More than 1000 genes may contribute to a person's risk of developing severe covid-19, on top of life circumstances such as their age, ethnicity and any health conditions. Most of the genes, discovered in a study of more than 1 million people, affect the functioning of two kinds of immune cells. If the results are confirmed, they could inform a test that assesses a person's risk of getting badly ill with covid-19, says Johnathan Cooper-Knock at the University of Sheffield, UK. "We know there are young people who are otherwise fit that get severe covid," he says. "We are trying to get at the genetic determinants that put people at risk irrespective of the more obvious things." Cooper-Knock's team used artificial intelligence to analyse results from a global dataset called The Covid-19 Host Genetics Initiative, a genetics project run by a group of researchers and companies.

Duality Technologies partners with Harvard Medical School and Two Six Labs to accelerate privacy-preserving, collaborative machine learning protected through Homomorphic Encryption. This capability supports research into COVID-19 susceptibility while keeping patient data private and secure. Duality Technologies, a leading provider of Privacy-Enhancing Technologies (PETs), announced that it has contracted with the Defense Advanced Research Projects Agency(DARPA) to develop a privacy-preserving Machine Learning (ML) capability that can train models on encrypted data from multiple sources. The capabilities developed under the contract will be applied to researching genomic susceptibility to severe COVID-19 symptoms in a manner which preserves individual privacy. The development is based on Homomorphic Encryption (HE), a privacy-enhancing technology that allows multiple parties to analyze encrypted data and gain insights without exposing Personally Identifiable Information (PII).