of well-known in America, this is called an echo chamber,” he says. “What you do is you only accept opinions from people who already agree with you; and then what happens is that you get an echo chamber where a whole bunch of people have exactly the same opinion. GLOM actually uses that in a constructive way.” The analogous phenomenon in Hinton’s system is those “islands of agreement.”

“Geoff is a highly unusual thinker…”

Sue Becker

“Imagine a bunch of people in a room, shouting slight variations of the same idea,” says Frosst—or imagine those people as vectors pointing in slight variations of the same direction. “They would, after a while, converge on the one idea, and they would all feel it stronger, because they had it confirmed by the other people around them.” That’s how GLOM’s vectors reinforce and amplify their collective predictions about an image.

GLOM uses these islands of agreeing vectors to accomplish the trick of representing a parse tree in a neural net. Whereas some recent neural nets use agreement among vectors for activation, GLOM uses agreement for representation—building up representations of things within the net. For instance, when several vectors agree that they all represent part of the nose, their small cluster of agreement collectively represents the nose in the net’s parse tree for the face. Another smallish cluster of agreeing vectors might represent the mouth in the parse tree; and the big cluster at the top of the tree would represent the emergent conclusion that the image as a whole is Hinton’s face. “The way the parse tree is represented here,” Hinton explains, “is that at the object level you have a big island; the parts of the object are smaller islands; the subparts are even smaller islands, and so on.”

Figure 2 from Hinton’s GLOM paper. The islands of identical vectors (arrows of the same color) at the various levels represent a parse tree.

GEOFFREY HINTON

According to Hinton’s long-time friend and collaborator Yoshua Bengio, a computer scientist at the University of Montreal, if GLOM manages to solve the engineering challenge of representing a parse tree in a neural net, it would be a feat—it would be important for making neural nets work properly. “Geoff has produced amazingly powerful intuitions many times in his career, many of which have proven right,” Bengio says. “Hence, I pay attention to them, especially when he feels as strongly about them as he does about GLOM.”

The strength of Hinton’s conviction is rooted not only in the echo chamber analogy, but also in mathematical and biological analogies that inspired and justified some of the design decisions in GLOM’s novel engineering.

“Geoff is a highly unusual thinker in that he is able to draw upon complex mathematical concepts and integrate them with biological constraints to develop theories,” says Sue Becker, a former student of Hinton’s, now a computational cognitive neuroscientist at McMaster University. “Researchers who are more narrowly focused on either the mathematical theory or the neurobiology are much less likely to solve the infinitely compelling puzzle of how both machines and humans might learn and think.”

Turning philosophy into engineering

So far, Hinton’s new idea has been well received, especially in some of the world’s greatest echo chambers. “On Twitter, I got a lot of likes,” he says. And a YouTube tutorial laid claim to the term “MeGLOMania.”

Hinton is the first to admit that at present GLOM is little more than philosophical musing (he spent a year as a philosophy undergrad before switching to experimental psychology). “If an idea sounds good in philosophy, it is good,” he says. “How would you ever have a philosophical idea that just sounds like rubbish, but actually turns out to be true? That wouldn’t pass as a philosophical idea.” Science, by comparison, is “full of things that sound like complete rubbish” but turn out to work remarkably well—for example, neural nets, he says.

GLOM is designed to sound philosophically plausible. But will it work?

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Why is it so hard to review the Johnson & Johnson vaccine? Data.

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Why is it so hard to review the Johnson & Johnson vaccine? Data.


“We’ll never have perfect data, and there will always be uncertainty,” said Grace Lee, a professor at Stanford University and chair of the advisory panel’s Covid-19 Vaccine Safety Technical Subgroup, when the group met on Wednesday. “It’s really, for me, about getting better risk estimates.” 

Committee members agreed to reconvene once they’ve had more time to gather and assess data about who might be most at risk of complications, and how that compares with the risk of catching and spreading covid.

All six of the cases reported after the vaccine became widely available occurred in women; one additional case—a man—was reported during clinical trials. All patients were between 18 and 48, and several were treated with the blood thinner heparin, which is typically used for clots but worsened the condition of these patients. The symptoms appear very similar to ones associated with AstraZeneca’s covid vaccine, which many European countries have limited or even stopped using. The active components of both are delivered to cells by adenoviruses that have been modified so they can’t replicate.

But because there are other treatments available that use totally different methods, experts say it is sensible to hold off to see if more information becomes available. The Johnson & Johnson vaccine counts for only 7.5 million of America’s 195 million shots delivered; the Pfizer-BioNTech and Moderna vaccines, which use mRNA rather than adenoviruses, are responsible for the rest.

“We’ll never have perfect data, and there will always be uncertainty. It’s really, for me, about getting better risk estimates.” 

“The risks and benefits of continuing to administer the J&J vaccine can’t be looked at in isolation,” says Seema Shah, a bioethicist at Lurie Children’s Hospital in Chicago. “If people have alternatives, at least while the FDA is figuring things out, it makes sense to steer people in the direction of those alternatives.”

Resumption of Johnson & Johnson shots may not mean that it becomes available to everybody, however. Ensuring the safety of vaccines is especially important because they’re given to healthy people, rather than treating people who are already sick, and successfully figuring out which groups might see the most benefit—or most harm—could lead to tiered recommendations. Several EU countries, for instance, have said the AstraZeneca vaccine should be given to older people at higher risk of complications from covid, rather than younger people who might be at higher risk of vaccine complications.

“At the end of the day, the critical issue is if I’m a 30-year-old woman and I get this vaccine, how much will that increase my risk of this bad thing?” says Arthur Reingold, chair of California’s Covid-19 Scientific Safety Review Workgroup and a former member of the CDC’s vaccine advisory panel. 

A more complicated question is what data the committee will review to make a final decision.

No comprehensive data

Information may be limited because the issue was caught quickly, and because the Johnson & Johnson vaccine is so far being deployed only in the US (the company said it was delaying delivery to European Union countries). But making a determination may also prove difficult because America’s medical data is highly fragmented.

Without a national health-care system, there’s no comprehensive way to assess risks and benefits for different groups that have received the vaccine. There is no routine federal capability to connect patient data with vaccine records. Instead, regulators hope clinicians will hear about the pause and proactively report cases they hadn’t previously connected to vaccinations. 

“It might stimulate some clinician to say, ‘Oh my God, Mrs. Jones had that three weeks ago,’” says Reingold. In addition, he says, “there’s still quite a few people who have gotten a dose within the last two weeks, and some of them could develop this rare side effect.”

The voluntary system may seem archaic, but that is how the six cases under review came to the attention of the authorities. They were reported to the CDC through an online database called the Vaccine Adverse Events Reporting System, or VAERS. It is an open website that medical staff, patients, and caregivers can use to notify the government about potential vaccine side effects. 

Because the system is so open, and requires opt-in participation, it’s impossible to calculate exact risks using VAERS data. Epidemiologists generally think of it as a place to look for hypotheses that tie vaccines to side effects, rather than a source that can be used to confirm their suspicions.

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