Researchers, led by Retsef Levi, COVID Variant have developed an artificial intelligence (AI) model capable of forecasting SARS-CoV-2 variants likely to spark new waves of infections, a crucial advancement given the current challenges posed by the virus’s evolving nature. Traditional models, used for predicting viral transmission dynamics, often lack the capacity to discern variant-specific spread.
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COVID Variant
To address this gap, Levi and his team delved into the analysis of a vast dataset comprising 9 million SARS-CoV-2 genetic sequences sourced from the Global Initiative on Sharing Avian Influenza Data (GISAID) across 30 countries. The dataset also encompassed information on vaccination rates, infection rates, and other pertinent factors.
The insights gleaned from this extensive analysis were harnessed to construct a machine-learning-enabled risk assessment model. Remarkably, this model exhibits an impressive predictive capability, identifying 72.8% of variants in each country anticipated to cause a minimum of 1,000 cases per million people within the subsequent three months. Notably, this accuracy increases to 80.1% after two weeks of observation.
Several key indicators contribute significantly to the model’s efficacy in predicting variant infectiousness. These include the early trajectory of infections induced by a specific variant, the spike mutations characteristic of the variant, and the distinctiveness of a new variant’s mutations compared to those of the dominant variant during the observation period.
Encouragingly, the success of this modeling approach suggests its potential applicability in forecasting the trajectories of other infectious diseases. The researchers’ innovative AI model not only offers a promising tool for anticipating the impact of SARS-CoV-2 variants but also opens avenues for enhancing our ability to predict and manage the course of various infectious diseases in the future.
