An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling

This repository contains code that demonstrate the application of approximate diffusion processes (SA) for environmental stochasticity, supporting the above paper. We have provided code and real/simulated data to reproduce the influenza modelling with SA/SDE. We have also included code and simulated data for the SIRS model. However, since the covid data is not publicly available we are only providing the code to fit the covid model. Access to the covid data can be made on request, see the Data availability statement in the paper.

Dependencies

Generic scientific Python stack: numpy, scipy, matplotlib, pandas, sklearn, seaborn, joblib.

The particle filter is implemented using Jax, to benefit from JIT compilation (and probable GPU usage down the line). Install numpyro which will also install Jax

To install NumPyro read the following: http://num.pyro.ai/en/stable/getting_started.html#installation

To wrapp the c++ code (needed to run the covid models) in python, the pybind11 package must be installed: https://pybind11.readthedocs.io/en/latest/installing.html#include-with-pypi

Influenza model

To run the fitting process, SA with 15 basis functions and SDE 100 particles for the SMC:

SIR_example.py --iterations 100000 --burnin 50000 --thin 50 --n_bases 15 --n_particles 100

To reproduce the MMD study, first run:

vary_n_study.py

then run

plot_mmd.py

Nonstationary signal (with SIRS model)

To run the fitting process, SA with 20 basis functions:

SIRS_example.py --iterations 500000 --burnin 250000 --thin 250 --n_bases 20

COVID19 model

Compile the c++ code

Go to ./models/COVID_CPP directory and then compile by using

python setup.py build_ext -i.

Then rename the generated *.so files to death_lik.so and seeiir_ode.so.

Do inference

Once you have access to the data, move these to the data directory. Then to run the model with random-walk stochasticity:

COVID_rw_example.py

and with the Brownian motion approximation

COVID_bma_example.py

Once MCMC is finished for both these, then run:

plot_covid.py to visualise the posterior predicitves.