Researchers at the Massachusetts Institute of Technology (MIT) have successfully engineered a novel Artificial Intelligence (AI) model. This cutting-edge model demonstrates the capability to forecast in advance the specific SARS-CoV-2 variants that are more likely to trigger fresh waves of infections. The innovation stands as a significant stride in proactive pandemic management, enabling early identification and response to potential surges.
Currently, the models used to predict the dynamics of viral transmission do not predict variant-specific spread.
Under the leadership of Retsef Levi from MIT's Sloan School of Management, the research team delved into an extensive analysis of 9 million SARS-CoV-2 genetic sequences sourced from the Global Initiative on Sharing Avian Influenza Data (GISAID). The study encompassed data from 30 countries, scrutinizing various factors such as vaccination rates, infection rates, and other pertinent elements to discern the determinants influencing the spread of the virus. The findings are published in the journal PNAS Nexus.
The patterns that emerged from this analysis were used to build a Machine Learning-enabled risk assessment model. The model can detect 72.8 per cent of the variants in each country that will cause at least 1,000 cases per million people in the next three months after an observation period of only one week after detection. This predictive performance increases to 80.1 per cent after two weeks of observation. Among the strongest predictors that a variant will become infectious are the early trajectory of the infections caused by the variant, the variant's spike mutations, and how different the mutations of a new variant are from those of the most dominant variant during the observation period.
“This work provides an analytical framework that leverages multiple data sources, including genetic sequence data and epidemiological data via machine-learning models to provide improved early signals on the spread risk of new SARS-CoV-2 variants,” said the researchers in the study.
While calling for more research, they noted that the modelling approach could potentially be extended to other respiratory viruses such as influenza, avian flu viruses, or other coronaviruses.
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Inputs from IANS
