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Monday, April 27, 2020


Youyang Gu

He made a machine-learning model in a week and ran it daily on his laptop (it only took an hour), generating remarkably accurate covid-19 predictions.

April 27, 2021
Youyang Gu

The data scientist Youyang Gu thinks of himself as a realist—he declares it in his Twitter profile: “Presenter of unbiased takes. Realist.”

When he noticed the scattershot covid-19 projections last spring—one model projected 2 million US deaths by the summer, another predicted 60,000—Gu questioned whether that was as good as the modeling could be. He decided to take a shot at making a covid-19 model himself. “My whole entire goal was to produce the most accurate model possible,” Gu says, from his apartment in Manhattan. “No ‘if this’ or ‘if that.’ Basically, no ‘ifs.’ It doesn’t really matter what the scenarios are. I just wanted to lay it out: ‘This is the most likely or realistic forecast for what’s going to happen.’”

Within a week, he’d built a machine-learning model and launched his COVID-19 Projections website. He ran the model every day—it only took one hour on his laptop—and posted covid-19 death projections for 50 US states, 34 counties, and 71 countries.

By the end of April, he was attracting attention—ultimately, millions checked his website daily. Carl Bergstrom, a professor of biology at the University of Washington, took notice and commented on Twitter that Gu’s model was “making predictions that seem as good as any I’ve seen.”

“I can be a bit of an ML skeptic. But in this case, don’t let the ‘machine learning’ text fool you into thinking this is snake oil,” Bergstrom tweeted.

An MIT grad with a master’s degree in electrical engineering and computer science (plus a degree in math), Gu, 27, had been working on a sports analytics startup when the pandemic hit. But he put that venture on pause as major league sports shut down. And then, by simply googling “epidemiology,” he began his foray into covid-19 modeling.

“I had zero background in infectious-disease modeling,” he says. But he did have a few years’ experience as a data scientist in finance, working with statistical models—models that, based on certain statistical assumptions, analyze data and make projections about, say, where the price of a stock will be in the future.

“It turns out that a lot of infectious-disease modeling is basically statistical modeling,” says Gu. And the finance industry’s profit-driven goal for accuracy served him well in the epidemiological domain. “If you can’t make an accurate model in finance, you won’t have a job anymore,” he says. By contrast, the goal in academia—from Gu’s perspective, at least—is not so much to make accurate models, but rather to publish papers and inform public policy. “That’s not to say they don’t make accurate models—just that they don’t optimize specifically for accuracy,” he says.

Gu’s model combines machine learning with a classic infectious-disease simulator called an SEIR model (factoring in individuals in the population who are susceptible, exposed, infectious, recovered, or removed due to death).

The SEIR component uses as input a simulated set of parameters—a best-guess range for variables such as the basic reproduction number (the rate at which new cases arise in an entirely susceptible population at the start of an outbreak, before interventions or immunity), infection rate, lockdown date, reopening date, and effective reproduction number (the rate at which new cases arise after some interventions). In terms of outputs, the SEIR simulator first computes the infections over time, and then computes the deaths (multiplying infections by the infection fatality rate).

Gu’s machine-learning layer then generates thousands of different combinations for those parameter sets in trying to find the real-life parameters for each geographical region. It learns which parameters generate the most accurate death projections by comparing the SEIR predictions with real data on daily deaths from Johns Hopkins University. “It tries to learn what parameter sets generate deaths that most closely match the actual observed data, looking back,” says Gu. “And then it uses those parameters to forecast and make projections about deaths into the future.”

The forecasts proved remarkably accurate. For instance, on May 3, he made an appearance on CNN Tonight and shared his model’s projections that the US would reach 70,000 deaths on May 5, 80,000 deaths on May 11, 90,000 deaths on May 18, and 100,000 deaths on May 27. On May 28, he tweeted, “covid19-projections.com got all 4 dates exactly correct.” With some rounding, that was true.

“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.”

Youyang Gu

The model wasn’t perfect, of course, but it impressed Nicholas Reich, a biostatistician and infectious-disease researcher at the University of Massachusetts, Amherst, whose lab, in collaboration with the US Centers for Disease Control and Prevention, aggregates results from about 100 international modeling teams. Among all the aggregated models, Reich observed, Gu’s model was “consistently among the top.”

On October 6, Gu posted his final death forecast, just before the fall wave. The model projected there would be 231,000 deaths in the US by November 1. The total recorded by that date: 230,995.

Gu shut down his first model in early October because by then there were lots of teams doing good death forecasts. He turned instead to modeling true infections versus reported infections. And then in December he started tracking vaccine rollout and the elusive “path to herd immunity”—which in early 2021 he revised to “path to normality.” Whereas herd immunity is achieved when a sufficient portion of a population is immune to the virus, thus curtailing further spread, Gu defines normality as “the lifting of all covid-19-related restrictions for the majority of US states.”

“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.”

Sunday, April 26, 2020

in any country connected by 4g= tech, it shouldnt have taken a virus to accelerate telemedicine/tirage-lets make sure the new normal never goes back to hi cost hi infection waiting rooms

10 years ago obama sponsored a state to maximise telemedicine but failed to market their good news to  other states where all the usual vested interests of making americas health service twice as costly and lastmile random blocked freedom of speech

kim is back


for 15 months kim left the world of last mile health, virus fighting, valuing youth as core to sdg economics - world bank 2012-2019 jan , www.pih.org boston to work on infrastructure investment

lets get back to mapping action collaborations at www.kimuniversity.com

Thursday, April 16, 2020

christopher macrae
virus demonstates sustainability of our species cannot afford the constitutional anachronisms of eu and usa Coronavirus may be the spark Western governments needed to reverse decades of decline bloomberg.com/opinion/articl via
collapsing almost every exponential timeline of our 1984 book the 2025 report on how to avoid orwell's endgame - always one of the 2 possibilities for 265 year of man and machines stared by smith/watt glasgow uni 1760
what massive coalitions in trusting youth to be the sdg generation are left???

-do you know of a coalition worth mapping in this last decade of moon races bach to sustaining our species - all eyes at chris.macrae@yahoo.co.uk bethesda md usa

Wednesday, April 15, 2020

bill and melinda gates who have been working on virus through years trump reduced fubding - back who - see abc now

see also cbs lenghi - will economic fallout be greater than virus- los angeles to ban spors to mid 2021- nj governoir says new normal wont be old normal- so does cuomo - heroes mount sinai/beth israel -nb while over 9000 medical workers got virus so far only 27 died - that is less than the death rate of eldes from other flus

this is a very targeted flu
death rates - ecven zero symptoms of youth who dont smoke and are not 0bese are minimal- almost zero among school-age children- death rates of over 80s or those with organic illnesses as frail as over 80 are maximised

it was trump the cdc/nih/fda that abused funding or created imperfect testing versus eg korea

trumps' forner bitch faisi fails to admit his so called victor 70000 deaths instead of 100000 deaths was based on locking in all americans to end may - he has no models pof loss of life due to contractionb of economy by 20% due to his lack of 2 types of data- infection tesing, antibody testing

fauci has perfect strategy of multiple waves - which will ultimately cause more deaths  (health and economic) -see rose garden convesation - attack on both who and wto

trump starts plantingjohn roberts fox and fascist aussie journalost
it is clamed rose garden white house 6.24 that mayo clinic and red cross and 1000 partners are collecting blood from survivors of the virus to support war (cue, innoculation) verus virus

in same speech spence (goebels had nothing on me) admits barely any patients at mercy and javeed centres ny, so army medics will be relocated
App-based contact tracing may help end coronavirus lockdowns

But only as part of a bigger system
Editor’s note: The Economist is making some of its most important coverage of the covid-19 pandemic freely available to readers of The Economist Today, our daily newsletter. To receive it, register here. For our coronavirus tracker and more coverage, see our hub
ON APRIL 10TH Apple and Google did something unusual: they announced plans to work together. These two firms exert varying degrees of control over almost every operational smartphone on the planet—Apple through its production of both iPhones and the software that runs them, and Google thanks to a range of programs found in nearly all of the iPhone’s Android-powered rivals. As a result, the two companies have access to a planet-spanning network of sensors and computing power some 3.5bn devices strong. Their plan is to combine their assets to assist the tracking of the covid-19 pandemic.

America, Britain, Germany, Ireland and many other countries were already building apps to track infection. They will now rewrite their software to take advantage of this new arrangement. These apps will work by broadcasting, from each phone they are installed on, a string of numbers and letters unique to that handset. These broadcasts will be detectable by any other phone within Bluetooth range (about nine metres) that has the same app installed. An app will also, simultaneously, listen for strings that other phones are broadcasting. Each phone carrying such an app will record all the character strings it hears, and thus all the phones it has been close to. For reasons of security (and because Apple’s and Google’s underlying cryptographic protocols demand it), the string of characters a phone broadcasts will change every 15 minutes. Also, at least to start with, the records of strings received will be stored only on the receiving phone. That makes hacking or abusing the system hard.
If, however, a phone-user develops symptoms and then tests positive for covid-19, this arrangement changes. Different strings of characters—one for each day that the person in question was potentially infectious—are now broadcast by the authorities to every other app in the network. These strings, which Apple calls diagnosis keys, command all apps so contacted to search records collected since that person’s putative time of infection for signs of proximity to the infected individual’s phone.
Blessed are the appmakers
What happens when a match is found is up to whoever deployed the app. A good response, though, would be to notify the person of interest, and ask him or her to get in touch and arrange to be tested. This way, infections will be detected quickly, and infected individuals offered suitable advice—and possibly quarantined.It all sounds like high-tech wizardry. And it is. But it is important not to get carried away. Smartphone contact tracing is just one part of a broader infrastructure that must be built to track down SARS-CoV-2 faster than it can spread through the population. It will not, for instance, be worth much unless ways of testing and diagnosing people en masse are also rolled out. Without these, there will be no information to feed back into the app network about who may be spreading the virus.
Ideally, such infrastructure will be built around testing stations that people can visit to have their noses and throats swabbed. Countries would in any case be well advised to construct these facilities, even if they do not deploy contact-tracing apps. Indeed, one option for ending the lockdowns many places are experiencing is to be able to test everyone so frequently that the authorities could be sure the virus was not spreading. This would be expensive, though, and deeply unpleasant (think having a Q-tip shoved up your nose once a week for the next two years). Contact tracing helps to direct testing more precisely at those likely to be infected. Using apps helps speed this up.
But only, though, if phone users are willing to adopt the app. Here, Singapore’s experience is salutary. Its government rolled out a contact-tracing app, TraceTogether, on March 20th. So far, however, this has been downloaded by only a sixth of Singapore’s population—barely a quarter of the 60% epidemiologists reckon is needed if it is to be effective at breaking the local epidemic. Perhaps the most used contact-tracing app in the world is that deployed by Iceland. Yet Rakning C-19 (“Rakning” is Icelandic for “tracking”) is used by only 40% of the country’s 364,000 people. If such a small, homogenous place cannot reach the required 60% download rate, what hope is there for large, diverse ones like America?
If tracing apps are to be widely adopted, they must make people want to use them, says Ciro Cattuto, an epidemiologist at the University of Turin, in Italy. “People need to feel like they are contributing to a common good,” he observes. “They need to feel empowered.” Maintaining public trust will be crucial. Since any such app will need to be updated as the situation develops, that trust can be maintained only by extreme transparency, Dr Cattuto says. This means no function creep.It is also important not to invest too much in the idea that automation is everything. Apps and phones can certainly provide location and proximity data, but only human tracers can bring human intelligence to bear on the matter. For example, in late January Taiwan’s contact-tracing team successfully used a mixture of data from the country’s national-health-insurance system and its mobile-phone firms to track down the source of infection for the island’s first covid-19 death—the unlucky taxi driver had picked up a Chinese businessman at the airport. They did this without resort to Bluetooth tracking apps—albeit that their ability to scrutinise the data they needed required the invocation of national-emergency powers.
As well as developing high-tech networks for tracking infection, information-technology firms should therefore also be writing software that improves the productivity of human contact-tracers like Taiwan’s. Interview forms for potential contacts, visualisation dashboards for relevant data, telemedicine for remote diagnostics—all these would be useful. Apps built using Apple’s and Google’s new protocol ought to focus on providing information to technologically empowered human contact-tracing teams, not on automating the whole process.
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