“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.”
The quest to show that biological sex matters in the immune system
She ultimately found a postdoctoral position in the lab of one of her thesis committee members. And in the years since, as she has established a lab of her own at the university’s Bloomberg School of Public Health, she has painstakingly made the case that sex—defined by biological attributes such as our sex chromosomes, sex hormones, and reproductive tissues—really does influence immune responses.
Through research in animal models and humans, Klein and others have shown how and why male and female immune systems respond differently to the flu virus, HIV, and certain cancer therapies, and why most women receive greater protection from vaccines but are also more likely to get severe asthma and autoimmune disorders (something that had been known but not attributed specifically to immune differences). “Work from her laboratory has been instrumental in advancing our understanding of vaccine responses and immune function on males and females,” says immunologist Dawn Newcomb of the Vanderbilt University Medical Center in Nashville, Tennessee. (When referring to people in this article, “male” is used as a shorthand for people with XY chromosomes, a penis, and testicles, and who go through a testosterone-dominated puberty, and “female” is used as a shorthand for people with XX chromosomes and a vulva, and who go through an estrogen-dominated puberty.)
Through her research, as well as the unglamorous labor of arranging symposia and meetings, Klein has helped spearhead a shift in immunology, a field that long thought sex differences didn’t matter. Historically, most trials enrolled only males, resulting in uncounted—and likely uncountable—consequences for public health and medicine. The practice has, for example, caused women to be denied a potentially lifesaving HIV therapy and left them likely to endure worse side effects from drugs and vaccines when given the same dose as men.
Men and women don’t experience infectious or autoimmune diseases in the same way. Women are nine times more likely to get lupus than men, and they have been hospitalized at higher rates for some flu strains. Meanwhile, men are significantly more likely to get tuberculosis and to die of covid-19 than women.
In the 1990s, scientists often attributed such differences to gender rather than sex—to norms, roles, relationships, behaviors, and other sociocultural factors as opposed to biological differences in the immune system.
For example, even though three times as many women have multiple sclerosis as men, immunologists in the 1990s ignored the idea that this difference could have a biological basis, says Rhonda Voskuhl, a neuroimmunologist at the University of California, Los Angeles. “People would say, ‘Oh, the women just complain more—they’re kind of hysterical,’” Voskuhl says. “You had to convince people that it wasn’t just all subjective or environmental, that it was basic biology. So it was an uphill battle.”
Despite a historical practice of “bikini medicine”—the notion that there are no major differences between the sexes outside the parts that fit under a bikini—we now know that whether you’re looking at your metabolism, heart, or immune system, both biological sex differences and sociocultural gender differences exist. And they both play a role in susceptibility to diseases. For instance, men’s greater propensity to tuberculosis—they are almost twice as likely to get it as women—may be attributed partly to differences in their immune responses and partly to the fact that men are more likely to smoke and to work in mining or construction jobs that expose them to toxic substances, which can impair the lungs’ immune defenses.
How to tease apart the effects of sex and gender? That’s where animal models come in. “Gender is a social construct that we associate with humans, so animals do not have a gender,” says Chyren Hunter, associate director for basic and translational research at the US National Institutes of Health Office of Research on Women’s Health. Seeing the same effect in both animal models and humans is a good starting point for finding out whether an immune response is modulated by sex.
Why can’t tech fix its gender problem?
Not competing in this Olympics, but still contributing to the industry’s success, were the thousands of women who worked in the Valley’s microchip fabrication plants and other manufacturing facilities from the 1960s to the early 1980s. Some were working-class Asian- and Mexican-Americans whose mothers and grandmothers had worked in the orchards and fruit canneries of the prewar Valley. Others were recent migrants from the East and Midwest, white and often college educated, needing income and interested in technical work.
With few other technical jobs available to them in the Valley, women would work for less. The preponderance of women on the lines helped keep the region’s factory wages among the lowest in the country. Women continue to dominate high-tech assembly lines, though now most of the factories are located thousands of miles away. In 1970, one early American-owned Mexican production line employed 600 workers, nearly 90% of whom were female. Half a century later the pattern continued: in 2019, women made up 90% of the workforce in one enormous iPhone assembly plant in India. Female production workers make up 80% of the entire tech workforce of Vietnam.
Venture: “The Boys Club”
Chipmaking’s fiercely competitive and unusually demanding managerial culture proved to be highly influential, filtering down through the millionaires of the first semiconductor generation as they deployed their wealth and managerial experience in other companies. But venture capital was where semiconductor culture cast its longest shadow.
The Valley’s original venture capitalists were a tight-knit bunch, mostly young men managing older, much richer men’s money. At first there were so few of them that they’d book a table at a San Francisco restaurant, summoning founders to pitch everyone at once. So many opportunities were flowing it didn’t much matter if a deal went to someone else. Charter members like Silicon Valley venture capitalist Reid Dennis called it “The Group.” Other observers, like journalist John W. Wilson, called it “The Boys Club.”
The venture business was expanding by the early 1970s, even though down markets made it a terrible time to raise money. But the firms founded and led by semiconductor veterans during this period became industry-defining ones. Gene Kleiner left Fairchild Semiconductor to cofound Kleiner Perkins, whose long list of hits included Genentech, Sun Microsystems, AOL, Google, and Amazon. Master intimidator Don Valentine founded Sequoia Capital, making early-stage investments in Atari and Apple, and later in Cisco, Google, Instagram, Airbnb, and many others.
Generations: “Pattern recognition”
Silicon Valley venture capitalists left their mark not only by choosing whom to invest in, but by advising and shaping the business sensibility of those they funded. They were more than bankers. They were mentors, professors, and father figures to young, inexperienced men who often knew a lot about technology and nothing about how to start and grow a business.
“This model of one generation succeeding and then turning around to offer the next generation of entrepreneurs financial support and managerial expertise,” Silicon Valley historian Leslie Berlin writes, “is one of the most important and under-recognized secrets to Silicon Valley’s ongoing success.” Tech leaders agree with Berlin’s assessment. Apple cofounder Steve Jobs—who learned most of what he knew about business from the men of the semiconductor industry—likened it to passing a baton in a relay race.
Predicting the climate bill’s effects is harder than you might think
Human decision-making can also cause models and reality to misalign. “People don’t necessarily always do what is, on paper, the most economic,” says Robbie Orvis, who leads the energy policy solutions program at Energy Innovation.
This is a common issue for consumer tax credits, like those for electric vehicles or home energy efficiency upgrades. Often people don’t have the information or funds needed to take advantage of tax credits.
Likewise, there are no assurances that credits in the power sectors will have the impact that modelers expect. Finding sites for new power projects and getting permits for them can be challenging, potentially derailing progress. Some of this friction is factored into the models, Orvis says. But there’s still potential for more challenges than modelers expect.
Putting too much stock in results from models can be problematic, says James Bushnell, an economist at the University of California, Davis. For one thing, models could overestimate how much behavior change is because of tax credits. Some of the projects that are claiming tax credits would probably have been built anyway, Bushnell says, especially solar and wind installations, which are already becoming more widespread and cheaper to build.
Still, whether or not the bill meets the expectations of the modelers, it’s a step forward in providing climate-friendly incentives, since it replaces solar- and wind-specific credits with broader clean-energy credits that will be more flexible for developers in choosing which technologies to deploy.
Another positive of the legislation is all its long-term investments, whose potential impacts aren’t fully captured in the economic models. The bill includes money for research and development of new technologies like direct air capture and clean hydrogen, which are still unproven but could have major impacts on emissions in the coming decades if they prove to be efficient and practical.
Whatever the effectiveness of the Inflation Reduction Act, however, it’s clear that more climate action is still needed to meet emissions goals in 2030 and beyond. Indeed, even if the predictions of the modelers are correct, the bill is still not sufficient for the US to meet its stated goals under the Paris agreement of cutting emissions to half of 2005 levels by 2030.
The path ahead for US climate action isn’t as certain as some might wish it were. But with the Inflation Reduction Act, the country has taken a big step. Exactly how big is still an open question.