epizootic

An R package for spatially explicit population models of disease transmission in wildlife

https://github.com/viralemergence/epizootic

Keywords

Repository

An R package for spatially explicit population models of disease transmission in wildlife

README.Rmd

---
output: github_document
---



```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# epizootic 


[![R-CMD-check](https://github.com/viralemergence/epizootic/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/viralemergence/epizootic/actions/workflows/R-CMD-check.yaml)
[![CRAN status](https://www.r-pkg.org/badges/version/epizootic)](https://CRAN.R-project.org/package=epizootic)
[![Download_count](https://cranlogs.r-pkg.org/badges/grand-total/epizootic)](https://CRAN.R-project.org/package=epizootic)
[![Last commit](https://img.shields.io/github/last-commit/viralemergence/epizootic.svg)](https://github.com/viralemergence/epizootic/commits/master)


`epizootic` is an extension to `poems`, a spatially-explicit, process-explicit, pattern-oriented framework for modeling population dynamics. This extension adds functionality for modeling disease dynamics in wildlife. It also adds capability for seasonality and for unique dispersal dynamics for each life cycle stage.

## Installation

You can install the latest release on CRAN with:

``` r
install.packages("epizootic")
```

You can install the latest version of epizootic from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
install.packages("poems")
devtools::install_github("viralemergence/epizootic")
```

Because `epizootic` is an extension to `poems`, it is necessary to install `poems`
first.

## About R6 classes

`poems` and `epizootic` run on [R6](https://r6.r-lib.org/articles/Introduction.html) classes. R is primarily a *functional* programming language in which the primary units of programming are expressions and functions. Here we use R6 to create an *object-oriented* framework inside of R. R6 classes such as `DiseaseModel` and `SimulationHandler` are used to store model attributes, check them for consistency, pass them to parallel sessions for simulation, and gather results and errors.

## Example

Here is the initial state of an idealized theoretical disease scenario, following a SIR disease model with three life cycle stages: juvenile, yearling, and adult.

```{r initial_state}
library(poems)
library(purrr)
library(epizootic)
example_region <- Region$new(coordinates = data.frame(x = rep(seq(177.01, 177.05, 0.01), 5),
                             y = rep(seq(-18.01, -18.05, -0.01), each = 5)))
initial_abundance <- c(c(5000, 5000, 5000, 1, 0, 0, 0, 0, 0),
                          rep(c(5000, 5000, 5000, 0, 0, 0, 0, 0, 0), 24)) |>
      matrix(nrow = 9)
example_region$raster_from_values(initial_abundance[3,]) |>
  raster::plot(main = "Susceptible Adults")
example_region$raster_from_values(initial_abundance[4,]) |>
  raster::plot(main = "Infected Juveniles")
```

```{r user defined functions, include = F}
sir_model_summer <- function(inputs) {
  list2env(inputs, environment())
  params1 <-
    list(
      recovery = recovery,
      fecundity = fecundity,
      mortality = mortality,
      transmission = transmission,
      breeding_season_length = breeding_season_length
    ) |>
    map(\(x) x[c(1, 4, 7)])
  params2 <-
    list(
      recovery = recovery,
      fecundity = fecundity,
      mortality = mortality,
      transmission = transmission,
      breeding_season_length = breeding_season_length
    ) |>
    map(\(x) x[c(2, 5, 8)])
  params3 <-
    list(
      recovery = recovery,
      fecundity = fecundity,
      mortality = mortality,
      transmission = transmission,
      breeding_season_length = breeding_season_length
    ) |>
    map(\(x) x[c(3, 6, 9)])
  init_list1 <- array_branch(segment_abundance[, occupied_indices], 2) |> 
    map(\(x) x[c(1, 4, 7)])
  init_list2 <- array_branch(segment_abundance[, occupied_indices], 2) |> 
    map(\(x) x[c(2, 5, 8)])
  init_list3 <- array_branch(segment_abundance[, occupied_indices], 2) |> 
    map(\(x) x[c(3, 6, 9)])
  
  demographic_sir_model <- function(time, state, params, ...) {
    # Unlist parameters from the params list, and convert as necessary
    transmission <- params[["transmission"]]
    recovery <- params[["recovery"]]
    bsl <- params[["breeding_season_length"]][1]
    death <- params[["mortality"]] / bsl
    
    # Unpack parameters from the state vector using indices
    S1 <- state[1]  # Susceptible class 1
    
    I1 <- state[2]  # Infected class 1
    
    R1 <- state[3]  # Recovered class 1
    
    dS1dt <- -transmission[1] * S1 * I1 - death[1] * S1
    dI1dt <- (transmission[1] * S1 * I1) - (recovery[1] * I1) - death[1] * I1
    dR1dt <- recovery[1] * I1 - death[1] * R1
    
    # Output: instantaneous change in the nine disease states for three classes
    return(list(c(dS1dt, dI1dt, dR1dt)))
  }
  
  # Solve the ordinary differential equations
  sir_sol2 <- map2(list(init_list1, init_list2, init_list3),
                   list(params1, params2, params3),
                   \(x, p) {
                     map(x,
                         \(y) deSolve::ode(
                           y = y,
                           times = seq(1, breeding_season_length[1], 1),
                           func = demographic_sir_model,
                           parms = p
                         ) |> _[breeding_season_length[1], 2:4] |> round())
                   })
  
  # Assign populations to occupied indices in segment_abundance
  for (i in 1:length(occupied_indices)) {
    segment_abundance[c(1, 4, 7), occupied_indices[i]] <- sir_sol2[[1]][[i]]
    segment_abundance[c(2, 5, 8), occupied_indices[i]] <- sir_sol2[[2]][[i]]
    segment_abundance[c(3, 6, 9), occupied_indices[i]] <- sir_sol2[[3]][[i]]
  }
  
  return(segment_abundance)
}

disperser <- function(params) {
  segment_abundance <- params$segment_abundance[, params$occupied_indices]
  # Iterate over each column
  for (col in 1:ncol(segment_abundance)) {
    # Operate row by row to maintain compartment integrity
    for (row in 1:nrow(segment_abundance)) {
      # Only proceed if the current cell has a positive number
      if (segment_abundance[row, col] > 0) {
        # Generate a random integer number for dispersal
        # This number should not exceed the current cell's population
        random_number <- runif(1, 
                               min = 1, 
                               max = segment_abundance[row, col]) |> round()
        
        # Subtract the random number from the current cell
        segment_abundance[row, col] <- segment_abundance[row, col] - random_number
        
        # Generate a random column index to add the random number to, excluding
        # the current column
        random_column <- sample(setdiff(1:ncol(segment_abundance), col), 1)
        
        # Add the random number to the corresponding disease compartment in the
        # random column
        segment_abundance[row, random_column] <-
          segment_abundance[row, random_column] + random_number
      }
    }
  }
  params$segment_abundance[, params$occupied_indices] <- segment_abundance
  return(params$segment_abundance)
}

```

Here I create a `DiseaseModel` object, which stores inputs for disease simulations and checks them for consistency and completeness.

```{r disease_model}
model_inputs <- DiseaseModel$new(
  time_steps = 10,
    seasons = 2,
    populations = 25,
    stages = 3,
    compartments = 3, # indicates disease compartments
    region = example_region,
    initial_abundance = initial_abundance,
    # Dimensions of carrying_capacity are populations by timesteps
    carrying_capacity = matrix(100000, nrow = 25, ncol = 10),
    # Indicates length of breeding season in days for each population
    breeding_season_length = rep(100, 25),
    # One mortality value for each stage and compartment
    mortality = c(0.4, 0.2, 0, 0.505, 0.25, 0.105, 0.4, 0.2, 0),
    # Indicates that these are seasonal mortality values
    mortality_unit = 1,
    # No reproduction in this simple example
    fecundity = 0,
    fecundity_unit = 1,
    fecundity_mask = rep(0, 9),
    # Transmission rates from infected individuals, one for each stage
    transmission = c(0.00002, 0.00001, 7.84e-06),
    # Indicates that these are daily transmission rates
    transmission_unit = 0,
    # Indicates that all stages in the first compartment, S, can be infected
    transmission_mask = c(1, 1, 1, 0, 0, 0, 0, 0, 0),
    recovery = c(0.05714286, 0.06, 0.1),
    recovery_unit = 0,
    # Indicates that all stages in the second compartment, I, can recover
    recovery_mask = c(0, 0, 0, 1, 1, 1, 0, 0, 0),
    season_functions = list(sir_model_summer, NULL), 
    dispersal = list(disperser),
    simulation_order = list(c("transition", "season_functions", "results"),
                            c("dispersal", "results")),
    verbose = F
)
model_inputs$is_complete()
model_inputs$is_consistent()
```

The core simulation engine of `epizootic` is the function `disease_simulator`, which simulates spatially explicit disease dynamics in populations. Here I show the results
from the non-breeding season in the tenth year of the simulation.

```{r disease_simulator}
results <- disease_simulator(model_inputs)
results$abundance_segments$stage_3_compartment_1[,10,2] |>
  example_region$raster_from_values() |>
  raster::plot(main = "Susceptible Adults")
results$abundance_segments$stage_3_compartment_2[,10,2] |>  
  example_region$raster_from_values() |>
  raster::plot(main = "Infected Adults")
```

Owner

GitHub Events

Packages