3 big data lessons from a COVID-19 mapping and modeling project

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Gathering data at the speed of life can make it hard to discern real information from a large amount of input. One data modeling and mapping project was able to make it work.

Big data lessons

Big data lessons- Finding a single version of the truth on the epidemiology of COVID-19 has proven elusive during this pandemic. There is no national case registry or medical inventory database. The epidemiological forecasting algorithms like SIR (Sampling-Importance Resampling) and IHME (International Health Metrics and Evaluation) that are used by federal and state governments lack reliable data. There is clearly a need to help public officials discern and navigate through health and economic risks better.

“I manage four different data labs throughout the world, and for the first few weeks of COVID-19, we were scrambling,” said Eric Haller, executive vice president and global head of Experian DataLabs, which provides advanced data analytics and research. “We had to learn how to shelter in place and to work remotely, but we were driven by a huge sense of responsibility to help government and healthcare providers sort through the data so we could make progress on the pandemic.”

The goal of lab efforts was to develop reliable data that could pinpoint and predict virus hot spots.

“Our process took about six weeks to build a core map that tracked COVID-19 outbreaks and responses,” Haller said. “We wanted to be able to provide the information to governments and healthcare so they could identify the hot spots and where they needed to double down with efforts for hard-hit communities.”

Data streams analyzed

Haller said there were three primary data streams that the analytics looked at.

The first was disease spread as represented by the number of cases and the number of deaths. A second data stream data stream provided co-morbidity rates. For those patients who died during a COVID-19 episode, how many had pre-existing conditions that made them especially vulnerable, such as heart disease or asthma?

“From the correlations of this data, we began to develop a health risk score on a county-by-county basis,” Haller said.

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Article Credit: TechRepublic

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