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A mathematical compartmental transmission model for RSV

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rsvmod

A mathematical compartmental transmission model for RSV

We developed a dynamic compartmental model of Respiratory Syncytial Virus (RSV) fitted to individually-linked population-based laboratory, perinatal and hospitalisation data for 2000-2012 from metropolitan Western Australia (WA), stratified by age and prior exposure. We account for the differential risk of RSV-hospitalisation in full-term and preterm infants (defined as <37 weeks gestation). We formulated a function relating age, RSV exposure history, and preterm status to the risk of RSV-hospitalisation given infection. Further details of this work can be found here https://doi.org/10.1186/s12879-024-09400-2. The wrapper file, main.r provides examples of how to run the model and produce some of the key plots.

A basic summary of the R code files follows. The functions are described in further detail in the comments above the relevant code.

data.r

This file contains descriptions of the two data files used. Note that the timeseries of monthly WA RSV-hospitalisations used in model fitting is not provided due to confidentiality issues.

parameters.r

Key parameter values are set here, including the estimated fitted values of the seasonal forcing function and exponential function that relates infections (by age) to hospitalisations (the age-to-risk function).

models.r

Contains the functions that define the model ODEs. These functions are used by the R package deSolve to solve the ODEs. The deSolve_base model does not stratify the population by gestational age whereas deSolve_preTerm stratifies the population into preterm (born <37 weeks gestational age) and full-term.

functions.r

This file contains functions to set up the age structure used in the model, a function to create the contact matrix (using the R package conmat and with resulting matrix saved as a .rda data file), functions used to set up the likelihood functions for model fitting using an mcmc process as implemented in the R package lazymcmc, and functions to find average age values.

diagnostics.r

This file contains functions to produce a range of different plots.

Linked publications

If you wish to use any part of this code, please reference

Giannini, F., Hogan, A.B., Sarna, M. et al. Modelling respiratory syncytial virus age-specific risk of hospitalisation in term and preterm infants. BMC Infect Dis 24, 510 (2024). https://doi.org/10.1186/s12879-024-09400-2

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