Global study of COVID-19 data finds new ways to simplify information and help fight future pandemics

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(LR) Kerrie Mengersen, Abhishek Varghese, Antonietta Mira, Edgar Santos-Fernandez. Credit: Queensland University of Technology

New research that analyzed complex global datasets on COVID-19 has found new ways to simplify information to help health authorities deal with future outbreaks.

Researchers from the QUT Center for Data Science, in collaboration with Italian and Swiss scientists, used advanced statistical and data science models to extract insights on a global scale.

The study, published in Scientific Reports, provided new insights into the COVID-19 pandemic related to daily case counts, deaths and government stringency measures. The data covered 454 days of the pandemic from March 1, 2020 to May 29, 2021 and included 115 countries.

Senior data science researcher Dr Edgar Santos-Fernandez said the research involved mapping the evolution of the pandemic.

“We were surprised to find that we could simplify a complex dataset with over 1,300 dimensions and classify it into just two groups characterized by a handful of relevant features,” he said.

“Despite the complex nature of statistics aggregated by country, we were able to unravel and extract valuable insights to help inform decision-making.”

The researchers found two clusters around the world, with countries in each cluster exhibiting similar patterns in their response to the pandemic.

For example, countries within the clusters responded with similar timing and strategies regarding austerity measures, such as closing schools, workplaces and borders.

Dr Santos-Fernandez said the patterns identified in the data can help predict the course of future outbreaks.

“This information can be used to help governments and healthcare providers plan and identify more effective strategies for non-pharmaceutical interventions and responses.”

Emeritus Professor Kerrie Mengersen said the research has highlighted the immense potential of data science to uncover deeper insights hidden in complex datasets.

“What’s equally exciting is that the methods developed here don’t just apply to COVID-19 research,” she said.

“Scientists in many other fields can use them to explore and make informed decisions about the problems they face.”

The data used in the research was obtained from Our World in Data’s “Data Explorer” and the COVID-19 Severity Index (CSI) from the Oxford Coronavirus Government Response Tracker (OxCGRT).

More information: Abhishek Varghese et al, A Global Perspective on the Intrinsic Dimensionality of COVID-19 Data, Scientific Reports (2023). DOI: 10.1038/s41598-023-36116-1

Journal Information: Scientific Reports

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