Don't miss the next session of the CTML Seminar Series on March 12th, where Karissa Huang and Philip Lee will discuss "Surrogate Modeling for Infectious Disease Dynamics Using Machine Learning." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.
Model complexity can often make parameter estimation challenging and inefficient in epidemiological models. In this work we propose a framework for using surrogate models to estimate the likelihood for compartmental models. The surrogate modeling approach reduces the computational costs of analyzing infectious disease dynamics, which is especially useful in contexts where timely results are needed.