Covid-19 vaccine development

 

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.

Dimitris Bertsimas (left) is the Boeing Leaders for Global Operations Professor of Management. Najat Khan is the chief data science officer and global head of strategy and operations for Janssen Research & Development.

Scientists at Janssen Research & Development, developers of the Johnson & Johnson-Janssen 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 MIT, 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.

DELPHI

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 faculty lead of entrepreneurship for the Jameel Clinic, brought his group of 25-plus doctoral and master’s students together to discuss how they could use their collective skills in machine learning and optimization to create new tools to aid the world in combating the spread of the disease.

The group started tracking their efforts on the COVIDAnalytics platform, where their models are generating accurate real-time insight into the pandemic. One of the group’s first projects was charting the progression of Covid-19 with an epidemiological model they developed named DELPHI, which predicts state-by-state infection and mortality rates based upon each state’s policy decision.

DELPHI is based on the standard SEIR model, a compartmental model that simplifies the mathematical modeling of infectious diseases by dividing populations in four categories: susceptible, exposed, infectious, and recovered. The ordering of the labels is intentional to show the flow patterns between the compartments. DELPHI expands on this model with a system that looks at 11 possible states of being to account for realistic effects of the pandemic, such comparing the length of time those who recovered from Covid-19 spent in the hospital versus those who died.

“The model has some values that are hardwired, such as how long a person stays in the hospital, but we went deeper to account for the nonlinear change of infection rates, which we found were not constant and varied over different periods and locations,” says Bertsimas. “This gave us more modeling flexibility, which led the model to make more accurate predictions.”

A key innovation of the model is capturing the behaviors of people related to measures put into place during the pandemic, such as lockdowns, mask-wearing, and social distancing, and the impact these had on infection rates.

“By June or July, we were able to augment the model with these data. The model then became even more accurate,” says Bertsimas. “We also considered different scenarios for how various governments might respond with policy decisions, from implementing serious restrictions to no restrictions at all, and compared them to what we were seeing happening in the world. This gave us the ability to make a spectrum of predictions. One of the advantages of the DELPHI model is that it makes predictions on 120 countries and all 50 U.S. states on a daily basis.”

A vaccine for today’s pandemic

Being able to determine where Covid-19 is likely to spike next proved to be critical to the success of Janssen’s clinical trials, which were “event-based” — meaning that “we figure out efficacy based on how many ‘events’ are in our study population, events such as becoming sick with Covid-19,” explains Khan.

“To run a trial like this, which is very, very large, it’s important to go to hot spots where we anticipate the disease transmission to be high so that you can accumulate those events quickly. If you