Home

Household-structured modelling of Non-Pharmaceutical Intervention

The code in this repository accompanies an arXiv preprint and can be used to reproduce all the results presented in that preprint.

Modelling approach

We model households composed of children (0-19 years) and adults (20+ years) with a Susceptible-Exposed-Prodromal-Infectious- Recovered (SEPIR) compartmental structure. Main model features:

• Infection enters the household through external imports and the household experiences a local continuous time stochastic epidemic
• Import rates are determined by class-stratified population prevalence and class-stratified contact behaviour (POLYMOD)
• Large population assumption: the evolution of the entire population of households can be captured by a set of “self- consistent” ODEs ( Ross et al. 2010).

This ODE formulation has the advantage of mathematical tractability over networks and agent-based models. Dynamics can be calculated through numerical integration, as well as quantities like the early exponential growth rate.

The high degree of population heterogeneity leads to a large number of ODEs (on the order of 10000 or more), and so efficient computation is crucial.

Our model is implemented in Python, with parallel processing used to simulate large number of policies and scenarios at once.

Repository structure

• The scripts in model directory contain objects, functions and class definitions needed to run the analyses
• Each folder in the examples directory contains scripts for carrying out simulations and reproducing the results from our preprint
• The files in the inputs directory contain demographic and social contact data which we use to parameterise the households model

Reference

Please cite this work as

article{hilton2021,
  author = {Hilton, Joe and Riley, Heather and Pellis, Lorenzo and Aziza, Rabia
    and Brand, Sam and Kombe, Ivy K. and Ojal, John and Parisi, Andrea and
    Keeling, Matt and D. Nokes, James and Manson-Sawko, Robert and House,
    Thomas},
  title = {A computational framework for modelling infectiousdisease policy
    based on age and household structurewith applications to the COVID-19
    pandemic},
  archivePrefix={arXiv},
  month = {January},
  year = {2022}
}