A team of researchers at the University of Waterloo and Dalhousie University have developed a method for forecasting the short-term progression of an epidemic using extremely limited amounts of data.
Their model, the Sparsity and Delay Embedding-based Forecasting model, or SPADE4, uses machine learning to predict the progression of an epidemic using only limited infection data. SPADE4 was tested on both simulated epidemics and real data from the fifth wave of the Covid-19 pandemic in Canada and successfully predicted the epidemics’ progressions with 95 per cent confidence.
“Covid taught us that we really need to come up with methods that can predict with the least amount of information,” said applied mathematics PhD candidate Esha Saha, the lead author of the study. “If we have a new virus emerge and testing has just started, we have to know what to do in the short-term.”
Read the full article from Waterloo News to learn more.