UCLA machine-learning model is helping CDC predict spread of COVID-19

A machine-learning model developed at theUCLA Samueli School of Engineering is helping the Centers for Disease Control and Prevention predict the spread of COVID-19.The model was created by a team led by Quanquan Gu, a UCLA assistant professor of computer science, and it is now one of 13 models that feed into aCOVID-19 Forecast Hub at the University of Massachusetts Amherst. Data from that hub, in turn, feeds into the CDC ’sonline forecasts for how the disease might continue to spread.Gu said his model is more accurate than most others because it does not rely only on confirmed COVID-19 cases and fatalities. It is epidemiology-driven and is one of only two models in the hub that use machine learning.The model ’s name,UCLA-SuEIR, is derived from the five types of observed and inferred COVID-19 data that factor into its projections — the number of cases categorized as susceptible, unreported, exposed, infectious and recovered.The UCLA model is unique because it doesn ’t simply fit the current curve, which is based only on reported cases. Rather, it infers the number of untested and unreported cases from the model’s data analysis and uses those inferences to predict how quickly the disease will spread. This is called an “epidemic model” because it takes i nto account the various factors that affect the rate of disease spread.UCLA-SuEIR produces state- and county-level models based on the numbers of fatalities and confirmedcases reported by the New York Times, and n...
Source: UCLA Newsroom: Health Sciences - Category: Universities & Medical Training Source Type: news