Geoprocessing-enabled COVID-19 map aids resource allocation amid pandemic

A new spatially enabled coronavirus map leverages multiple data models to more aptly pinpoint areas in need of limited medical equipment and resources.

Months after the initial cases of COVID-19, the pandemic continues to spread around the globe. During that time, a seemingly infinite scroll of coronavirus infographics and forecasting models detailing ways to “flatten the curve” have become evening news mainstays. In lieu of a vaccine, these models exist as the only feasible front-line strategy to mitigate the spread of the virus and salvage any semblance of a functional healthcare system in the months ahead.

Recently, we had a chance to speak with Lauren Bennett who is the spatial analysis and data science software development lead at Esri. Bennett’s team of geographers and statisticians are working to transition this armamentarium of models into sophisticated mapping solutions to aid coronavirus response efforts. Did we mention there are lots of models?

“If you want the top 10 most used models, it’s 10 different answers, right? They all have similarities, they’re not like 10 completely different answers, but they’re all different. In some cases, it’s levels of magnitude different, depending,” Bennett said. “They have pros and cons.”

At the moment, there are innumerable models for agencies and health officials to leverage. As is the case with any forecasting tool, the model is only as smart as its inputs and the underlying algorithm. For increased utility and presumed accuracy, more organizations are deploying a suite of tools to guide their approach to the pandemic.

“There’s a lot of models and they all have weaknesses and our users, what we’re seeing from the local county level, at the state level, and at the federal level, no one wants to make decisions using just a single model, which is great. They shouldn’t, right?” Bennett said rhetorically.

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Understanding these forecasting tools

The epidemiologists often utilize mathematical “SIR” models that account for the susceptible, infectious, and recovered within a given population. The Predictive Healthcare Team at Penn Medicine modified a SIR to create their own model known as the COVID-19 Hospital Impact Model for Epidemics, or simply CHIME. CHIME then uses available data to forecast hospitalizations and, consequently, the overall demand for critical medical resources (ICU beds, ventilators, etc.)

Unlike other traditional epidemiological SIR models that simply churn out projections with minimal or user inputs, CHIME allows users to add more data to the model. This is invaluable for local agencies with a keen understanding of hyper localized data in their area.

“Your county who knows literally the exact things that are going on in your county, you may want to put in data because you know more of what’s going on on the ground than anyone else. That’s a pro of being able to plug in your own data,” Bennett said.

CHIME also enables organizations to incorporate hypothetical data to run a series of what-if scenarios to test the forecasted effects of public policy such as lifting lockdown measures. This scenario-specific component will become critical as more states begin to ease restrictions in the weeks ahead.

Adding the spatial piece to the epidemiological puzzle

 
Bennett’s team incorporated their geospatial expertise to these open source tools to help organizations visualize this hyper localized data within a geographic information system (GIS) for forecasting purposes. In more simplified terms: Esri parlays info from this enhanced SIR model used in tandem with localized health data to create a map.

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