AI experts convened to discuss how to combat the coronavirus crisis.

What’s new: An online conference hosted by Stanford University’s Institute for Human-Centered AI explored how machine learning is being deployed to address this pandemic — and prepare for the next one. You can watch the video here.

The agenda: Nearly two dozen experts in fields as diverse as machine learning and public health delivered updates on topics from containing the pandemic to finding treatments. A few notable presentations from the six-hour conference:

  • Epidemiology: Given the many people who fall ill but don’t enter a hospital, not to mention those who never show symptoms, it can be maddeningly difficult to track how the disease spreads and how many it kills. Lucy Li of the Chan Zuckerberg BioHub used a branching model to estimate how many people have been infected. Each time a virus infects a host, it mutates slightly. Analyzing viral DNA extracted from each known patient, the model uses the rate of mutation to interpolate how many other people the virus passed through along the way. According to Li’s estimates, 87 percent of all Chinese cases and 95 percent of total cases have gone undetected.
  • Social Distancing: Stanford pediatrician C. Jason Wang described how Taiwan’s Central Epidemic Command Center tracked individual Covid-19 cases and enforced social distancing rules. Activated in response to the epidemic, the data analytics hub uses GPS, health insurance records, and immigration data to track infections and alert individuals who may have been exposed.
  • Treatment: Creating new drugs from scratch takes a lot of time. So Stefano Rensi and colleagues in Stanford’s Department of Bioengineering searched for existing compounds to fight Covid-19. They used natural language processing to sift the medical literature for clues about how the novel coronavirus delivers its payload to a cell’s nucleus. Then they used a model that predicts protein structure to look for proteins that might inhibit this process. They found 15 known drug candidates that contain this protein, and they’re conducting their own trials to gauge their effectiveness.

Behind the news: Infectious disease expert Dr. Michele Barry explained that machine learning was critical to keeping infection rates low in Singapore, South Korea, and Taiwan. All three countries deployed the technology to encourage social distancing, move medical supplies where they were needed most, and keep the public informed.

Why it matters: Machine learning engineers and disease specialists have a lot to learn from one another. Conferences like this can bring them together and build lasting alliances that may result in tools for fighting future outbreaks.

We’re thinking: If you’re itching to join the fight against Covid-19, you can find a list of tools, datasets, and information just above the news in this issue of The Batch. Also: Stay at home and wash your hands!

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