蓝莓视频

A team of researchers at the University of 蓝莓视频 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鈥� progressions with 95 per cent confidence.

鈥淐ovid taught us that we really need to come up with methods that can predict with the least amount of information,鈥� said applied mathematics PhD candidate Esha Saha, the lead author of the study. 鈥淚f we have a new virus emerge and testing has just started, we have to know what to do in the short-term.鈥�

When a disease breakout occurs 鈥� whether for new infections like Covid-19 or existing ones like Ebola 鈥� being able to predict the development of the disease is essential for making public policy decisions.

鈥淭hat鈥檚 what policymakers need right at the beginning,鈥� Saha said. 鈥淲hat should we do in the next seven days? How should I allocate resources?鈥�

Traditionally, epidemiologists prefer to build and use complex models to understand the progression of epidemics.听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. 听

The 蓝莓视频 research team鈥檚 new model addresses these drawbacks.

鈥淏y the time we鈥檙e working on vaccines and cures, we鈥檙e looking at longer-term data,鈥� Saha said. 鈥淏ut when a new disease arrives, this method can help give us insight into how to behave.鈥�

The study, appears in the Bulletin of Mathematical Biology.