https://interestingengineering.com/innovation/scientists-develop-ai-system-to-alert-us-of-next-pandemic
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Abstract concept of fighting Coronavirus global pandemic with AI machine learning.
We all know how devastating the COVID-19 pandemic has been – and it could have been even worse if not for the efforts of scientists and health workers around the world. But what if we could get a heads-up on the next most dangerous variants of a virus before they become a global threat?
Well, a new AI system can just do that. It can warn us about the emergence of dangerous virus variants in future pandemics, according to a study by scientists from Scripps Research and Northwestern University in the US.
The system, named early warning anomaly detection (EWAD), uses machine learning to analyze the genetic sequences, frequencies, and mortality rates of virus variants as they spread across the world.
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The researchers tested EWAD on real data from the COVID-19 pandemic and found that it was able to accurately predict which variants of concern (VOCs) would arise as the virus mutated. The system could also estimate how public health measures such as vaccines and mask-wearing would affect the evolution of the virus.
The study, published in the journal Cell Patterns, shows that EWAD could help us prepare for and respond to future outbreaks by identifying potential threats before they are officially designated by the World Health Organization (WHO).
“We could see key gene variants appearing and becoming more prevalent, as the mortality rate also changed, and all this was happening weeks before the VOCs containing these variants were officially designated by the WHO,” says William Balch, a microbiologist at Scripps Research and one of the lead authors of the study.
A new Scripps Research machine-learning system tracks how epidemic viruses evolve.Credits: Scripps Research, Graphic made using BioRender.com
The AI system uses a mathematical technique called Gaussian process-based spatial covariance, which can predict new data based on existing data and their relationships. The system can also detect patterns and rules of virus evolution that are otherwise hidden in the vast amount of data.
“One of the big lessons of this work is that it is important to take into account not just a few prominent variants, but also the tens of thousands of other undesignated variants, which we call the ‘variant dark matter,’” says Balch.
The researchers say that their system could also help us understand more about the basic biology of viruses and how they adapt to different environments. This could lead to better treatments and prevention strategies for viral diseases.