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“It became clear that we will not achieve herd immunity in 2021, definitely not across the country,” he says. “And I think it’s important, especially if you’re trying to build trust, to do it the smart way we can return to normal. We should not associate this with an unrealistic goal such as gaining immunity from herds. I’m still optimistic that the original predictions I made in February would return to normalcy in the summer would be valid. “
In early March, he filled the entire store, figuring out what contribution he could make. “I wanted to go back and let other modelers and experts do their job,” he says. “I don’t want to confuse space.”
It still observes data, conducts research and analyzes – variants, vaccine spread and the fourth wave. “If I see something that people aren’t particularly concerned about or talking about, I’ll definitely post it,” he says. But for now, it’s focusing on other projects, such as “YOLO Stocks, ”A stock ticker analysis platform. Her main pandemic work is as a member of the World Health Organization’s covid-19 technical advisory team on mortality assessment, and she reports on her outside experience.
“I’ve definitely learned a lot over the last year,” Guek says. “His eyes were wide open.”
Lesson 1: Pay attention to the basics
“From a data science perspective, my models have shown the importance of simplicity, which is often underestimated,” says Gu. The death prediction model was simple not only in its design – with a SEIR component with a machine learning layer – but also a “very bottom-up” approach to input data. From the bottom up it means “starting at least from the bare bones and adding complexity,” he says. “My model only uses past deaths to predict future deaths. It doesn’t use any other real data sources.”
We noticed that other models included cases such as hospitalization, testing, mobility, use of mask, combinations, age distribution, eclectic variety. demography, pneumonia seasonality, annual pneumonia death rate, population density, air pollution, altitude, smoking data, self-reported contacts, passenger air traffic, point of attention, smart thermometers, Facebook posts, Google searches, etc.
“If you add more data to the model or make it more sophisticated, it is believed that the model will do better,” he says. “But in real-world situations like a pandemic, when the data is so noisy, you want to keep things as simple as possible.”
“I initially decided that past deaths are the best predictors of future deaths. It’s very simple: input, output. Adding more data sources will make it harder to get the signal out of the noise.”
Lesson 2: Minimize hypotheses
Gu believes he had the advantage of tackling the problem with a blank slate. “My goal was to track data about the cobido to learn about the cobido,” he says. “That’s one of the main advantages of the outside view.”
But since he was not an epidemiologist, we also had to make sure that he did not make incorrect or inaccurate assumptions. “My job is to design the model for me to learn the hypotheses,” he says.
“When new data arrives that goes against our beliefs, sometimes we forget or ignore that new data, which can have an impact on the road,” he noted. “I was definitely a victim of that, and I know a lot of other people have it too.”
“So being aware of the potential bias we have and recognizing that and adjusting our priorities – adjusting our beliefs if we reject new data – is very important, especially in a fast-paced environment like we’ve seen.
Lesson 3: Test the hypothesis
“What I’ve seen in recent months is that anyone can manipulate claims or data to fit the narrative of what they want to believe,” Gue says. This highlights the importance of making only demonstrable hypotheses.
“For me, that’s the whole basis of my projections and predictions. I have a set of assumptions, and if those assumptions are true, that’s what we predict will happen in the future, “he says. if you have them, then there is no way to show that you are really right or wrong. “
Lesson 4: Learn from mistakes
“Not all of the projections I did were correct,” Guek says. In May 2020, it predicted 180,000 deaths in the U.S. by August. “That’s a lot bigger than what we saw,” he recalled. His provable hypothesis was wrong – “which forced me to adjust my beliefs.”
At the time, Gu had a fixed infection mortality rate of about 1% as a constant in the SEIR simulator. When the death rate of infections in the summer dropped to around 0.4% (and then to 0.7%), its projections returned to more realistic ranges.
Lesson 5: Involve critics
“Not everyone will agree with my ideas, and that’s welcome,” says Gu, who used Twitter to post his projections and analysis. “I try to respond to people as much as possible, defend my position and discuss it with people. It forces you to think about what your beliefs are and why you think they are correct. “
“It goes back to the confirmation bias,” he says. “If I am not able to defend my position properly, is it really a correct claim, and should I make those claims? Contacting other people helps me understand how to think about these issues. When other people present evidence that goes against my positions, I have to admit that I may be wrong in some of my assumptions. And that has helped me tremendously in improving my model. “
Lesson 6: Exercise healthy skepticism
“Now I’m much more skeptical of science, and it’s not bad,” Guek says. “I think it’s important to always question the results, but in a healthy way. It’s a fine line. Because a lot of people dismiss science as flat, and that’s also not the way to do that. ”
“But I think it’s important not to trust science blindly either,” he continues. “Scientists are not perfect.” He says it’s appropriate, if something doesn’t look right, to ask questions and find explanations. “It’s important to have different perspectives. If it’s something we’ve learned over the last year, 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 this past year. I have often been wrong. 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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