We are a team of faculty, research scientists, and pre- and post-doctoral trainees at Harvard, Stanford, the University of Maryland, and Yale.

We represent a diverse range of disciplinary backgrounds, including: epidemiology, virology, biostatistics, operations research, and health economics. Like so many other research teams around the world, we have dropped what we are doing, volunteered our time and effort, and convened to help predict and understand the trajectory of the COVID-19 pandemic.


We are distinguished from other COVID-19 modeling groups by our focus on decisions and the role that information plays in those decisions. A large number of us have advanced training in decision science and so-called value-of-information methods. This leads us to prioritize pragmatic decision support and the careful management of uncertainty in our modeling. We view the model-building exercise as a practical means of ‘taking stock’ of the evidence base: assembling what we know, identifying what don’t we know, and evaluating what we might be willing to give up (in terms of time, new infections, costs, and/or mortality) to acquire better information.

Our model development activities have both short- and long-term objectives: In the short-term, we aim to predict the impact of imminent public health control measures on the trajectory of the epidemic and on the use of scarce healthcare resources. We are using our models to identify what we know, what we don’t know, and where/how our data collection and analysis efforts might most usefully be deployed to improve longer-term decision making.