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Create a SimInf_events object

Source: R/SimInf_events.R
SimInf_events.Rd

The argument events must be a data.frame with the following columns:

event

Four event types are supported by the current solvers: exit, enter, internal transfer, and external transfer. When assigning the events, they can either be coded as a numerical value or a character string: exit; 0 or 'exit', enter; 1 or 'enter', internal transfer; 2 or 'intTrans', and external transfer; 3 or 'extTrans'. Internally in SimInf, the event type is coded as a numerical value.

time

When the event occurs i.e., the event is processed when time is reached in the simulation. Can be either an integer or a Date vector. A Date vector is coerced to a numeric vector as days, where t0 determines the offset to match the time of the events to the model tspan vector.

node

The node that the event operates on. Also the source node for an external transfer event. 1 <= node[i] <= Number of nodes.

dest

The destination node for an external transfer event i.e., individuals are moved from node to dest, where 1 <= dest[i] <= Number of nodes. Set event = 0 for the other event types. dest is an integer vector.

n

The number of individuals affected by the event. n[i] >= 0.

proportion

If n[i] equals zero, the number of individuals affected by event[i] is calculated by sampling the number of individuals from a binomial distribution using the proportion[i] and the number of individuals in the compartments. Numeric vector. 0 <= proportion[i] <= 1.

select

To process an event[i], the compartments affected by the event are specified with select[i] together with the matrix E, where select[i] determines which column in E to use. The specific individuals affected by the event are sampled from the compartments corresponding to the non-zero entries in the specified column in E[, select[i]], where select is an integer vector.

shift

Determines how individuals in internal transfer and external transfer events are shifted to enter another compartment. The sampled individuals are shifted according to column shift[i] in matrix N i.e., N[, shift[i]], where shift is an integer vector. See above for a description of N. Unsued for the other event types.

Usage

SimInf_events(E = NULL, N = NULL, events = NULL, t0 = NULL)

Arguments

E

Each row corresponds to one compartment in the model. The non-zero entries in a column indicates the compartments to include in an event. For the exit, internal transfer and external transfer events, a non-zero entry indicate the compartments to sample individuals from. For the enter event, all individuals enter first non-zero compartment. E is sparse matrix of class dgCMatrix.

N

Determines how individuals in internal transfer and external transfer events are shifted to enter another compartment. Each row corresponds to one compartment in the model. The values in a column are added to the current compartment of sampled individuals to specify the destination compartment, for example, a value of 1 in an entry means that sampled individuals in this compartment are moved to the next compartment. Which column to use for each event is specified by the shift vector (see below). N is an integer matrix.

events

A data.frame with events.

t0

If events$time is a Date vector, then t0 determines the offset to match the time of the events to the model tspan vector, see details. If events$time is a numeric vector, then t0 must be NULL.

Value

S4 class SimInf_events

Examples

## Let us illustrate how movement events can be used to transfer
## individuals from one node to another.  Use the built-in SIR
## model and start with 2 nodes where all individuals are in the
## first node (100 per compartment).
u0 <- data.frame(S = c(100, 0), I = c(100, 0), R = c(100, 0))

## Then create 300 movement events to transfer all individuals,
## one per day, from the first node to the second node. Use the
## fourth column in the select matrix where all compartments
## can be sampled with equal weight.
events <- data.frame(event      = rep("extTrans", 300),
                     time       = 1:300,
                     node       = 1,
                     dest       = 2,
                     n          = 1,
                     proportion = 0,
                     select     = 4,
                     shift      = 0)

## Create an SIR model without disease transmission to
## demonstrate the events.
model <- SIR(u0      = u0,
             tspan  = 1:300,
             events = events,
             beta   = 0,
             gamma  = 0)

## Run the model and plot the number of individuals in
## the second node.  As can be seen in the figure, all
## indivuduals have been moved to the second node when
## t = 300.
plot(run(model), index = 1:2, range = FALSE)


## Let us now double the weight to sample from the 'I'
## compartment and rerun the model.
model@events@E[2, 4] <- 2
plot(run(model), index = 1:2, range = FALSE)


## And much larger weight to sample from the I compartment.
model@events@E[2, 4] <- 10
plot(run(model), index = 1:2, range = FALSE)


## Increase the weight for the R compartment.
model@events@E[3, 4] <- 4
plot(run(model), index = 1:2, range = FALSE)