When starting a vaccine program, scientists generally have anecdotal understanding of the disease they’re aiming to target. When Covid-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease.
Scientists at Janssen Research & Development, developers of the Johnson & Johnson Covid-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company’s research efforts into a potential vaccine.
“Data science and machine learning can be used to augment scientific understanding of a disease,” says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. “For Covid-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms.”
In preparing for clinical studies of Janssen’s lead vaccine candidate, Khan put out a call for collaborators on predictive modeling efforts to partner with her data science team to identify key locations to set up trial sites. Through Regina Barzilay, the MIT School of Engineering Distinguished Professor for AI and Health, faculty lead of AI for MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, and a member of Janssen’s scientific advisory board, Khan connected with Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management at the MIT Sloan School of Management, who had developed a leading machine learning model that tracks Covid-19 spread in communities and predicts patient outcomes, and brought him on as the primary technical partner on the project.
When the World Health Organization declared Covid-19 a pandemic in March 2020 and forced much of the world into lockdown, Bertsimas, who is also the
This article is trimmed, please visit the source to read the full article.