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Reimagining our pandemic problems with the mindset of an engineer

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Reimagining our pandemic problems with the mindset of an engineer


The last 20 months turned every dog into an amateur epidemiologist and statistician. Meanwhile, a group of bona fide epidemiologists and statisticians came to believe that pandemic problems might be more effectively solved by adopting the mindset of an engineer: that is, focusing on pragmatic problem-solving with an iterative, adaptive strategy to make things work.

In a recent essay, “Accounting for uncertainty during a pandemic,” the researchers reflect on their roles during a public health emergency and on how they could be better prepared for the next crisis. The answer, they write, may lie in reimagining epidemiology with more of an engineering perspective and less of a “pure science” perspective.

Epidemiological research informs public health policy and its inherently applied mandate for prevention and protection. But the right balance between pure research results and pragmatic solutions proved alarmingly elusive during the pandemic.

We have to make practical decisions, so how much does the uncertainty really matter?

Seth Guikema

“I always imagined that in this kind of emergency, epidemiologists would be useful people,” Jon Zelner, a coauthor of the essay, says. “But our role has been more complex and more poorly defined than I had expected at the outset of the pandemic.” An infectious disease modeler and social epidemiologist at the University of Michigan, Zelner witnessed an “insane proliferation” of research papers, “many with very little thought about what any of it really meant in terms of having a positive impact.”

“There were a number of missed opportunities,” Zelner says—caused by missing links between the ideas and tools epidemiologists proposed and the world they were meant to help.

Giving up on certainty

Coauthor Andrew Gelman, a statistician and political scientist at Columbia University, set out “the bigger picture” in the essay’s introduction. He likened the pandemic’s outbreak of amateur epidemiologists to the way war makes every citizen into an amateur geographer and tactician: “Instead of maps with colored pins, we have charts of exposure and death counts; people on the street argue about infection fatality rates and herd immunity the way they might have debated wartime strategies and alliances in the past.”

And along with all the data and public discourse—Are masks still necessary? How long will vaccine protection last?—came the barrage of uncertainty.

In trying to understand what just happened and what went wrong, the researchers (who also included Ruth Etzioni at the University of Washington and Julien Riou at the University of Bern) conducted something of a reenactment. They examined the tools used to tackle challenges such as estimating the rate of transmission from person to person and the number of cases circulating in a population at any given time. They assessed everything from data collection (the quality of data and its interpretation were arguably the biggest challenges of the pandemic) to model design to statistical analysis, as well as communication, decision-making, and trust. “Uncertainty is present at each step,” they wrote.

And yet, Gelman says, the analysis still “doesn’t quite express enough of the confusion I went through during those early months.”

One tactic against all the uncertainty is statistics. Gelman thinks of statistics as “mathematical engineering”—methods and tools that are as much about measurement as discovery. The statistical sciences attempt to illuminate what’s going on in the world, with a spotlight on variation and uncertainty. When new evidence arrives, it should generate an iterative process that gradually refines previous knowledge and hones certainty.

Good science is humble and capable of refining itself in the face of uncertainty.

Marc Lipsitch

Susan Holmes, a statistician at Stanford who was not involved in this research, also sees parallels with the engineering mindset. “An engineer is always updating their picture,” she says—revising as new data and tools become available. In tackling a problem, an engineer offers a first-order approximation (blurry), then a second-order approximation (more focused), and so on.

Gelman, however, has previously warned that statistical science can be deployed as a machine for “laundering uncertainty”—deliberately or not, crappy (uncertain) data are rolled together and made to seem convincing (certain). Statistics wielded against uncertainties “are all too often sold as a sort of alchemy that will transform these uncertainties into certainty.”

We witnessed this during the pandemic. Drowning in upheaval and unknowns, epidemiologists and statisticians—amateur and expert alike—grasped for something solid in trying to stay afloat. But as Gelman points out, wanting certainty during a pandemic is inappropriate and unrealistic. “Premature certainty has been part of the challenge of decisions in the pandemic,” he says. “This jumping around between uncertainty and certainty has caused a lot of problems.”

Letting go of the desire for certainty can be liberating, he says. And this, in part, is where the engineering perspective comes in.

A tinkering mindset

For Seth Guikema, co-director of the Center for Risk Analysis and Informed Decision Engineering at the University of Michigan (and a collaborator of Zelner’s on other projects), a key aspect of the engineering approach is diving into the uncertainty, analyzing the mess, and then taking a step back, with the perspective, “We have to make practical decisions, so how much does the uncertainty really matter?” Because if there’s a lot of uncertainty—and if the uncertainty changes what the optimal decisions are, or even what the good decisions are—then that’s important to know, says Guikema. “But if it doesn’t really affect what my best decisions are, then it’s less critical.”

For instance, increasing SARS-CoV-2 vaccination coverage across the population is one scenario in which even if there is some uncertainty regarding exactly how many cases or deaths vaccination will prevent, the fact that it is highly likely to decrease both, with few adverse effects, is motivation enough to decide that a large-scale vaccination program is a good idea.

An engineer is always updating their picture.

Susan Holmes

Engineers, Holmes points out, are also very good at breaking problems down into critical pieces, applying carefully selected tools, and optimizing for solutions under constraints. With a team of engineers building a bridge, there is a specialist in cement and a specialist in steel, a wind engineer and a structural engineer. “All the different specialties work together,” she says.

For Zelner, the notion of epidemiology as an engineering discipline is something he  picked up from his father, a mechanical engineer who started his own company designing health-care facilities. Drawing on a childhood full of building and fixing things, his engineering mindset involves tinkering—refining a transmission model, for instance, in response to a moving target.

“Often these problems require iterative solutions, where you’re making changes in response to what does or doesn’t work,” he says. “You continue to update what you’re doing as more data comes in and you see the successes and failures of your approach. To me, that’s very different—and better suited to the complex, non-stationary problems that define public health—than the kind of static one-and-done image a lot of people have of academic science, where you have a big idea, test it, and your result is preserved in amber for all time.” 

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AI and data fuel innovation in clinical trials and beyond

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AI and data fuel innovation in clinical trials and beyond


Laurel: So mentioning the pandemic, it really has shown us how critical and fraught the race is to provide new treatments and vaccines to patients. Could you explain what evidence generation is and then how it fits into drug development?

Arnaub: Sure. So as a concept, generating evidence in drug development is nothing new. It’s the art of putting together data and analyses that successfully demonstrate the safety and the efficacy and the value of your product to a bunch of different stakeholders, regulators, payers, providers, and ultimately, and most importantly, patients. And to date, I’d say evidence generation consists of not only the trial readout itself, but there are now different types of studies that pharmaceutical or medical device companies conduct, and these could be studies like literature reviews or observational data studies or analyses that demonstrate the burden of illness or even treatment patterns. And if you look at how most companies are designed, clinical development teams focus on designing a protocol, executing the trial, and they’re responsible for a successful readout in the trial. And most of that work happens within clinical dev. But as a drug gets closer to launch, health economics, outcomes research, epidemiology teams are the ones that are helping paint what is the value and how do we understand the disease more effectively?

So I think we’re at a pretty interesting inflection point in the industry right now. Generating evidence is a multi-year activity, both during the trial and in many cases long after the trial. And we saw this as especially true for vaccine trials, but also for oncology or other therapeutic areas. In covid, the vaccine companies put together their evidence packages in record time, and it was an incredible effort. And now I think what’s happening is the FDA’s navigating a tricky balance where they want to promote the innovation that we were talking about, the advancements of new therapies to patients. They’ve built in vehicles to expedite therapies such as accelerated approvals, but we need confirmatory trials or long-term follow up to really understand the evidence and to understand the safety and the efficacy of these drugs. And that’s why that concept that we’re talking about today is so important, is how do we do this more expeditiously?

Laurel: It’s certainly important when you’re talking about something that is life-saving innovations, but as you mentioned earlier, with the coming together of both the rapid pace of technology innovation as well as the data being generated and reviewed, we’re at a special inflection point here. So, how has data and evidence generation evolved in the last couple years, and then how different would this ability to create a vaccine and all the evidence packets now be possible five or 10 years ago?

Arnaub: It’s important to set the distinction here between clinical trial data and what’s called real-world data. The randomized controlled trial is, and has remained, the gold standard for evidence generation and submission. And we know within clinical trials, we have a really tightly controlled set of parameters and a focus on a subset of patients. And there’s a lot of specificity and granularity in what’s being captured. There’s a regular interval of assessment, but we also know the trial environment is not necessarily representative of how patients end up performing in the real world. And that term, “real world,” is kind of a wild west of a bunch of different things. It’s claims data or billing records from insurance companies. It’s electronic medical records that emerge out of providers and hospital systems and labs, and even increasingly new forms of data that you might see from devices or even patient-reported data. And RWD, or real-world data, is a large and diverse set of different sources that can capture patient performance as patients go in and out of different healthcare systems and environments.

Ten years ago, when I was first working in this space, the term “real-world data” didn’t even exist. It was like a swear word, and it was basically one that was created in recent years by the pharmaceutical and the regulatory sectors. So, I think what we’re seeing now, the other important piece or dimension is that the regulatory agencies, through very important pieces of legislation like the 21st Century Cures Act, have jump-started and propelled how real-world data can be used and incorporated to augment our understanding of treatments and of disease. So, there’s a lot of momentum here. Real-world data is used in 85%, 90% of FDA-approved new drug applications. So, this is a world we have to navigate.

How do we keep the rigor of the clinical trial and tell the entire story, and then how do we bring in the real-world data to kind of complete that picture? It’s a problem we’ve been focusing on for the last two years, and we’ve even built a solution around this during covid called Medidata Link that actually ties together patient-level data in the clinical trial to all the non-trial data that exists in the world for the individual patient. And as you can imagine, the reason this made a lot of sense during covid, and we actually started this with a covid vaccine manufacturer, was so that we could study long-term outcomes, so that we could tie together that trial data to what we’re seeing post-trial. And does the vaccine make sense over the long term? Is it safe? Is it efficacious? And this is, I think, something that’s going to emerge and has been a big part of our evolution over the last couple years in terms of how we collect data.

Laurel: That collecting data story is certainly part of maybe the challenges in generating this high-quality evidence. What are some other gaps in the industry that you have seen?

Arnaub: I think the elephant in the room for development in the pharmaceutical industry is that despite all the data and all of the advances in analytics, the probability of technical success, or regulatory success as it’s called for drugs, moving forward is still really low. The overall likelihood of approval from phase one consistently sits under 10% for a number of different therapeutic areas. It’s sub 5% in cardiovascular, it’s a little bit over 5% in oncology and neurology, and I think what underlies these failures is a lack of data to demonstrate efficacy. It’s where a lot of companies submit or include what the regulatory bodies call a flawed study design, an inappropriate statistical endpoint, or in many cases, trials are underpowered, meaning the sample size was too small to reject the null hypothesis. So what that means is you’re grappling with a number of key decisions if you look at just the trial itself and some of the gaps where data should be more involved and more influential in decision making.

So, when you’re designing a trial, you’re evaluating, “What are my primary and my secondary endpoints? What inclusion or exclusion criteria do I select? What’s my comparator? What’s my use of a biomarker? And then how do I understand outcomes? How do I understand the mechanism of action?” It’s a myriad of different choices and a permutation of different decisions that have to be made in parallel, all of this data and information coming from the real world; we talked about the momentum in how valuable an electronic health record could be. But the gap here, the problem is, how is the data collected? How do you verify where it came from? Can it be trusted?

So, while volume is good, the gaps actually contribute and there’s a significant chance of bias in a variety of different areas. Selection bias, meaning there’s differences in the types of patients who you select for treatment. There’s performance bias, detection, a number of issues with the data itself. So, I think what we’re trying to navigate here is how can you do this in a robust way where you’re putting these data sets together, addressing some of those key issues around drug failure that I was referencing earlier? Our personal approach has been using a curated historical clinical trial data set that sits on our platform and use that to contextualize what we’re seeing in the real world and to better understand how patients are responding to therapy. And that should, in theory, and what we’ve seen with our work, is help clinical development teams use a novel way to use data to design a trial protocol, or to improve some of the statistical analysis work that they do.

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Power beaming comes of age

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Power beaming comes of age


The global need for power to provide ubiquitous connectivity through 5G, 6G, and smart infrastructure is rising. This report explains the prospects of power beaming; its economic, human, and environmental implications; and the challenges of making the technology reliable, effective, wide-ranging, and secure.

The following are the report’s key findings:

Lasers and microwaves offer distinct approaches to power beaming, each with benefits and drawbacks. While microwave-based power beaming has a more established track record thanks to lower cost of equipment, laser-based approaches are showing promise, backed by an increasing flurry of successful trials and pilots. Laser-based beaming has high-impact prospects for powering equipment in remote sites, the low-earth orbit economy, electric transportation, and underwater applications. Lasers’ chief advantage is the narrow concentration of beams, which enables smaller trans- mission and receiver installations. On the other hand, their disadvantage is the disturbance caused by atmospheric conditions and human interruption, although there are ongoing efforts to tackle these deficits.

Power beaming could quicken energy decarbonization, boost internet connectivity, and enable post-disaster response. Climate change is spurring investment in power beaming, which can support more radical approaches to energy transition. Due to solar energy’s continuous availability, beaming it directly from space to Earth offers superior conversion compared to land-based solar panels when averaged over time. Electric transportation—from trains to planes or drones—benefits from power beaming by avoiding the disruption and costs caused by cabling, wiring, or recharge landings.

Beaming could also transfer power from remote renewables sites such as offshore wind farms. Other areas where power beaming could revolutionize energy solutions include refueling space missions and satellites, 5G provision, and post-disaster humanitarian response in remote regions or areas where networks have collapsed due to extreme weather events, whose frequency will be increased by climate change. In the short term, as efficiencies continue to improve, power beaming has the capacity to reduce the number of wasted batteries, especially in low-power, across-the- room applications.

Public engagement and education are crucial to support the uptake of power beaming. Lasers and microwaves may conjure images of death rays and unanticipated health risks. Public backlash against 5G shows the importance of education and information about the safety of new, “invisible” technologies. Based on decades of research, power beaming via both microwaves and lasers has been shown to be safe. The public is comfortable living amidst invisible forces like wi-fi and wireless data transfer; power beaming is simply the newest chapter.

Commercial investment in power beaming remains muted due to a combination of historical skepticism and uncertain time horizons. While private investment in futuristic sectors like nuclear fusion energy and satellites booms, the power-beaming sector has received relatively little investment and venture capital relative to the scale of the opportunity. Experts believe this is partly a “first-mover” problem as capital allocators await signs of momentum. It may be a hangover of past decisions to abandon beaming due to high costs and impracticality, even though such reticence was based on earlier technologies that have now been surpassed. Power beaming also tends to fall between two R&D comfort zones for large corporations: it does not deliver short-term financial gain, but it is also not long term enough to justify a steady financing stream.

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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The porcelain challenge didn’t need to be real to get views

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The porcelain challenge didn’t need to be real to get views


“I’ve dabbled in the past with trying to make fake news that is transparent about being fake but spreads nonetheless,” Durfee said. (He once, with a surprising amount of success, got a false rumor started that longtime YouTuber Hank Green had been arrested as a teenager for trying to steal a lemur from a zoo.)

On Sunday, Durfee and his friends watched as #PorcelainChallenge gained traction, and they celebrated when it generated its first media headline (“TikTok’s porcelain challenge is not real but it’s not something to joke about either”). A steady parade of other headlines, some more credulous than others, followed. 

But reflex-dependent viral content has a short life span. When Durfee and I chatted three days after he posted his first video about the porcelain challenge, he already could tell that it wasn’t going to catch as widely as he’d hoped. RIP. 

Nevertheless, viral moments can be reanimated with just the slightest touch of attention, becoming an undead trend ambling through Facebook news feeds and panicked parent groups. Stripping away their original context can only make them more powerful. And dubious claims about viral teen challenges are often these sorts of zombies—sometimes giving them a second life that’s much bigger (and arguably more dangerous) than the first.

For every “cinnamon challenge” (a real early-2010s viral challenge that made the YouTube rounds and put participants at risk for some nasty health complications), there are even more dumb ideas on the internet that do not trend until someone with a large audience of parents freaks out about them. 

Just a couple of weeks ago, for instance, the US Food and Drug Administration issued a warning about boiling chicken in NyQuil, prompting a panic over a craze that would endanger Gen Z lives in the name of views. Instead, as Buzzfeed News reported, the warning itself was the most viral thing about NyQuil chicken, spiking interest in a “trend” that was not trending.

And in 2018, there was the “condom challenge,” which gained widespread media coverage as the latest life-threatening thing teens were doing online for attention—“uncovered” because a local news station sat in on a presentation at a Texas school on the dangers teens face. In reality, the condom challenge had a few minor blips of interest online in 2007 and 2013, but videos of people actually trying to snort a condom up their nose were sparse. In each case, the fear of teens flocking en masse to take part in a dangerous challenge did more to amplify it to a much larger audience than the challenge was able to do on its own. 

The porcelain challenge has all the elements of future zombie content. Its catchy name stands out like a bite on the arm. The posts and videos seeded across social media by Durfee’s followers—and the secondary audience coming across the work of those Durfee deputized—are plausible and context-free. 

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