AMRSpreadModel

AMRSpreadModel is an R package designed to create and run deterministic models of the spread of antimalarial drug resistance. This package provides a range of functionalities to simulate malaria transmission dynamics and visualize the results.

Installation

To install the package, you can use the devtools package to install directly from GitHub:

if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}
devtools::install_github("ChaokunHong/MalariaResistSim")

Dependencies

The package imports several other R packages, including: odin, ICDMM, tidyr, stats, ggplot2, data.table, utils, dplyr, Rcpp, magrittr. These packages will be installed automatically when you install the AMRSpreadModel package.

Usage

Creating a Malaria Model

The malaria_model function is used to create a model of malaria transmission dynamics. You can either provide a list of parameters directly or generate parameters based on the Entomological Inoculation Rate (EIR) and treatment rate (ft).

Example 1: Create a model using parameters

library(AMRSpreadModel)

# Define parameters
params <- list(
  S = 1000, D = 10, A = 5, T = 3, Sv = 200, Ev = 10, Iv = 5,
  m = 0.1, a = 0.3, b = 0.55, phi = 0.3, fT = 0.5, rD = 0.1, rA = 0.1,
  rT_S = 0.1, rT_R = 0.1, e = 0.2, mu = 0.1, n = 10, cA = 0.1, cD = 0.2, cT = 0.3,
  ton = 5000, toff = 50000, res_time = 3000, init_res = 0.01, rTR_true = 0.1
)

# Create the model
model <- malaria_model(params = params)

# Print model summary
print(model)

Example 2: Create a model using EIR and treatment rate

# Create the model using EIR and ft
model <- malaria_model(EIR = 10, ft = 0.5)

# Print model summary
print(model)

Running the Model

Once the model is created, you can run it over a specified period and visualize the results. #### Example: Run the model for 1000 days

# Run the model for 1000 days
results <- model$run(0:1000)

# Plot the results
plot <- plot_model(results, res_time = 3000, ton = 5000, toff = 50000)
print(plot)

Running the Model Over Ranges of Parameters

The range_model function allows you to run the model over a range of parameter values and summarize the results. #### Example: Run the model over parameter ranges

# Define parameter ranges
param_ranges <- list(EIR = seq(1, 20, by = 1), ft = seq(0.1, 0.9, by = 0.1))

# Run the model over the parameter ranges
range_results <- range_model(param_ranges, run_time = 1000, time_points = c(100, 200, 300, 400, 500))

# Plot the results for the parameter ranges
plot <- plot_range(range_results, x_var = "EIR", y_var = "prevalence", color_var = "ft")
print(plot)

Using modelrun for Custom Simulations

The modelrun function allows you to run the model with custom parameters and visualize the results. #### Example: Run the model with custom parameters

# Define parameters
params <- list(
  S = 1000, D = 10, A = 5, T = 3, Sv = 200, Ev = 10, Iv = 5,
  m = 0.1, a = 0.3, b = 0.55, phi = 0.3, fT = 0.5, rD = 0.1, rA = 0.1,
  rT_S = 0.1, rT_R = 0.1, e = 0.2, mu = 0.1, n = 10, cA = 0.1, cD = 0.2, cT = 0.3,
  ton = 5000, toff = 50000, res_time = 3000, init_res = 0.01, rTR_true = 0.1
)

# Create the model
model <- malaria_model(params = params)

# Define custom time points
time_points <- c(0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)

# Run the model with custom time points
results <- model$run(time_points)

# Print the results
print(results)

Creating Equilibrium Initial Conditions

The equilibrium_init_create function generates initial conditions for the model based on equilibrium assumptions. #### Example: Create equilibrium initial conditions

# Define parameters
params <- list(EIR = 10, ft = 0.5, phi = 0.3, b = 0.55, m = 0.1, a = 0.3, cA = 0.1, cD = 0.2, cT = 0.3, n = 10, mu = 0.1, rD = 0.1, rA = 0.1, rT_S = 0.1, rT_R = 0.1)

# Create equilibrium initial conditions
equilibrium_init <- equilibrium_init_create(params)

# Print equilibrium initial conditions
print(equilibrium_init)

Generating Parameters Based on EIR and ft

The phi_eir_rel function generates a list of model parameters based on specified values of EIR and ft. #### Example: Generate parameters based on EIR and ft

# Generate parameters
params <- phi_eir_rel(EIR = 10, ft = 0.5)

# Print generated parameters
print(params)

Contributing

Contributions to the package are welcome. You can contribute by reporting issues, suggesting new features, or submitting pull requests.

License

This package is licensed under the GPL-3 license.

Contact

For any questions or issues, please contact Chaokun Hong at chaokunhong@gmail.com