Take a data-driven approach rooted in epidemiology to forecast infections and deaths from the COVID-19 / coronavirus epidemic in the US and around the world
Our COVID-19 prediction model adds the power of artificial intelligence on top of a classic infectious disease model. We developed a simulator based on the SEIR/SEIS model (Wikipedia) to simulate the COVID-19 epidemic in each region. The parameters/inputs of this simulator are then learned using machine learning techniques that attempts to minimize the error between the projected outputs and the actual results. We utilize daily deaths data reported by each region to forecast future reported deaths. After some additional validation techniques (to minimize a phenomenon called overfitting), we use the learned parameters to simulate the future and make projections.
The goal of this project is to showcase the strengths of artificial intelligence to tackle one of the world’s most difficult problems: predict the track of a pandemic. Here, we use a pure data-driven approach by letting the machine do the learning.
We are currently making projections for: the United States, all 50 US states + DC, and 40 countries (including all 27 EU countries).