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Calculate the proportion of individuals with disease in the population, or the proportion of nodes with at least one diseased individual, or the proportion of individuals with disease in each node.
# S4 method for SimInf_model
prevalence(model, formula, level, index, format = c("data.frame", "matrix"))A data.frame if format = "data.frame", else
a matrix.
The model with trajectory data to calculate
the prevalence from.
A formula that specifies the compartments that
define the cases with a disease or that have a specific
characteristic (numerator), and the compartments that define
the entire population of interest (denominator). The
left-hand-side of the formula defines the cases, and the
right-hand-side defines the population, for example,
I~S+I+R in a ‘SIR’ model (see
‘Examples’). The . (dot) is expanded to all
compartments, for example, I~. is expanded to
I~S+I+R in a ‘SIR’ model (see
‘Examples’). The formula can also contain a condition
(indicated by |) for each node and time step to further
control the population to include in the calculation, for
example, I ~ . | R == 0 to calculate the prevalence
when the recovered is zero in a ‘SIR’ model. The
condition must evaluate to TRUE or FALSE in each
node and time step. Note that if the denominator is zero, the
prevalence is NaN.
The level at which the prevalence is calculated at
each time point in tspan. 1 (population prevalence):
calculates the proportion of the individuals (cases) in the
population. 2 (node prevalence): calculates the proportion of
nodes with at least one case. 3 (within-node prevalence):
calculates the proportion of cases within each node. Default
is 1.
indices specifying the subset of nodes to include
when extracting data. Default (index = NULL) is to
extract data from all nodes.
The default (format = "data.frame") is to
generate a data.frame with one row per time-step with
the prevalence. Using format = "matrix" returns the
result as a matrix.
## Create an 'SIR' model with 6 nodes and initialize
## it to run over 10 days.
u0 <- data.frame(S = 100:105, I = c(0, 1, 0, 2, 0, 3), R = rep(0, 6))
model <- SIR(u0 = u0, tspan = 1:10, beta = 0.16, gamma = 0.077)
## Run the model to generate a single stochastic trajectory.
result <- run(model)
## Determine the proportion of infected individuals (cases)
## in the population at the time-points in 'tspan'.
prevalence(result, I ~ S + I + R)
## Identical result is obtained with the shorthand 'I~.'
prevalence(result, I ~ .)
## Determine the proportion of nodes with infected individuals at
## the time-points in 'tspan'.
prevalence(result, I ~ S + I + R, level = 2)
## Determine the proportion of infected individuals in each node
## at the time-points in 'tspan'.
prevalence(result, I ~ S + I + R, level = 3)
## Determine the proportion of infected individuals in each node
## at the time-points in 'tspan' when the number of recovered is
## zero.
prevalence(result, I ~ S + I + R | R == 0, level = 3)
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