It’s been dubbed “Disease X” – the next global pandemic, which some experts predict is pretty much bound to happen.
Over the next decade, according to certain forecasts, there’s a one in four chance of another outbreak on the scale of Covid-19.
It could be influenza or coronavirus – or something completely new.
Covid-19, of course, infected and killed millions of people worldwide, so it’s a frightening prospect.
So could AI help to alleviate it?
Researchers in California are developing an AI-based early warning system that will examine social media posts to help predict future pandemics.
The researchers, from University of California, Irvine (UCI) and the University of California, Los Angeles (UCLA), are part of the US National Science Foundation’s Predictive Intelligence for Pandemic Prevention grant programme.
This funds research that “aims to identify, model, predict, track and mitigate the effects of future pandemics”.
The project builds on earlier work by UCI and UCLA researchers, including a searchable database of 2.3 billion US Twitter posts collected since 2015, to monitor public health trends.
Prof Chen Li is spearheading the project at UCI’s Department of Computer Science. He says they have been collecting billions of tweets on X, formerly known as Twitter, over the past few years.
The tool works by figuring out which tweets are meaningful and training the algorithm to help to detect early signs of a future pandemic, predict upcoming outbreaks, and evaluate the potential outcomes of specific public health policies, says Prof Chen.
“We have developed a machine-learning model for identifying and categorising significant events that may be indicative of an upcoming epidemic from social media streams.”
The tool, which is targeted at public health departments and hospitals, can also “evaluate the effects of treatments to the spread of viruses”, he says.
However, it’s not without problems. For example, it is reliant on X, a platform not accessible in some countries.
“The availability of data outside the US has been mixed,” admits Prof Chen.
“So far our focus has been within the US. We are working to overcome the data scarcity and potential bias when we expand the coverage to other regions of the world.”
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