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covid-19 modeling, Youyang Gu, machine learning, data science

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covid-19 modeling, Youyang Gu, machine learning, data science


“It became clear that we’re not going to reach herd immunity in 2021, at least definitely not across the whole country,” he says. “And I think it’s important, especially if you’re trying to instill confidence, that we make sensible paths to when we can go back to normal. We shouldn’t be pegging that on an unrealistic goal like reaching herd immunity. I’m still cautiously optimistic that my original forecast in February, for a return to normal in the summer, will be valid.”

In early March, he packed up shop entirely—he figured he’d made what contribution he could. “I wanted to step back and let the other modelers and experts do their work,” he says. “I don’t want to muddle the space.”

He’s still keeping an eye on the data, doing research and analysis—on the variants, the vaccine rollout, and the fourth wave. “If I see anything that’s particularly troubling or worrisome that I think people aren’t talking about, I’ll definitely post it,” he says. But for the time being he is focusing on other projects, such as “YOLO Stocks,” a stock ticker analytics platform. His main pandemic work is as a member of the World Health Organization’s technical advisory group on covid-19 mortality assessment, where he shares his outsider’s expertise.

“I’ve definitely learned a lot this past year,” Gu says. “It was very eye-opening.”

Lesson #1: Focus on fundamentals

“From the data science perspective, my models have shown the importance of simplicity, which is often undervalued,” says Gu. His death forecasting model was simple in not only its design—the SEIR component with a machine-learning layer—but also its very pared-down, “bottom-up” approach regarding input data. Bottom-up means “start from the bare-bones minimum and add complexity as needed,” he says. “My model only uses past deaths to predict future deaths. It doesn’t use any other real data source.”

Gu noticed that other models drew on an eclectic variety data about cases, hospitalizations, testing, mobility, mask use, comorbidities, age distribution, demographics, pneumonia seasonality, annual pneumonia death rate, population density, air pollution, altitude, smoking data, self-reported contacts, airline passenger traffic, point of care, smart thermometers, Facebook posts, Google searches, and more.

“There is this belief that if you add more data to the model, or make it more sophisticated, then the model will do better,” he says. “But in real-word situations like the pandemic, where data is so noisy, you want to keep things as simple as possible.”

“I decided early on that past deaths are the best predictor of future deaths. It’s very simple: input, output. Adding more data sources will just make it more difficult to extract the signal from the noise.”

Lesson #2: Minimize assumptions

Gu considers that he had an advantage in approaching the problem with a blank slate. “My goal was to just follow the data on covid to learn about covid,” he says. “That’s one of the main benefits of an outsider’s perspective.”

But not being an epidemiologist, Gu also had to be sure that he wasn’t making incorrect or inaccurate assumptions. “My role is to design the model such that it can learn the assumptions for me,” he says.

“When new data comes along that goes against our beliefs, sometimes we tend to overlook that new data or ignore it, and that can cause repercussions down the road,” he notes. “I certainly found myself falling victim to that, and I know that lots of other people have as well.”

“So being aware of the potential bias that we have and recognizing it, and being able to adjust our priors—adjusting our beliefs if new data disproves them—is really important, especially in a fast-moving environment like what we’ve seen with covid.”

Lesson #3: Test the hypothesis

“What I’ve seen over the last few months is that anyone can make claims or manipulate data to fit the narrative of what they want to believe in,” Gu says. This highlights the importance of simply making testable hypotheses.

“For me, that is the whole basis of my projections and forecasts. I have a set of assumptions, and if those assumptions are true, then this is what we predict will happen in the future,” he says. “And if the assumptions end up being wrong, then of course we have to admit that the assumptions we make are not true and adjust accordingly. If you don’t make testable hypotheses, then there is no way to show whether you are actually right or wrong.”

Lesson #4: Learn from mistakes

“Not all the projections that I made were correct,” Gu says. In May 2020, he projected 180,000 deaths in the US by August. “That is much higher than we saw,” he recalls. His testable hypothesis proved incorrect—“and that forced me to adjust my assumptions.”

At the time, Gu was using a fixed infection fatality rate of approximately 1% as a constant in the SEIR simulator. When in the summer he lowered the infection fatality rate to about 0.4% (and later to about 0.7%), his projections returned to a more realistic range. 

Lesson #5: Engage critics

“Not everyone will agree with my ideas, and I welcome that,” says Gu, who used Twitter to post his projections and analysis. “I try to respond to people as much as I can, and defend my position, and debate with people. It forces you to think about what your assumptions are and why you think they are correct.”

“It goes back to confirmation bias,” he says. “If I am not able to properly defend my position, then is it really the right claim, and should I be making these claims? It helps me understand, by engaging with other people, how to think about these problems. When other people present evidence that counters my positions, I have to be able to acknowledge when I may be incorrect in some of my assumptions. And that has actually helped me tremendously in improving my model.”

Lesson #6: Exercise healthy skepticism

“I am now much more skeptical of science—and it’s not a bad thing,” Gu says. “I think it’s important to always question results, but in a healthy way. It’s a fine line. Because a lot of people just flat-out reject science, and that’s not the way to go about it either.”

“But I think it’s also important to not just blindly trust science,” he continues. “Scientists aren’t perfect.” It is appropriate, he says, if something doesn’t seem right, to ask questions and find explanations. “It’s important to have different perspectives. If there is anything we’ve learned over the past year, it’s that no one is 100% right all the time.”

“I can’t speak for all scientists, but my job is to cut through all the noise and get to the truth,” he says. “I’m not saying I’ve been perfect over this past year. I’ve been wrong many times. But I think we can all learn to approach science as a method of finding the truth, rather than the truth itself.”

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Your microbiome ages as you do—and that’s a problem

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Your microbiome ages as you do—and that’s a problem


These ecosystems appear to change as we age—and these changes can potentially put us at increased risk of age-related diseases. So how can we best look after them as we get old? And could an A-grade ecosystem help fend off diseases and help us lead longer, healthier lives?

It’s a question I’ve been pondering this week, partly because I know a few people who have been put on antibiotics for winter infections. These drugs—lifesaving though they can be—can cause mass destruction of gut microbes, wiping out the good along with the bad. How might people who take them best restore a healthy ecosystem afterwards?

I also came across a recent study in which scientists looked at thousands of samples of people’s gut microbe populations to see how they change with age. The standard approach to working out what microbes are living in a person’s gut is to look at feces. The idea is that when we have a bowel movement, we shed plenty of gut bacteria. Scientists can find out which species and strains of bacteria are present to get an estimate of what’s in your intestines.

In this study, a team based at University College Cork in Ireland analyzed data that had already been collected from 21,000 samples of human feces. These had come from people all over the world, including Europe, North and South America, Asia, and Africa. Nineteen nationalities were represented. The samples were all from adults between 18 and 100. 

The authors of this study wanted to get a better handle on what makes for a “good” microbiome, especially as we get older. It has been difficult for microbiologists to work this out. We do know that some bacteria can produce compounds that are good for our guts. Some seem to aid digestion, for example, while others lower inflammation.
 
But when it comes to the ecosystem as a whole, things get more complicated. At the moment, the accepted wisdom is that variety seems to be a good thing—the more microbial diversity, the better. Some scientists believe that unique microbiomes also have benefits, and that a collection of microbes that differs from the norm can keep you healthy.
 
The team looked at how the microbiomes of younger people compared with those of older people, and how they appeared to change with age. The scientists also looked at how the microbial ecosystems varied with signs of unhealthy aging, such as cognitive decline, frailty, and inflammation.
 
They found that the microbiome does seem to change with age, and that, on the whole, the ecosystems in our guts do tend to become more unique—it looks as though we lose aspects of a general “core” microbiome and stray toward a more individual one.
 
But this isn’t necessarily a good thing. In fact, this uniqueness seems to be linked to unhealthy aging and the development of those age-related symptoms listed above, which we’d all rather stave off for as long as possible. And measuring diversity alone doesn’t tell us much about whether the bugs in our guts are helpful or not in this regard.
 
The findings back up what these researchers and others have seen before, challenging the notion that uniqueness is a good thing. Another team has come up with a good analogy, which is known as the Anna Karenina principle of the microbiome: “All happy microbiomes look alike; each unhappy microbiome is unhappy in its own way.”
 
Of course, the big question is: What can we do to maintain a happy microbiome? And will it actually help us stave off age-related diseases?
 
There’s plenty of evidence to suggest that, on the whole, a diet with plenty of fruit, vegetables, and fiber is good for the gut. A couple of years ago, researchers found that after 12 months on a Mediterranean diet—one rich in olive oil, nuts, legumes, and fish, as well as fruit and veg—older people saw changes in their microbiomes that might benefit their health. These changes have been linked to a lowered risk of developing frailty and cognitive decline.
 
But at the individual level, we can’t really be sure of the impact that changes to our diets will have. Probiotics are a good example; you can chug down millions of microbes, but that doesn’t mean that they’ll survive the journey to your gut. Even if they do get there, we don’t know if they’ll be able to form niches in the existing ecosystem, or if they might cause some kind of unwelcome disruption. Some microbial ecosystems might respond really well to fermented foods like sauerkraut and kimchi, while others might not.
 
I personally love kimchi and sauerkraut. If they do turn out to support my microbiome in a way that protects me against age-related diseases, then that’s just the icing on the less-microbiome-friendly cake.

To read more, check out these stories from the Tech Review archive:
 
At-home microbiome tests can tell you which bugs are in your poo, but not much more than that, as Emily Mullin found.
 
Industrial-scale fermentation is one of the technologies transforming the way we produce and prepare our food, according to these experts.
 
Can restricting your calorie intake help you live longer? It seems to work for monkeys, as Katherine Bourzac wrote in 2009. 
 
Adam Piore bravely tried caloric restriction himself to find out if it might help people, too. Teaser: even if you live longer on the diet, you will be miserable doing so. 

From around the web:

Would you pay $15,000 to save your cat’s life? More people are turning to expensive surgery to extend the lives of their pets. (The Atlantic)
 
The World Health Organization will now start using the term “mpox” in place of “monkeypox,” which will be phased out over the next year. (WHO)
 
After three years in prison, He Jiankui—the scientist behind the infamous “CRISPR babies”—is attempting a comeback. (STAT)
 
Tech that allows scientists to listen in on the natural world is revealing some truly amazing discoveries. Who knew that Amazonian sea turtles make more than 200 distinct sounds? And that they start making sounds before they even hatch? (The Guardian)
 
These recordings provide plenty of inspiration for musicians. Whale song is particularly popular. (The New Yorker)
 
Scientists are using tiny worms to diagnose pancreatic cancer. The test, launched in Japan, could be available in the US next year. (Reuters)

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The Download: circumventing China’s firewall, and using AI to invent new drugs

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The Download: circumventing China’s firewall, and using AI to invent new drugs


As protests against rigid covid control measures in China engulfed social media in the past week, one Twitter account has emerged as the central source of information: @李老师不是你老师 (“Teacher Li Is Not Your Teacher”). 

People everywhere in China have sent protest footage and real-time updates to the account through private messages, and it has posted them, with the sender’s identity hidden, on their behalf.

The man behind the account, Li, is a Chinese painter based in Italy, who requested to be identified only by his last name in light of the security risks. He’s been tirelessly posting footage around the clock to help people within China get information, and also to inform the wider world.

The work has been taking its toll—he’s received death threats, and police have visited his family back in China. But it also comes with a sense of liberation, Li told Zeyi Yang, our China reporter. Read the full story.

Biotech labs are using AI inspired by DALL-E to invent new drugs

The news: Text-to-image AI models like OpenAI’s DALL-E 2—programs trained to generate pictures of almost anything you ask for—have sent ripples through the creative industries. Now, two biotech labs are using this type of generative AI, known as a diffusion model, to conjure up designs for new types of protein never seen in nature.

Why it matters: Proteins are the fundamental building blocks of living systems. These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function. In effect, this makes it possible to come up with new proteins to do particular jobs on demand. Researchers hope that this will eventually lead to the development of new and more effective drugs. Read the full story.

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The Blue Technology Barometer 2022/23

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The Blue Technology Barometer 2022/23


Overall ranking

Pillars

Comparative

The overall rankings tab shows the performance of the examined
economies relative to each other and aggregates scores generated
across the following four pillars: ocean environment, marine activity,
technology innovation, and policy and regulation.

This pillar ranks each country according to its levels of
marine water contamination, its plastic recycling efforts, the
CO2 emissions of its marine activities (relative to the size
of its economy), and the recent change of total emissions.

This pillar ranks each country on the sustainability of its
marine activities, including shipping, fishing, and protected
areas.

This pillar ranks each country on its contribution to ocean
sustainable technology research and development, including
expenditure, patents, and startups.

This pillar ranks each country on its stance on ocean
sustainability-related policy and regulation, including
national-level policies, taxes, fees, and subsidies, and the
implementation of international marine law.

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Experts

MIT Technology Review Insights would like to thank the following
individuals for their time, perspective, and insights:

  • Valérie Amant, Director of Communications, The SeaCleaners
  • Charlotte de Fontaubert, Global Lead for the Blue Economy, World Bank Group
  • Ian Falconer, Founder, Fishy Filaments
  • Ben Fitzgerald, Managing Director, CoreMarine
  • Melissa Garvey, Global Director of Ocean Protection, The Nature Conservancy
  • Michael Hadfield, Emeritus Professor, Principal Investigator, Kewalo Marine Laboratory, University of Hawaii
    at Mānoa
  • Takeshi Kawano, Executive Director, Japan Agency for Marine-Earth Science and Technology
  • Kathryn Matthews, Chief Scientist, Oceana
  • Alex Rogers, Science Director, REV Ocean
  • Ovais Sarmad, Deputy Executive Secretary, United Nations Framework Convention on Climate Change
  • Thierry Senechal, Managing Director, Finance for Impact
  • Jyotika Virmani, Executive Director, Schmidt Ocean Institute
  • Lucy Woodall, Associate Professor of Marine Biology, University of Oxford, and Principal Scientist at Nekton
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About

Methodology: The Blue Technology Barometer 2022/23

Now in its second year, the Blue Technology Barometer assesses and ranks how each of the world’s largest
maritime economies promotes and develops blue (marine-centered) technologies that help reverse the impact of
climate change on ocean ecosystems, and how they leverage ocean-based resources to reduce greenhouse gases and
other effects of climate change.

To build the index, MIT Technology Review Insights compiled 20 quantitative and qualitative data indicators
for 66 countries and territories with coastlines and maritime economies. This included analysis of select
datasets and primary research interviews with global blue technology innovators, policymakers, and
international ocean sustainability organizations. Through trend analysis, research, and a consultative
peer-review process with several subject matter experts, weighting assumptions were assigned to determine the
relative importance of each indicator’s influence on a country’s blue technology leadership.

These indicators measure how each country or territory’s economic and maritime industries have affected its
marine environment and how quickly they have developed and deployed technologies that help improve ocean
health outcomes. Policy and regulatory adherence factors were considered, particularly the observance of
international treaties on fishing and marine protection laws.

The indicators are organized into four pillars, which evaluate metrics around a sustainability theme. Each
indicator is scored from 1 to 10 (10 being the best performance) and is weighted for its contribution to its
respective pillar. Each pillar is weighted to determine its importance in the overall score. As these research
efforts center on countries developing blue technology to promote ocean health, the technology pillar is
ranked highest, at 50% of the overall score.

The four pillars of the Blue Technology Barometer are:

Carbon emissions resulting from maritime activities and their relative growth. Metrics in this pillar also
assess each country’s efforts to mitigate ocean pollution and enhance ocean ecosystem health.

Efforts to promote sustainable fishing activities and increase and maintain marine protected areas.

Progress in fostering the development of sustainable ocean technologies across several relevant fields:

  • Clean innovation scores from MIT Technology Review Insights’ Green Future Index 2022.
  • A tally of maritime-relevant patents and technology startups.
  • An assessment of each economy’s use of technologies and tech-enabled processes that facilitate ocean
    sustainability.

Commitment to signing and enforcing international treaties to promote ocean sustainability and enforce
sustainable fishing.

About Us

MIT Technology Review was founded at the Massachusetts Institute of Technology in 1899. MIT Technology Review
Insights is the custom publishing division of MIT Technology Review. We conduct qualitative and quantitative
research and analysis worldwide and publish a wide variety of content, including articles, reports,
infographics, videos, and podcasts.

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