A new model for predicting COVID-19’s impact using artificial intelligence (AI) dramatically outperforms other models, so much so that it has attracted the interest of public health officials across the country.
While existing models to predict the spread of a disease already exist, few, if any, incorporate AI, which allows a model to make predictions based on observations of what is actually happening—for example, increasing cases among specific populations—as opposed to what the model’s designers think will happen. With the use of AI, it is possible to discover patterns hidden in data that humans alone might not recognize.
“AI is a powerful tool, so it only makes sense to apply it to one of the most urgent problems the world faces,” says Yaser Abu-Mostafa (Ph.D. ’83), professor of electrical engineering and computer science, who led the development of the new CS156 model (so-named for the Caltech computer science class where it got its start).
The researchers evaluate the accuracy of the model by comparing it to the predictions of an ensemble model built by the Centers for Disease Control and Prevention from 45 major models from universities and institutes across the country. Using 1,500 predictions as points of comparison with the CDC ensemble, the researchers found that the CS156 model was more accurate than the ensemble model 58 percent of the time as of November 25. It also routinely outperforms the benchmark projections of the Institute for Health Metrics and Evaluation (IHME).
The CS156 model is an amalgam of several models developed by Abu-Mostafa and his group over the past nine months; the weight that each model has on the overall CS156 model’s output is determined based on its performance over the previous weeks (using statistics reported in The New York Times for comparison).