This is the second of a two part series.
One form of information that can help us with the CV quarantine is data about crowding at the grocery stores. There are many companies that provide video surveillance at retail stores and if only they would aggregate and anonymize such information and share publicly. We can then choose to go when there are fewer people in the store. And we can use one of our many classical randomization algorithms to prevent flash crowds, i.e., all of us see the store is empty of people and decide at the same time to visit the store [ Paper-ACM-17 ] [ Paper-NSDI-07 ].
Quintessential American Hero
Big tech, big healthcare tech that is, may yet play the hero’s role in this saga. Quite emblematic of the spirit of American ingenuity and entrepreneurship and R&D muscle, two things caught my eye. First, Remdesivir is an antiviral drug that had success against prior coronavirus infections and is being tested now against Covid-19 in trials in Washington and China. This was developed by an American biotechnology company, Gilead Sciences. Second, a CV vaccine entered Phase 1 trial on Mar 16. An “ordinary” American, Jennifer Haller, stepped up at the Kaiser Permanente Washington Health Research Institute in Seattle to take the first shot in that trial. The vaccine was developed by … you guessed it, an American company (Massachusetts-based biotechnology firm Moderna). But wait, it was co-developed with the National Institutes of Health (NIH), a big government entity!
I wanted to use my data scientist’s hubris to see what causative factors can help explain how successful a country has been, or not, in controlling the spread of the CV. So I dug into the CV spread information from Johns Hopkins, as superbly organized and analyzed by Imperial College. I put the countries into three coarse categories: being good at containment, so-so, and being caught napping (i.e., messing it up). And then I looked to see if any of the factors like per capita healthcare spending, the freedom enjoyed by the people of the country, or the population size of the country could explain this. My data scientist’s hubris was knocked down quickly. None of these factors could shed light on whether a country has been good at containing the spread of the CV.
Where’s GFT When We Need It?
I also thought of the victory of Google Flu Trends (GFT) in tracking flu outbreaks. The idea behind GFT was that, by monitoring millions of users’ health tracking behaviors online, the large number of Google search queries gathered can be analyzed to reveal if there is the presence of flu-like illness in a population. This was once held-up as the prototypical example of the power of big data. By leveraging search term data, folks at Google with no medical expertise were able to predict the spread of flu across the continental United States. In near real-time. At a marginal cost. And more accurately than the “experts” at the Centre for Disease Control (CDC) with their models built from expensive survey data, available only after the fact. The 2009 influenza H1N1 pandemic provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. And it worked remarkably well.
Would a GFT-like system have worked today to predict the spread? We can only speculate since the system was mothballed in 2015. And my speculation is that it would not have been of any use. Since we are all Googling “Do I have the CV?”, “Do I have the CV yet?”, and “What should I do to practice social distancing?”. So data science may not have all the answers for us here after all.
Answers instead come from low tech — the tape measure showing 6 ft, the distance I am supposed to keep from my colleagues, the robust supply chain that keeps my local grocery chain stocked and my gas station filled, and the sense of social responsibility that most people around me are demonstrating. I opened a kombucha bottle today, one of those new age ones that dish out lessons on their bottle caps. It said something strangely topical.
We are all in this together.
 Mark Handley, University College London, "CoVID 19 Worldwide Growth Rates," At: http://nrg.cs.ucl.ac.uk/mjh/covid19/