Most COVID-19 maps fail to improve public understanding of pandemic

Since COVID-19 first arose as a worldwide health threat, millions of websites with maps charting the spread and prevalence of the virus have appeared on the internet. However, most of these maps do not improve public understanding of the potential risk of contracting COVID-19 or promote compliance with health guidelines meant to slow its spread, according to a new study led by University of Utah Health scientists in collaboration with other institutions.

In fact, the researchers found that study participants who didn’t see a map were more knowledgeable about the total number of COVID-19 cases in the United States than those who viewed one. They also found that maps did not influence a person’s perception of their own risk of contracting the disease.

A map of the U.S. with red circles that represent COVID-19 outbreaks.

Most COVID-19 maps, such as this bubble map, fail to improve public understanding of the pandemic and its risks.

“We know that people’s behavior and intentions are influenced by how well informed they are and how they perceive risk,” says Alistair Thorpe, lead author of the study and a U of U Health postdoctoral research fellow. “Prevalence maps are designed to help with that and so many have appeared during the COVID-19 pandemic. However, we found that simply presenting COVID-19 information in a map doesn’t necessarily have the intended influence on knowledge, perception, or behavior.”

The study appears in JAMA Network Open.

In a May 2020 online survey, the researchers asked 2,676 people, aged 18 to 91, to view one of six randomly selected maps of COVID-19 prevalence in the United States. Then, they were questioned about their knowledge of confirmed COVID-19 cases, their perceived risk of getting the disease, and whether they intended to adhere to COVID-19 prevention guidelines. A control group was not shown any of the maps and was simply asked to answer the questions based on their own knowledge without visual aids.

“The goal of this study was to try to find the best method for having people understand how common COVID-19 is nationwide as well as where they live,” says Angela Fagerlin, senior author of the paper, a U of U Health professor of population health sciences, and a research scientist at the Veterans Affairs Salt Lake City Health Care System. “We were hoping that understanding the prevalence of this disease would inspire them to take action to prevent the spread of COVID-19.”

A map of the U.S. where states are colored on a gradient from light beige to red to represent COVID-19 outbreak densities.

Heat maps, such as this one, that display COVID-19 cases per capita by state appear to be more effective than others.

Overall, those who saw maps were no more knowledgeable about the COVID-19 pandemic than those who didn’t see them. Participants who saw a map had lower perceptions of the risk to society; they were also more optimistic that the pandemic would be better in two weeks compared to those who didn’t see a map. None of the maps appeared to influence how participants perceived their personal susceptibility to the virus or whether they intended to follow public health guidelines.

However, heat maps that depicted per capita cases by state in varying hues and intensities depending on localized prevalence appeared to be more effective than others.

“The features of these maps appear to be the most effective for improving or at least maintaining public knowledge of COVID-19 cases,” Thorpe says.

Among the study’s limitations are its reliance on self-reported information and potential barriers to participation, including lower English proficiency and limited or no internet access.

Moving forward, the researchers believe this finding could have significant long-term implications.

“There’s a lot of data coming out during this outbreak about how to effectively communicate health information that will help us in the next pandemic,” Fagerlin says. “I hope we can learn from this experience so we don’t have to recreate the wheel every time we experience a pandemic.”

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First daily surveillance of emerging COVID-19 hot spots

Over the course of the coronavirus epidemic, COVID-19 outbreaks have hit communities across the United States. As clusters of infection shift over time, local officials are forced into a whack-a-mole approach to allocating resources and enacting public health policies. 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 that timely, localized data will help inform future decisions.

Using innovative space-time statistics, the researchers detected geographic areas where the population had an elevated risk of contracting the virus. They ran the analysis every day using daily COVID-19 case counts from Jan. 22 to June 5, 2020 to establish regional clusters, defined as a collection of disease cases closely grouped in time and space. For the first month, the clusters were very large, especially in the Midwest. Starting on April 25, the clusters become smaller and more numerous, a trend that persists until the end of the study.

The article published online on June 27, 2020, in the journal Spatial and Spatio-temporal Epidemiology. The study builds on the team’s previous work by evaluating the characteristics of each cluster and how the characteristics change as the pandemic unfolds.

Weekly clusters resulting from the prospective space-time scan statistic.

“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.”

The researchers used daily case counts reported in the COVID-19 Data Repository from the Center for Systems Science and Engineering at Johns Hopkins University, which lists cases at the county level in the contiguous U.S. They used the U.S. Census website’s 2018 five-year population estimates within each county.

To establish the clusters, they ran a space-time scan statistic that takes into account the observed number of cases and the underlying population within a given geographic area and timespan. Using SatScan, a widely used software, they identified areas of significantly elevated risk of COVID-19. Due to the large variation between counties, evaluating risk is tricky. Rural areas and small, single counties may not have large populations, therefore just a handful of cases would make risk go up significantly.

This study is the third of the research group’s iteration using the statistical method for detecting and monitoring COVID-19 clusters in the U.S. Back in May, the group published their first geographical study to use the tracking method, which was also of the first paper published 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.”

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 from the Center for Systems Science and Engineering at Johns Hopkins University, 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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