Stopping whack-a-mole approach: Scientists track coronavirus hotspots

Coronavirus Updates

Screen grab of interactive tracking map

SALT LAKE CITY, Utah (ABC4 News) – The coronavirus pandemic has been like a game of whack-a-mole. That’s the game where plastic rodents pop up randomly and you have to pop them back down. COVID-19 outbreaks hit communities across the country, local officials scramble to allocate resources and enact public health policies. It’s playing with chance and risk, but now, there’s new data that can help.

Now, for the first time, how COVID-19 moves over time is being mapped.

In a new study led by the University of Utah, geographers published the first effort to conduct daily surveillance of emerging COVID-19 hotspots for every county in the contiguous U.S. The researchers hope the timely and localized data will help inform future decisions.

How did scientists track COVID-19?

According to a press release, the scientists used innovative space-time statistics. First, the researchers detected geographic areas where the population had an elevated risk of contracting the virus. Then, they ran an analysis every day using the daily COVID-19 case counts from Jan. 22 to June 5, 2020. This established regional clusters, a group of disease cases closely grouped in time and space. For the first month, the clusters were very large, especially in the Midwest. Then they noticed a change, starting on April 25, 2020, the cluster become smaller and more numerous, a trend that persists until the study ends.

Below is a slideshow of a still of the interactive map, and three of the four researchers.

The article was published online on June 27, in the journal Spatial and Spatio-temporal Epidemiology. The study built on the team’s previous work by evaluating the characteristics of each cluster and how the characteristics change as the pandemic unfolds.

“We applied a clustering method that identifies areas of concern, and also tracks characteristics of the clusters—are they growing or shrinking, what is the population density like, is relative risk increasing or not?” said Alexander Hohl, lead author and assistant professor at the Department of Geography at the U. “We hope this can offer insights into the best strategies for controlling the spread of COVID-19, and to potentially predict future hotspots.”

The research team, including Michael Desjardins of Johns Hopkins Bloomberg School of Public Health’s Spatial Science for Public Health Center and Eric Delmelle and Yu Lan of the University of North Carolina at Charlotte, have created a web application of the clusters that the public can check daily at COVID19scan.net. The app is just a start, Hohl warned. State officials would need to do smaller-scale analysis to identify specific locations for intervention.

“The app is meant to show where officials should prioritize efforts—it’s not telling you where you will or will not contract the virus,” Hohl said. “I see this more as an inspiration, rather than a concrete tool, to guide authorities to prevent or respond to outbreaks. It also gives the public a way to see what we’re doing.”

In order to create the model over time, the researchers used daily case counts reported in the COVID_19 Data Repository from the Center for Systems Science and Engineering at John Hopkins University, which lists cases at the county level in the contiguous U.S. They also used the data from the 2018 U.S. census and the five-year population estimates of each county.

How the science works

To identify clusters, researchers ran a space-time statistic that takes the observed number of COVID-19 cases, and the population within a given geographic area and the dates (time-span).

Tracking the virus has been like playing the game whack-a-mole

Using SatScan, a widely used software in the community, they identified areas of higher risk of COVID-19. Due to the large differences between counties, researchers say defining risk is tricky.

As an example, a rural area with less population could have a handful of cases, and that makes risk go up significantly.

This study is the third from the research group using the statistical method for detecting COVID-19 clusters. In May the group published its first geographic study using the traffic method, which was also the first by geographers analyzing COVID-19. In June, they published an update.

“May seems like an eternity ago because the pandemic is changing so rapidly,” Hohl said. “We continue to get feedback from the research community and are always trying to make the method better. This is just one method to zero in on communities that are at risk.”

Researchers say a big limitation of the analysis is the data itself. COVID-19 reporting is different for each state. There’s no uniform way that information flows from the labs that confirm the diagnoses, to the state health agencies to the COVID-19 Data Repository, where the study gets its data. Also, the testing efforts are quite different between states, and the team is working to adjust the number of observed cases to reflect a state’s efforts.

Hohl is also working with other U researchers to look at the relationship between social media and COVID-19 to predict the future trajectory of outbreaks.

“We’ve been working on this since COVID-19 first started and the field is moving incredibly fast,” said Hohl. “It’s so important to get the word out and react to what else is being published so we can take the next step in the project.”

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