Research

Data Model Confirms That Wearing Masks Saves Lives

November 24, 2020 2043
quartet in face masks

Dr. Biplav Srivastava, professor of computer science at the University of South Carolina, and his team have developed a data-driven tool that helps demonstrate the effect of wearing masks on COVID-19 cases and deaths. His model utilizes a variety of data sources to create alternate scenarios that can tell us “What could have happened?” if a county in the U.S. had a higher or lower rate of mask adherence. In this interview, he explains how the model works, its limitations and what conclusions we can draw from it.

Computer scientist Biplav Srivastava provides a demo of the simulation to show that earlier policies to recommend mask-wearing make a bigger difference on the spread of the coronavirus.

What does this computer model do?

This is a nationwide tool which can show the effect that wearing masks can have. If it’s a county where people wear masks regularly, it will show you how many COVID-19 cases and deaths they avoided. If you pick a county where people don’t wear masks, it will show you how many cases and deaths could have been prevented there.

How does it do it?

We need a lot of data to do this. The New York Times surveyed almost every county in the U.S. over the summer and assigned a mask-wearing score of 0-5 to each of them, so this is at the heart of the model. We also use New York Times and Johns Hopkins data for real-time case numbers; census data for demographics such as population size, median age and more; and geographic data to measure the distance between counties.

It is based on a mathematical technique called robust synthetic control, which is often used in drug research, where there is a control group and there is a treatment group.

The Conversation logo
This article by Biplav Srivastava originally appeared on The Conversation, a Social Science Space partner site, under the title “A new data-driven model shows that wearing masks saves lives – and the earlier you start, the better”

For example, let’s look at Wyandotte County, Kansas. It has a relatively high mask-wearing score of about 3.4. Because the model is designed to tell us the “what if?” scenario, it will look at what would have happened if the mask-wearing score was reduced to 3.0, which is our cutoff for “low mask-wearing,” but the user can experiment with other values too just to see what happens. We arrived at 3.0 based on analysis of nationwide mask-wearing habits. The actual values ranged between 1.4 and 3.85, with a national average of 2.98.

We can set a date at which the mask-wearing score changes to 3.0. If we set it to run from June 1 to Oct 1, it tells us that Wyandotte County would have had 101.5 percent more cases and 150 more deaths in that period. It tells the user how many deaths have occurred or been prevented based on a mortality rate parameter that the user can set. In this example, it was set at 2 percent.

How does the model create the “what if?” scenario if it didn’t actually happen? It does this by looking at other counties that are close by and have similar demographics and case count but a lower mask-wearing threshold. It tries to come up with a weighted average to form a synthetic control group which is similar to our county of interest (treatment group). The model then looks at how much the two groups have diverged in terms of the case counts. The difference in case counts between the two groups is converted to a difference in deaths using the mortality rate parameter.

What does this tell us about the impact of mask-wearing policies?

Keeping up mask-wearing or implementing a mask policy at any time can be helpful. But its impact is highest when you do it early. When you run this model multiple times using different dates, you see that the impact reduces as you delay implementing a mask-wearing policy. So if a county implemented a mask policy on June 1, it would have prevented many cases. If it acted on July 1, it would have a smaller impact. If it acted in August, it would still have prevented cases, but a very small number.

What are the limitations of this model?

This tool works better for some counties than others. In general, it works best with counties that are closer to the average, because it will have closer matches to compare against. There is also a limitation in the sense that The New York Times mask adherence survey was done in the summer, and things keep changing. So if other researchers use this tool, they will have to account for the changes.

But what you see is that when you implement a mask policy or the population regularly wears masks, it makes a positive impact. And the earlier you do it, the more effective it is.

I would like to acknowledge the work of my team, Sparsh Johri, Kartikaya Srivastava, Chinmayi Appajigowda and Lokesh Johri, in developing this program.

Biplav Srivastava is a professor of computer science at the AI Institute at the University of South Carolina. He is interested in enabling people to make rational decisions despite real world complexities of poor data, changing goals and limited resources by augmenting their cognitive limitations with technology. Previously, he was at IBM for nearly two decades as a research scientist, distinguished data scientist and master inventor. Srivastava is an ACM Distinguished Scientist, AAAI Senior Member, IEEE Senior Member and AAAS Leshner Fellow for Public Engagement on AI (2020-2021).

View all posts by Biplav Srivastava

Related Articles

2024 Holberg Prize Goes to Political Theorist Achille Mbembe
News
March 14, 2024

2024 Holberg Prize Goes to Political Theorist Achille Mbembe

Read Now
Four Reasons to Stop Using the Word ‘Populism’
Insights
March 4, 2024

Four Reasons to Stop Using the Word ‘Populism’

Read Now
Alison Gopnik on Care
Social Science Bites
March 4, 2024

Alison Gopnik on Care

Read Now
New Feminist Newsletter The Evidence Makes Research on Gender Inequality Widely Accessible
Impact
March 4, 2024

New Feminist Newsletter The Evidence Makes Research on Gender Inequality Widely Accessible

Read Now
Did the Mainstream Make the Far-Right Mainstream?

Did the Mainstream Make the Far-Right Mainstream?

The processes of mainstreaming and normalization of far-right politics have much to do with the mainstream itself, if not more than with the far right.

Read Now
The Importance of Using Proper Research Citations to Encourage Trustworthy News Reporting

The Importance of Using Proper Research Citations to Encourage Trustworthy News Reporting

Based on a study of how research is cited in national and local media sources, Andy Tattersall shows how research is often poorly represented in the media and suggests better community standards around linking to original research could improve trust in mainstream media.

Read Now
Revolutionizing Management Research with Immersive Research Methods

Revolutionizing Management Research with Immersive Research Methods

In this article, Anand van Zelderen, Nicky Dries, and Elise Marescaux reflect on their decision to explore nontraditional research.

Read Now
0 0 votes
Article Rating
Subscribe
Notify of
guest

This site uses Akismet to reduce spam. Learn how your comment data is processed.

0 Comments
Inline Feedbacks
View all comments