multigroup.vaccine

Overview

multigroup.vaccine models infectious disease dynamics in populations with multiple distinct subgroups that have:

• Different vaccination rates
• Different vaccine effectiveness
• Differential contact patterns between and within groups

The package provides both an interactive Shiny dashboard and programmatic R functions for epidemiological modeling and outbreak forecasting.

Installation

From R-universe (Recommended)

install.packages("multigroup.vaccine", 
                 repos = "https://epiforesite.r-universe.dev")

From GitHub

# install.packages("remotes")
remotes::install_github("EpiForeSITE/multigroup-vaccine")

Dependencies

The package depends on the following R packages:

• Imports: deSolve, graphics, shiny, stats, bslib (>= 0.8.0), htmltools, socialmixr
• Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, epiworldR

Note that some vignettes require ggplot2 and other suggested packages.

Quick Start

Interactive Dashboard

Launch the Shiny dashboard for interactive modeling:

library(multigroup.vaccine)
run_my_app()

The dashboard models two distinct sub-populations with differential within-group and across-group contact rates and different vaccination adherence levels. See Nguyen et al. (2024) for more details on this modeling approach.

Programmatic Usage

You can also use the package functions directly in R scripts. Here’s an example of comparing populations with different vaccination rates:

Two-Group Population Comparison

# Compare populations with different vaccination rates
results <- getFinalSize(
  vacPortion = c(0.9, 0.3),      # 90% vs 30% vaccinated
  popSize = c(10000, 10000),      # Equal population sizes
  R0 = 15,                        # Basic reproduction number
  recoveryRate = 0.1,             # 1/recovery rate = 10 days
  relContact = 0.05,              # 5% contact between groups
  contactWithinGroup = c(0.8, 0.8), # 80% within-group contact
  relSusc = c(0.1, 1)            # 10% relative susceptibility if vaccinated
)

print(results)

Features

• Multi-group SIR modeling with vaccination and variable contact rates
• Age-structured population models using census data
• Contact matrix integration via POLYMOD and custom matrices
• Final outbreak size calculations using both analytic and stochastic methods
• Interactive Shiny dashboard for scenario exploration
• Real-world case studies including measles outbreaks with census data

Documentation

Comprehensive documentation and vignettes are available at: https://epiforesite.github.io/multigroup-vaccine/

View all available vignettes:

browseVignettes("multigroup.vaccine")

Core Functions

• getOutputTable(): Run age-structured outbreak simulations with vaccination
• getFinalSize(): Calculate final outbreak size for two-group populations
• getFinalSizeDist(): Stochastic outbreak simulations with distributional results
• transmissionRates(): Calculate transmission rate matrices from R0 and contact patterns
• contactMatrixPolymod(): Generate age-structured contact matrices from POLYMOD data
• getCensusData(): Download and process US Census Bureau population data
• run_my_app(): Launch the interactive Shiny dashboard

Getting Help

• Bug reports: GitHub Issues
• Documentation: Package website

Citation

If you use this package in your research, please obtain citation information in R:

citation("multigroup.vaccine")

Development Setup

For local development:

# Clone the repository
# git clone https://github.com/EpiForeSITE/multigroup-vaccine.git

# Install development dependencies
install.packages(c("devtools", "roxygen2", "pkgdown", "lintr"))

# Load the package for development
devtools::load_all()

# Run tests
devtools::test()

# Check package
devtools::check()

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

This package is part of the EpiForeSITE software ecosystem developed by the ForeSITE Group at the University of Utah. Development was supported by the Centers for Disease Control and Prevention’s Center for Forecasting and Outbreak Analytics (Cooperative agreement CDC-RFA-FT-23-0069).

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EpiForeSITE: Epidemiological Forecasting and Scenario Modeling Initiative for Translational Epidemiology