README

Supporting code for the publication entitled: "Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models" in Nature Communications Medicine.

Data

The data used for this study was live operational data from NHS England definition given here. This influenza data is not in the public domain, we have therefore provided simulated data in this repository to show the models with.

The data is generated assuming a national epidemic wave, represented by local units that follow the same basic trend + perturbations.

Structure

The running of this code assumes the working directory is at the root folder level (ie where show_models.R sits).

The main file to run to test the models and visualise them is ~/show_models.R.

This script will:

1. install the package dependencies, from ~/src/depends.R (you may want to install here first to set working directories)
2. generate the simulated epidemic data from ~/simulate_hierarchical_epidemic.R
3. source and run the ARIMA model, from ~/run_arima.R
4. source and run the GAM model, from ~/run_gam.R
5. write out the different data to ~/data/
6. read in the data, plot it and score the models

The configuration for data generation, overall modelling, and GAM parameters can be adjusted in ~/config.yaml

Note that the models have not been pre-tuned.

• The bounds of available ranges for the ARIMA can be selected within ~/src/arima.R, though the model will select sensible values itself.
• The days per basis function for the GAM can be updated in the ~/config.yaml file.

The GAM model can either be run in parallel or sequentially. It runs on the scale of 1-5 minutes per fit, so adjust expectations on run time if going sequentially. The option to change this is in ~/config.yaml. In the default setting, it requires ~12 cores to run in parallel, one core per n_lookbacks.

Running

To test the code and different components you can:

1. run ~/simulate_heirarchical_epidemic.R, ~/run_arima.R and ~/run_gam.R separately to inspect each step or
2. bring it all together in ~/show_models.R which also has plotting and scoring code