COVID-19 data models caused delays in response

A University of Derby academic named as one of the UK's most influential technology figures has argued that the models used for analyzing COVID-19 data caused delays in action taken to combat the coronavirus pandemic.

In a new article titled "Big Data and Analytics Lessons from the COVID-19 Pandemic," Richard Self, Senior Lecturer in Governance of Advanced and Emerging Technologies at the University, writes that the models being used for COVID-19 data were old and created "for a totally different context."

He also argues that delays to the first lockdown in March last year were caused because data about the virus "didn't indicate urgency."

Richard, who was recently named as the country's second most influential academic in the Tyto Tech 500 Power List, writes: "COVID-19 has demonstrated the limitations of this approach in situations where the data and knowledge (science) is incomplete, the models are inaccurate and do not accurately represent reality and the decision-makers have what could be called tunnel-vision and only consider what their models tell them.

"Part of the COVID problem was that the model being used was an old model created over a period of decades for a totally different context."

The article was written for the AI Summit Vision Aires website, with Richard being among the first experts from around the world to be invited to write for the site.