Supply Chain and the Johns Hopkins COVID-19 Dashboard?

In a war against an invisible enemy, accurate information is a powerful weapon. It not only provides the basis for smart decisions, but, when clearly presented and easily digested, it can offer a sense of peace and comfort that helps ward off anxiety.

That helps explain the immense popularity of Johns Hopkins University’s web-based COVID-19 dashboard, which NPR dubbed, “one of the world’s most authoritative sources for the latest coronavirus numbers and trends.” It’s a go-to source for everyone from journalists to private citizens and politicians to professors because it offers near real-time, comprehensive, global updates on what’s happening with the pandemic.

The dashboard, however, is far more than just a visually cool compilation of data – it’s also a great example of how to effectively use global mapping and spatial analytics software. As the dashboard illustrates, these tools can dissect data in ways that help solve the mysteries of what has happened and why, while also helping to accurately predict trends for the future. 

Mapping Solutions

This is important to the everyday work of governments and businesses, especially from a supply chain perspective. And as Cindy Elliott told me in a recent video interview, it has broad implications in the fight against the spread of the coronavirus, including how we improve supply chain performance in response to the disease. 

Elliott is the head of the commercial industry team at Esri, the California-based company that makes the mapping software used by platforms like the one created by a team at Johns Hopkins. The company has been around since 1969 – Esri originally was Environmental Sciences Research Institute – and it has evolved into a robust technology provider. It’s Geospatial Information Systems (GIS) software turns maps into insights.

By looking at where certain events took place and the results of what happened, Elliott said, GIS “helps organizations, people, communities and governments better understand why it happened, where it is likely to happen again and what’s going to happen next.”

Ersi has strong ties with nonprofits like the World Health Organization, the Center for Disease Control, National Geographic, and the Audubon Society, but it also works in the private sector with oil and gas companies, utilities, manufacturing, retail, and logistics. And in the crisis of a pandemic, the needs of many of those partners have overlapped. 

Businesses, for instance, are trying to understand and improve their healthcare supply chains, and they can analyze geospatial data to discover what Elliott calls “hyper-local insights” that affect things like market demands, production, and distribution. 

When it comes to COVID-19, teams operating dashboards like the one run by Johns Hopskins can take their analysis of cases, mortality rates, and recovery rates even deeper by using geospatial software.

“You’ve all heard about curves, right?” Elliott said during our video call. “There’s not one COVID curve. There are millions of COVID curves. In fact, we all carry our own COVID curve. The John Hopkins dashboard allowed all this data to be seen, not only by our communities so we could know, but by healthcare providers globally. … They could start to analyze this curve and, as we saw it, it went from just understanding the red dots on the map to now being able to predict where the surges are going to happen. What’s the need? Where are the ICU beds needed? Where are the ventilators needed? So it’s really helped drive the model in which this response can be provided.”

Improving Supply Chains

In supply chain management, of course, forecasting is critical to efficiency, but it’s challenging because it involves looking at the past to predict the future. And in the midst of a pandemic, the future doesn’t really look much like the past. While we still lack sufficient data on many aspects of the crisis, the situation improves with each passing day because more and more information is available.

“Over the last few weeks especially,” Elliott said, “the data has been able to get to a much new granularity, moving from a national view, to a state view, to a county, and we’re on the cusp of being able to breakdown the analysis to a zip code level, which is a tremendous breakthrough in being able to do these models.”

Looking at a specific county, for instance, can involve sifting through demographic information like the age of the population, as well as how people typically interact. A rural county might skew older and be higher risk for COVID-19, but people might be spread out geographically. Another county might be rural but have several nursing homes or prisons, which would increase the risk factors. This level of analysis can help more accurately forecast the likelihood of an outbreak and the need for products and services in a given area, while also improving response times when new cases occur. 

“Layering all these factors together, which are all locations in nature, helps drive these particular models,” Elliott said. 

Analyzing data in near real-time, Elliott pointed out, means business, government, and nonprofit officials can make decisions using a dimmer switch rather than a binary light switch. In other words, they can make gradual adjustments that hopefully keep people safe without forcing us to live in the dark. And when you can’t see the enemy, nothing’s more helpful than bringing more information into the light.

Acknowledgement: An enormous thank you to Cindy Elliott, Maddi Heise, Stephen Caldwell, the Sam M. Walton College of Business and the SCM Research Center at the University of Arkansas for their support in developing this article.