Improved forecast with a combination of mechanistic and statistical predictive models
Predictive models based on past data could be good predictors of the future outcomes; however, they usually don't explain the causal and feedback relationships leading to the outcome. Conversely,
mechanistic models could uncover complex interaction between underlying processes, but sometimes their calibration and validation could be
unrealistic. Combining the two approaches into a semi-mechanistic model can lead to a winning combination. We present examples of historic
epidemic data as well as simulated data, where a combination of neural networks with a mechanistic Susceptible, Exposed, Infected and Recovered (SEIR) model produces more reliable predictions with less
Bobashev, G., Ellner, S. P., & Bailey, B. A. (2009). Improved forecast with a combination of mechanistic and statistical predictive models. In Technosocial Predictive Analytics; Papers from the 2009 AAAI Spring Symposium Menlo Park, CA: AAAI Press.