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Introduction

• New and emerging variants of SARS-CoV-2 virus continue to pose a threat to the health of populations across the globe. Until January 2022, there have been more than 6,000 mutations in the spike gene of the SARS-CoV-2.
• Early prediction for emergence of new strains is critical for pandemic preparedness.
• Most of the currently available predictive models are based on the reported infections and deaths.
• But now researchers have come up with Strainflow Model. It is a supervised predictive model using features of SARS-CoV-2 genome sequences.

Strainflow Model

• Earlier models do not incorporate features from the virus sequences in a predictive manner.
• Strainflow, plugs this gap by taking a sequence-driven approach to predict future surges using a novel artificial intelligence pipeline.
• This study was based on a simple hypothesis — virus sequences can be treated as documents that can be read like a book by natural language understanding (NLU) models. Further, the models can discover the underlying “grammar” patterns which are causally predictive of future surges.
• Thus, Strainflow is a genomic surveillance model for SARS-CoV-2 genome sequences.
• Here, sequences are treated as documents with words (codons) to learn the codon context of 0.9 million spike genes using the skip-gram algorithm.
• The team experimented with several NLU models optimised for efficiently learning the “grammar of Spike gene”.
• The best model compressed the viral sequences in 36 dimensions. Each of these 36 dimensions is a different cocktail mix of codon level relationships. Some of these 36 cocktail mixtures may encode the patterns that make the virus spread faster.
• Time series analysis of the information shows their leading relationship with the monthly COVID-19 cases for seven countries (e.g., USA, Japan, India, and others).
• And Machine Learning modeling can help develop an epidemiological early warning system for the COVID-19 caseloads.

https://www.thehindu.com/sci-tech/science/covid-19-surge-preparedness-with-ai-genomic-surveillance/article65344737.ece

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