In a study that evaluated the effectiveness of 25 different COVID-19 dashboards, faculty researchers at the Raymond A. Mason School of Business and the United States Air Force Academy found that the countries most successful in curbing the transmission of COVID-19 were the ones with governments that took swift action, imposed early stringency measures, and implemented tracking and analysis mechanisms through which infectious disease data could be disseminated rapidly to the general public.
Dr. Joe Wilck, Faculty Director of the Business Analytics programs at the Mason School, was joined by his William & Mary colleague Dr. Scott McCoy, Area Head and Professor of Business, and Dr. Jesse Pietz, an Associate Professor and Director of Research & Consulting in the Management Department at the United States Air Force Academy as they looked at the publicly available dashboards and the data they represented. They published their study, “Chasing John Snow: data analytics in the COVID-19 era” in the European Journal of Information Systems in July 2020.
“Our initial frustration was with the myriad of analytic products out there at the onset of the pandemic that presented COVID data in so many different ways,” explained Pietz. “Research has shown that information overload and misinformation can be impediments to an effective response to an infectious disease.”
Wilck, McCoy, and Pietz surveyed 25 different publicly available data analytics dashboards, 18 of which were endorsed by the Center for Disease Control (CDC), representative of five different sectors including academia, research, government, news, and independent sources. They highlighted the modeling approach taken by each and developed a multi-attribute utility (MAUT) theory model to assess each dashboard’s effectiveness in communicating key features that explain the spread of COVID-19 and how interventions may affect death rates. Their assessment focused on status and actions across seven categories: population, infections, recoveries, deaths, factors affecting infection rate, factors affecting recovery rate, and factors affecting death rate.
“Compartmental epidemiology models are the most common by far,” said Wilck. “They model how people in a population transition between various disease infection categories like susceptible, infected, recovered, and deceased persons. To understand what is happening, we needed information about how many people are in each category, how fast they move from one category to another, and information about actions that affect these counts and rates.”
Their study is named after the father of modern epidemiology John Snow, who in 1854 created a map of London’s cholera outbreak to identify its mode of communication. His visualization was a mix of a map chart and bar chart that illustrated how cholera infections were distributed around the city. It is considered one of the most influential data visualizations ever made as a premodern contract tracing study.
In chasing John Snow, Wilck, McCoy, and Pietz found that the dashboards created by Johns Hopkins University and Massachusetts Institute of Technology were particularly good at visualizing the information feeding their MAUT model. However, all of the dashboards they analyzed lacked clearly presented action-oriented data.
“In all of the data visualization dashboards that we considered, they did not have data related to what actions governments have taken to affect the spread of disease and how effective were those actions,” said Pietz. “One of the main findings of our research is that governments really do need to do a better job of tracking, analyzing, and disseminating infectious disease data in a consistent way.”
The team took their study further by linking their assessments to stringency, a measure developed by Oxford University that collects data from countries around the world and evaluates different types of response measures. They studied actions like international travel controls, prohibition of large gatherings, cancellation of large public events, testing, and contact tracing and looked at how they affected infections and death rates.
“Timing was really important here,” said Wilck. “We found that countries that are less capable in terms of their healthcare and technology infrastructure were still good at mitigating COVID-19 by enacting stringency measures early.”
Their study found that countries like Zimbabwe and Vietnam responded well to the outbreak despite limited resources because they implemented controls months before their tenth reported case.
“Of the different measures we looked at, they all work differently for different countries but the two variables that rose to the top had to do with contact tracing and widespread testing,” said McCoy. “Governments that started early, contained the pandemic much better than those that started late. We were able to use this data to make some definitive recommendations on how governments can respond to a resurgence of the current pandemic as well as future pandemics.”
To read about their research in its entirety and learn more about the recommendations Wilck, McCoy, and Pietz have for governments working to curb the spread of COVID-19, download the study which is available in print J. Pietz, S. McCoy, J. Wilck, "Chasing John Snow: data analytics in the COVID-19 era," European Journal of Information Systems, 29(4), 388-404, 2020.