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.
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% confidence. The study,"Spade4: Sparsity and Delay Embedding Based Forecasting of Epidemics," appears in the"COVID taught us that we really need to come up with methods that can predict with the least amount of information," said applied mathematics Ph.D.
When a disease breakout occurs—whether for new infections like COVID-19 or existing ones like Ebola—being able to predict the development of the disease is essential for making public policy decisions. "That's what policymakers need right at the beginning," Saha said."What should we do in the next seven days? How should I allocate resources?"
Traditionally, epidemiologists prefer to build and use complex models to understand the progression of epidemics. These models, however, have several drawbacks, Saha said. They require complex demographic information that is frequently unavailable at the beginning of an outbreak. Even if that detailed information is available, the models may not accurately reflect the complexity of the population or dynamics of the disease."By the time we're working on vaccines and cures, we're looking at longer-term data," Saha said."But when a newSaha, E et al, SPADE4: Sparsity and Delay Embedding Based Forecasting of Epidemics.
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