Example event data for the SIS model with cattle herds

Dataset containing 466,692 scheduled events for a population of 1,600 cattle herds over 1,460 days (4 years). Demonstrates how demographic and movement events affect SIS dynamics in a cattle disease context.

Usage

events_SIS()

Value

A data.frame with columns:

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

Details

The event data contains three types of scheduled events that affect cattle herds (nodes):

Exit: Deaths or removal of cattle from a herd (n = 182,535). These events decrease the population in both susceptible and infected compartments.
Enter: Births or introduction of cattle to a herd (n = 182,685). These events add susceptible cattle to herds.
External transfer: Movement of cattle between herds (n = 101,472). These events transfer cattle from one herd to another, potentially spreading disease across the herd network. Either susceptible or infected animals may be transferred.

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

select = 1 corresponds to Enter events, targeting the Susceptible (S) compartment.
select = 2 corresponds to Exit and External Transfer events, targeting all compartments (S and I).

Events are distributed across all 1,600 herds over the 4-year period, reflecting realistic patterns of cattle demographic change and herd-to-herd movement. In SIS dynamics, these events can introduce disease to previously unaffected herds or remove infected cattle from the system.

Examples

## 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 'SIS' model with 1600 cattle herds (nodes) and initialize
## it to run over 4*365 days. Add one infected animal to the first
## herd to seed the outbreak. Define 'tspan' to record the state of
## the system at daily time-points. Load scheduled events for the
## population of nodes with births, deaths and between-node movements
## of individuals.
u0 <- u0_SIS()
u0$I[1] <- 1
model <- SIS(u0     = u0,
             tspan  = seq(from = 1, to = 4*365, by = 1),
             events = events_SIS(),
             beta   = 0.16,
             gamma  = 0.01)

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


## Summarize the trajectory. The summary includes the number of events
## by event type.
summary(result)
#> Model: SIS
#> Number of nodes: 1600
#> 
#> Transitions
#> -----------
#>  S -> beta*S*I/(S+I) -> I
#>  I -> gamma*I -> S
#> 
#> Global data
#> -----------
#>  Number of parameters without a name: 0
#>  - None
#> 
#> Local data
#> ----------
#>  Parameter Value
#>  beta      0.16 
#>  gamma     0.01 
#> 
#> Scheduled events
#> ----------------
#>  Exit: 182535
#>  Enter: 182685
#>  Internal transfer: 0
#>  External transfer: 101472
#> 
#> Network summary
#> ---------------
#>             Min. 1st Qu. Median Mean 3rd Qu. Max.
#>  Indegree:  40.0    57.0   62.0 62.1    68.0 90.0
#>  Outdegree: 36.0    57.0   62.0 62.1    67.0 89.0
#> 
#> Compartments
#> ------------
#>     Min. 1st Qu. Median  Mean 3rd Qu.  Max.
#>  S   0.0     7.0   10.0  44.2    96.0 218.0
#>  I   0.0     0.0   96.0  80.3   125.0 228.0

Example event data for the SIS model with cattle herds

Usage

Value

Details

See also

Examples