Can Computer Models Select the Best Public Health Interventions for COVID-19?

A new XPrize challenges simulators to go from forecasting case numbers to recommending policy

3 min read
covid-19 and statistics
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Many associate XPrize with a $10-million award offered in 1996 to motivate a breakthrough in private space flight. But the organization has since held other competitions related to exploration, ecology, and education. And in November, they launched the Pandemic Response Challenge, which will culminate in a $500,000 award to be split between two teams that not only best predict the continuing global spread of COVID-19, but also prescribe policies to curtail it.

“The whole point was to create a platform to create pandemic mitigation strategies based on evidence and science,” says Amir Banifatemi, XPrize’s chief innovation and growth officer. “But also to make the resulting insights available freely to everyone, in an open-source manner—especially for all those communities that may not have access to data and epidemiology divisions, statisticians, or data scientists.”

Pandemic predictions are hard enough, as we’ve seen with forecasting’s spotted track record over the past year. Prescriptions are harder still. Any non-pharmaceutical intervention (NPI), like closing schools and businesses, limiting travel, or establishing contact tracing, will be implemented differently in different areas; these interventions can also interact in surprising ways.

The XPrize Pandemic Response Challenge emerged from a paper posted to the preprint server arXiv in May 2020 by a team led by Risto Miikkulainen, a computer scientist at the University of Texas at Austin and associate vice president for evolutionary intelligence at Cognizant Technology Solutions, an IT and consulting company.

The paper, by Miikkulainen and colleagues at UT and Cognizant, lays out a way to go from prediction to prescription for COVID-19. As a first step, the team trained a neural network to predict new infections, using past data on infections and NPIs implemented. Then they created another neural net to serve as the prescriptor, taking in past infections and NPIs and outputting a new set of NPIs. To optimize the prescriptor, they created a whole population of prescriptors and used artificial evolution. They evaluated the prescriptors using the predictor as a surrogate for reality; in other words, based on the interventions prescribed, what would be the predicted effect on case numbers? The best performing prescriptors were kept, copied, and mutated.

Notably, evolution produced not a single good prescriptor but a set of them, each good in its own way. They were selected for their ability to minimize not just infections, but also interventions themselves—otherwise, they’d just prescribe total lockdowns, which have serious impacts on the economy and quality of life. Policymakers could theoretically look at the set of prescriptors and pick one, depending on how much they wanted to emphasize physical health or social and economic health.

Miikkulainen’s team placed an interactive demo online. “Amir [Banifatemi] saw that and figured that this would make a great XPrize,” Miikkulainen says. Suddenly, artificial intelligence and big data seemed capable of authoring useful policy recommendations. Cognizant is partnering with XPrize to run the challenge, and their code is offered to contestants as an optional starting point.

Some XPrizes span years. This one has a compressed schedule, for obvious reasons. There are two phases. For Phase 1, teams had to submit prediction models by 22 December. They were given data on infections and NPIs around the world (the NPI data came from the comprehensive Oxford COVID-19 Government Response Tracker), and the models are now being judged over a three-week period on how closely their predictions of new cases each day match reality across more than 200 regions (countries, U.S. states, and provinces of Canada and Brazil). Teams will also be judged qualitatively on factors such as innovation, model speed, prediction consistency, explanation, and collaboration with other teams.

Up to 50 teams will make it to Phase 2, where they must submit a prescription model. The best predictors from Phase 1 will be combined to evaluate the prescriptions in Phase 2. Prescriptors can offer up to 10 prescriptions per region per day, covering different infection-intervention tradeoffs. (The economic cost of each intervention will be given to the models. Of course, figuring out the real costs is a problem in itself.) Again, these will be evaluated both quantitatively and qualitatively. The top two teams will split half a million dollars.

The competition may not end there. XPrize’s Banifatemi says a third phase might test models on vaccine deployment prescriptions. And beyond the contest, some cities or countries might put some of the Phase 2 or 3 models into practice, if Banifatemi can find adventurous takers.

The organizers expect a wide variety of solutions. Banifatemi says the field includes teams from AI strongholds such as Stanford, Microsoft, MIT, Oxford, and Quebec’s Mila, but one team consists of three women in Tunisia. In all, 104 teams from 28 countries have registered.

“We’re hoping that this competition can be a springboard for developing solutions for other really big problems as well,” Miikkulainen says. Those problems include pandemics, global warming, and challenges in business, education, and healthcare. In this scenario, “humans are still in charge,” he emphasizes. “They still decide what they want, and AI gives them the best alternatives from which the decision-makers choose.”

But Miikkulainen hopes that data science can help humanity find its way. “Maybe in the future, it’s considered irresponsible not to use AI for making these policies,” he says.

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Andrew Ng: Unbiggen AI

The AI pioneer says it’s time for smart-sized, “data-centric” solutions to big issues

10 min read
​Andrew Ng listens during the Power of Data: Sooner Than You Think global technology conference in Brooklyn, New York, on Wednesday, October 30, 2019.

Andrew Ng was involved in the rise of massive deep learning models trained on vast amounts of data, but now he’s preaching small-data solutions.

Cate Dingley/Bloomberg/Getty Images

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

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