events_SISe3: Example event data for the SISe3 model with cattle herds

Description

Dataset containing 783,773 scheduled events for a population of 1,600 cattle herds stratified by age over 1,460 days (4 years). Demonstrates how demographic, movement, and age-transition events affect SISe3 dynamics in a cattle disease context.

Usage

data(events_SISe3)

Arguments

Format

A data.frame

Details

This dataset contains four types of scheduled events that affect cattle herds (nodes) with age structure:

Exit: Deaths or removal of cattle from a herd (n = 182,535). These events remove cattle from susceptible or infected compartments across age categories.
Enter: Births or introduction of cattle to a herd (n = 182,685). These events add susceptible cattle, typically to the youngest age category.
Internal transfer: Age transitions or within-herd movements (n = 317,081). These events move cattle between age categories within a herd, reflecting maturation and changing infection risk with age.
External transfer: Movement of cattle between herds (n = 101,472). These events transfer cattle from one herd to another across age categories, potentially introducing infected animals.

The select column in the returned data frame is mapped to the columns of the internal select matrix as follows:

select = 1: Targets S_1 (Susceptible, age 1).
select = 2: Targets S_2 (Susceptible, age 2).
select = 3: Targets S_3 (Susceptible, age 3).
select = 4: Targets S_1 and I_1 (Susceptible and Infected, age 1).
select = 5: Targets S_2 and I_2 (Susceptible and Infected, age 2).
select = 6: Targets S_3 and I_3 (Susceptible and Infected, age 3).

The shift column is used for Internal transfer events to define the destination compartment. It corresponds to the column index in the internal N matrix that specifies the transition (e.g., moving from age 1 to age 2).

Events are distributed across all 1,600 herds over the 4-year period. These are synthetic data generated to illustrate how to incorporate scheduled events (including births, deaths, movements, and age transitions) into an age-structured compartment model in the SimInf framework. The higher event count compared to non-age-structured models reflects the addition of internal transfer events required for age category transitions.

The data contains:

event: Event type: "exit", "enter", "intTrans", or "extTrans".
time: Day when event occurs (1-1460).
node: Affected herd identifier (1-1600).
dest: Destination herd for external transfer events, else 0.
n: Number of cattle affected.
select: Model compartment to affect (see SimInf_events).
proportion: 0. Not used in this example.
shift: Determines how individuals in internal transfer events are shifted to enter another compartment.

Examples

Run this code

## For reproducibility, call the set.seed() function and specify the
## number of threads to use. To use all available threads, remove the
## set_num_threads() call.
set.seed(123)
set_num_threads(1)

## Create an 'SISe3' model with 1600 cattle herds (nodes) stratified
## by age, initialize it to run over 4*365 days and record data at
## weekly time-points. Add ten infected animals to age category 1 in
## the first herd to seed the outbreak.  Define 'tspan' to record the
## state of the system at weekly time-points. Load scheduled events
## events for the population of nodes with births, deaths and
## between-node movements of individuals.
u0 <- u0_SISe3
u0$I_1[1] <- 10
model <- SISe3(
    u0 = u0,
    tspan = seq(from = 1, to = 4*365, by = 7),
    events = events_SISe3,
    phi = rep(0, nrow(u0)),
    upsilon_1 = 1.8e-2,
    upsilon_2 = 1.8e-2,
    upsilon_3 = 1.8e-2,
    gamma_1 = 0.1,
    gamma_2 = 0.1,
    gamma_3 = 0.1,
    alpha = 1,
    beta_t1 = 1.0e-1,
    beta_t2 = 1.0e-1,
    beta_t3 = 1.25e-1,
    beta_t4 = 1.25e-1,
    end_t1 = 91,
    end_t2 = 182,
    end_t3 = 273,
    end_t4 = 365,
    epsilon = 0
)

## Display the number of cattle affected by each event type per day.
plot(events(model))

## Run the model to generate a single stochastic trajectory.
result <- run(model)

## Plot the median and interquartile range of the number of
## susceptible and infected individuals.
plot(result)

## Plot the proportion of nodes with at least one infected individual.
plot(result, I_1 + I_2 + I_3 ~ ., level = 2, type = "l")

## Plot the trajectory for the first herd.
plot(result, index = 1)

## Summarize the trajectory. The summary includes the number of events
## by event type.
summary(result)