As another sign of the times, Stanford repurposed its planned Human-Centered AI (HAI) Conference into a digital-only, publicly accessible symposium on how technology has been and can be employed in fighting the spread and assisting in the treatment of COVID-19. We heard from researchers, doctors, statisticians, AI developers, and policymakers about a wide variety of strategies and solutions. Some of them have been working on this problem for a long time, some have quickly re-purposed their flu research, and others have shifted entirely from what they were doing before because of the urgency of this crisis.
Forecasting Tools for Policy Makers
For public officials trying to assess how various interventions will affect the spread of COVID-19, and the impact it will have on health infrastructure, or just for curious individuals who want to get more information than is provided in often confusing national briefings, Stanford’s SURF (Systems Utilization Research for Stanford Medicine) gives you a way to experiment with various values for the spread of the disease and predicted effectiveness of possible interventions and look at how that will affect how many will become ill, and how severely. The tool is pre-loaded with current case numbers by county throughout the US.
Mapping the Travel of the Novel Coronavirus Using Its Genome
From this graphic, you can see the chronology of how the virus spread around the world.
One of the most impressive aspects of the HAI event was the amazing number of non-profit research efforts made possible by scientists dedicated to improving public health. One of those is Nextstrain.org. The group provides an open-source toolkit for bioinformatics and collects data created with it to provide visualizations of various aspects of a variety of pathogens, now including the novel coronavirus. The featured image for this story is a genetic “family tree” of 2499 samples from around the world. You can visit the site and even see an animation of how the virus must have spread based on how its genome mutated.
Learnings From Singapore and Taiwan
While mainland China stumbled badly in its initial response to COVID-19, and we in the US clearly acted much too slowly to nip it in the proverbial bud, a few countries, including Singapore and Taiwan, have done a particularly effective job of preventing the pandemic from ravaging their population. A number of their strategies have been widely reported, but there are also several very interesting applications of technology used in those countries that were covered at the HAI conference.
Stanford & Woods Institute’s Michele Barry told us about a clever mobile app, TraceTogether, that has been widely deployed in Singapore. It uses a combination of location history and current Bluetooth proximity to not only let you know whether you are near someone who has tested positive for the virus, but alert you in the event that someone you have been near in the last couple weeks is now testing positive. Obviously this involves sharing a lot of information, which would face plenty of legal and social challenges in the US or most other countries. But it has proven very effective in slowing the spread of the disease. The same is true of the mandatory location tracking implemented for those coming into the country with any symptoms.
Chinese State media and US mainstream media show different perspectives in their coverage. — Courtesy of Stanford Cyber Policy Center.
Similarly, Taiwan implemented an extensive testing and mandatory quarantine of symptomatic individuals. Incoming flights were boarded and temperatures were taken, for example. Those with fevers found on planes or when entering public buildings were placed in quarantine, brought food, and paid a salary. Passenger travel databases were also connected to the national health database, so it was possible to alert those who had been near an infected individual so that they could get tested. It also meant that anytime anyone visited a doctor, the physician would know in advance if they were at high risk of being exposed and should therefore take precautions. Real-time mask availability maps were made available online in Taiwan, which worked because after their 2003 experience the country acted early to ramp up mask production so that there were enough for everyone to use one all the time.
One striking number from mainland China is that they sent 15,000 epidemiologists to Hubei Province once they decided to deal with the outbreak head-on — that’s twice as many as we have total in the United States.
Data Literacy and Critical Thinking are Key Skills for the General Public
Several of the speakers addressed the manifold issues with a large amount of often contradictory information, along with misinformation and disinformation, that is bombarding people worldwide. The specifics of the problem vary greatly by country and by demographic. In some countries like China, information tends to come “top-down” and be heavily filtered, so the problem becomes finding additional sources of information. In countries like the US, the problem can be the opposite, where there are far too many sources of information, many of which aren’t reliable or are deliberately spreading false information. But even here, politicization and factionalization have meant that reliable sources of information can be hard to come by.
HealthMap has added COVID-19 tracking to its existing crowdsourced flu-tracking capability.
One place where all the speakers were in agreement is that increased data literacy and critical thinking are key skills for individuals wanting to understand what is happening and have an informed perspective on how they should act, and how they should encourage others to act. In terms of data literacy, two concepts that are now front and center are dealing with the implications of exponential growth, and of interpreting margins of error in forecasts. Anyone trained in science, engineering, or math may be familiar with them, but it is clear many individuals — including many of our policy-making public officials — aren’t. As far as critical thinking, checking sources and putting data in context is more important than ever given the large amount of rapidly evolving data being produced on this topic. Even within the research community, the urgency to get research published is causing a lot of early printing of papers and rushed studies with limited datasets.
We’ve only covered a few of the highlights of Stanford’s HAI event in this article. There was also an entire technical session on tactics for developing drugs, and several excellent talks on telemedicine and using AI for eldercare. For those of you who are involved with machine learning, Kaggle’s Anthony Goldbloom gave a great description of how the platform is being deployed to assist, and how individuals can get involved. Harvard’s John Brownstein also showed off some of their impressive crowdsource data that populates healthmap.org. A few of the full talks are already online on the event web site, and more are being added as they are made available.
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