SEITR Network Analysis

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This project implements a SEITR (Susceptible, Exposed, Infected, Treated, Recovered) model for network analysis using R. The model simulates the spread of a disease through a network of individuals, allowing for various network types and parameters.

Table of Contents

• Installation
• Usage
  • SEITR_network
    • Parameters
  • compare_experiment_sets
• Examples
• Contributing
• License

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Installation

To use this project, you need to have R installed on your system. You can install the required packages using the following commands:

install.packages(c("igraph", "deSolve", "ggplot2", "dplyr"))

Installing the Package from GitHub

You can install the SEITR Network Analysis package directly from GitHub using the devtools package. First, make sure you have devtools installed:

install.packages("devtools")

Then, use the install_github function to install the package:

devtools::install_github("skaraoglu/SEITRNet")

Loading the Package

After installing the package, you can load it using the library function:

library(SEITRNet)

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Usage

SEITR_network

The SEITR_network function performs SEITR network analysis.

Parameters

• n: (int) Number of nodes in the network.

• network_type: (str) The type of network to create. This can be one of the following:

  • ER: Erdős-Renyi random graph
  • BA: Barabasi-Albert scale-free network
  • WS: Watts-Strogatz small-world network
  • LN: Lattice network
  • RR: Random regular network

• n_par1: (float) First network parameter. Assigned for "p" argument in Erdős-Renyi and Watts-Strogatz graphs, assigned for "k" argument for Lattice and Random regular, n * n_par1 is used as "m" for Barabási-Albert networks.

• n_par2: (float) Second network parameter that is only needed for Watts-Strogatz networks to assign "k" argument.

• Lambda: (float) Birth rate.

• alpha1: (float) Treatment rate

• alpha2: (float) Recovery rate from treatment

• delta_I: (float) Death rate from infection

• delta_T: (float) Death rate from treatment

• mu: (float) Natural death rate

• beta1: (float) The infection rate parameter.

• beta2: (float) The exposure rate parameter.

• beta3: (float) The recovery rate parameter.

• initial_statuses: (int) The initial status of the nodes in the network. Where each status can be one of the following:

  S: Susceptible, E: Exposed, I: Infected, T: Treatment, R: Recovered

• t: (int) Time period.

• num_exp: (int) The number of experiments to run.

• verbose: (bool) Verbose output.

SEITR_network <- function(network_type="ER", n=100, n_par1=.9, n_par2=10, Lambda=1.1, beta1=.8, beta2=.18, beta3=.02, alpha1=.1, alpha2=.055, delta_I=.03, delta_T=.03, mu=.01, S=85, E=5, I=10, Tt=0, R=0, N=100, t=100, num_exp = 10, verbose = F, state = NULL, parameters = NULL) {
  # Function implementation
}

Compare Experiment Sets

The compare_experiment_sets function compares the results of multiple experiment sets. Currently inactive.

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Examples

Here are some examples of how to use the functions in this project:

Example 1: SEITR Network Analysis

# Perform SEITR network analysis
ws_p.1_k20 <- SEITR_network("WS", n_par1=0.1, n_par2=20, num_exp = 3)
er_p.2 <- SEITR_network("ER", n_par1=0.2, num_exp = 3)
ba_m.75 <- SEITR_network("BA", n_par1=0.75, num_exp = 3)

Example 2: Compare Experiment Sets

# Compare the results of multiple experiment sets
compare_experiment_sets(list(ws_p.1_k20, er_p.2, ba_m.75))

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Citation

If you use this pipeline or build on this work, please cite: Karaoglu, S., Imran, M. & McKinney, B.A. Network-based SEITR epidemiological model with contact heterogeneity: comparison with homogeneous models for random, scale-free and small-world networks. Eur. Phys. J. Plus 140, 551 (2025). https://doi.org/10.1140/epjp/s13360-025-06481-z