Model selection for seasonal influenza forecasting

Two firsts in one: "Model selection for seasonal influenza forecasting" is Alex's first first-author and my first last-author paper. It will appear in an upcoming issue of Infectious Disease Modelling, a new open access journal with a focus on the interface between mathematical modelling, data analysis, and public health decision support.

This paper presents a likelihood-based method for selecting models that best predict future data (when conditioned on past data), and uses it to evaluate the predictive skill of disease transmission models that incorporate various features, such as inhomogeneous population mixing and climatic effects on transmission. I won't say any more here; if you're interested, read the paper!

The source code used to generate all of the results presented in this manuscript is available under the BSD 3-Clause license.