UCLS Gu ML on covid19 prediction

https://newsroom.ucla.edu/releases/machine-learning-model-cdc-covid19

UCLA-SuEIR produces state- and county-level models based on the numbers of fatalities and confirmed cases reported by the New York Times, and national models based on data reported by Johns Hopkins University.

The University of Massachusetts added UCLA’s model to its hub on May 6 after Gu sent details about his work to UMass biostatistics professor Nicolas Reich, the hub’s project lead. Gu’s team had noticed that several models in the hub were producing varying predictions, mostly based on curve-fitting models.

“Without any epidemic modeling, the projection by the curve-fitting model is very misleading since it only depends on the observed data pattern but ignores the underlying epidemic dynamics that drive the data,” Gu said.

UCLA Samueli School of Engineering

A diagram of the machine-learning model. One feature of of SuEIR is that people in both “exposed” and “infectious” can infect people in “susceptible,” as indicated by the dashed arrows.

The UCLA team checks its model’s accuracy by making a prediction one week in advance of future confirmed cases, death and recovered cases, then verifying it against the actual reported data. The model’s machine-learning algorithm enables Gu to train a new prototype in less than five seconds and allows the team to update its model on a daily basis, which is more efficient than other models. Gu’s team has actually created a total of 232 sub-models in all — one for the U.S. overall, as well as one for each state and 181 for any counties with more than 1,000 confirmed cases.

Gu said the UCLA model has been consistently the most accurate in the Massachusetts hub in predicting death counts for the U.S. and most states, and that it is among the top three models that best match their predictions with the actual number of reported deaths nationwide.