The amount of data created by the COVID-19 pandemic is massive. While data aggregators and technology companies are using new data to release dashboards and contact tracing apps, that data usage is not always transparent, rigorous or collaborative.
In a May 8 article for the Harvard Business Review, academics and providers affiliated with Cambridge, Mass.-based Harvard University wrote that while data can be an essential tool in measuring how effective pandemic interventions are, incomplete or incorrect data can be harmful.
Here are five observations on deciphering which COVID-19 data are trustworthy from the following researchers:
- Satchit Balsari, MD, physician at Beth Israel Deaconess Medical Center in Boston and assistant professor in emergency medicine and public health at Harvard
- Caroline Buckee, PhD, associate professor of epidemiology at the Harvard T.H. Chan School of Public Health
- Tarun Khanna, PhD, the Jorge Paulo Lemann Professor at Harvard Business School
1. There are common pitfalls leaders can avoid when finding trustworthy data sources. The researchers said scrutiny should be given to data that are too broad, too specific or lack context.
"Over-aggregated data — such as national metrics of physical distancing that some of our largest data aggregators in the world are putting out — obscure important local and regional variation, are not actionable, and mean little if used for inter-nation comparisons given the massive social, demographic, and economic disparities in the world," the researchers wrote.
2. Transparency about data and the technology used to present it — like representativeness, analytics methods and algorithms — should be sought. Providers who are more confident about their processes are more likely to open them to public scrutiny and "are the safest knowledge partners," the researchers said.
3. More trustworthy analysts will be the ones who are "conservative in their recommendations, share the uncertainty associated with their interpretations, and situate their findings in the appropriate local context," according to the researchers.
4. It's important to review the credentials of those providing data, according to the researchers, as leaders with little epidemiological expertise are publishing large amounts of data.
5. Models that are open platforms and collaborative are the ones leaders should look to.
"There are several data aggregators that are committed to supporting an ecosystem of communities, businesses, and research partners, by sharing data or code in safe and responsible ways. Such open ecosystem approaches, while not easy to manage, can yield high dividends," the researchers said.
Read the full HBR article here.
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