DataRobot header v.1.0 3

Epidemiologist Michal Mina Says Lack of Cheap, At-Home Testing Is Making COVID-19 Worse

May 27, 2021
· 3 min read

For more than a year, the world has been grappling with the COVID-19 pandemic—easily the worst public-health crisis since the 1918 flu pandemic. More than half a million Americans have died from the novel coronavirus, and as of this writing India is seeing more than 300,000 new cases each day. 

With that alarming background, this seemed like an extremely appropriate time to talk with Michael Mina, an Assistant Professor of Epidemiology at Harvard T. H. Chan School of Public Health. Michael earned his MD and PhD degrees from Emory University, and today his research combines mathematical and epidemiological models with high-throughput phage-display–based serological laboratory investigations. 

In other words, he’s just our kind of guy. 

Michael does not mince words when he describes our overall response to COVID-19. “A disaster,” he told me. “Half a million dead, and with the exception of vaccines, we still haven’t acted in an appropriate way that’s commensurate with this pandemic.” 

In particular, Mina believes that our failure to test effectively has allowed COVID-19 to spread. True, there are tests—and they are good ones—but they are relatively expensive, require an outside laboratory, and take two days or more for results. And that, he says, is not good enough. 

“What the country really needs and what the world needs to mitigate the spread of this virus is not expensive medical diagnostic testing, it’s public health testing” he says. “This is testing where people test themselves multiple times a week. Then if someone discovers they’re infected, they’re empowered to take the initiative to isolate themselves.” 

One reason this is not more widespread, he says, is that public health testing is misunderstood. “You don’t need a screening program that is 100 percent effective. You just need screening that is 90 percent effective. That sounds like a failure, but if we can get 100 people to infect 90 people and do that four or five weeks in a row, then we end up with 30 people infected instead of 600, because normally if you allow this virus to go how it normally goes, 100 people infect 130. 

“That kind of screening can really bend the curve of an outbreak. We’ve published a number of papers now that show it’s the frequency of testing, and testing—in particular of people who don’t realize they’re infected—that slows the spread.” 

So why haven’t we adopted this approach? It’s not that we lack easy-to-use tests, Michael says. It’s a lack of regulatory agility. 

“The FDA [Food and Drug Administration] needs to come up with an innovative approach to understand how to regulate a test that is not a medical diagnostic test, but that has the sole purpose of preventing disease spread,” he told me. “Instead, the FDA requires that tests be measured against qPCR [quantitative polymerase chain reaction], which ultimately is just the wrong benchmark. 

“Many epidemiologists said contact tracing and laboratory-based qPCR testing just can’t work once you have a lot of cases. Nevertheless, every public health agency in the nation kept plodding along on that course.” 

Michael believes easily available, cheap (or free) at-home tests, coupled with powerful tools such as artificial intelligence and machine learning, could give public health officials the ability to have one-click reporting on increases, decreases, and the geographic spread of COVID-19.  

Creating a better testing and tracing system would be a valuable takeaway from the current pandemic. Because, he says, another one is inevitable. “Pandemics are becoming more common, not less. And if we don’t build a pandemic infrastructure today for tomorrow’s pandemic, we’re just fooling ourselves.”

Michael Mina: Applying the Lessons of the Pandemic to Public Health
Watch Now
About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

Meet DataRobot
  • Listen to the blog
  • Share this post
    Subscribe to DataRobot Blog
    Newsletter Subscription
    Subscribe to our Blog