Calculate prevalence from a model object with trajectory data
Source:R/prevalence.R
prevalence-SimInf_model-method.RdCalculate 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.
Usage
# S4 method for class 'SimInf_model'
prevalence(model, formula, level, index, format = c("data.frame", "matrix"))Arguments
- model
The
modelwith trajectory data to calculate the prevalence from.- formula
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+Rin a ‘SIR’ model (see ‘Examples’). The.(dot) is expanded to all compartments, for example,I~.is expanded toI~S+I+Rin 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 == 0to calculate the prevalence when the recovered is zero in a ‘SIR’ model. The condition must evaluate toTRUEorFALSEin each node and time step. Please note, if the denominator is zero, the prevalence isNaN. Additionally, whenlevel=3(within-node prevalence) and the formula contains a condition that evaluates toFALSE, the prevalence is alsoNaN.- level
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 is1.- index
indices specifying the subset of nodes to include when extracting data. Default (
index = NULL) is to extract data from all nodes.- format
The default (
format = "data.frame") is to generate adata.framewith one row per time-step with the prevalence. Usingformat = "matrix"returns the result as a matrix.
Examples
## 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)
#> time prevalence
#> 1 1 0.01127214
#> 2 2 0.01288245
#> 3 3 0.01127214
#> 4 4 0.01288245
#> 5 5 0.01449275
#> 6 6 0.01449275
#> 7 7 0.01771337
#> 8 8 0.01610306
#> 9 9 0.01449275
#> 10 10 0.01610306
## Identical result is obtained with the shorthand 'I~.'
prevalence(result, I ~ .)
#> time prevalence
#> 1 1 0.01127214
#> 2 2 0.01288245
#> 3 3 0.01127214
#> 4 4 0.01288245
#> 5 5 0.01449275
#> 6 6 0.01449275
#> 7 7 0.01771337
#> 8 8 0.01610306
#> 9 9 0.01449275
#> 10 10 0.01610306
## Determine the proportion of nodes with infected individuals at
## the time-points in 'tspan'.
prevalence(result, I ~ S + I + R, level = 2)
#> time prevalence
#> 1 1 0.5
#> 2 2 0.5
#> 3 3 0.5
#> 4 4 0.5
#> 5 5 0.5
#> 6 6 0.5
#> 7 7 0.5
#> 8 8 0.5
#> 9 9 0.5
#> 10 10 0.5
## Determine the proportion of infected individuals in each node
## at the time-points in 'tspan'.
prevalence(result, I ~ S + I + R, level = 3)
#> node time prevalence
#> 1 1 1 0.000000000
#> 2 2 1 0.009803922
#> 3 3 1 0.000000000
#> 4 4 1 0.019047619
#> 5 5 1 0.000000000
#> 6 6 1 0.037037037
#> 7 1 2 0.000000000
#> 8 2 2 0.009803922
#> 9 3 2 0.000000000
#> 10 4 2 0.019047619
#> 11 5 2 0.000000000
#> 12 6 2 0.046296296
#> 13 1 3 0.000000000
#> 14 2 3 0.009803922
#> 15 3 3 0.000000000
#> 16 4 3 0.019047619
#> 17 5 3 0.000000000
#> 18 6 3 0.037037037
#> 19 1 4 0.000000000
#> 20 2 4 0.019607843
#> 21 3 4 0.000000000
#> 22 4 4 0.019047619
#> 23 5 4 0.000000000
#> 24 6 4 0.037037037
#> 25 1 5 0.000000000
#> 26 2 5 0.019607843
#> 27 3 5 0.000000000
#> 28 4 5 0.028571429
#> 29 5 5 0.000000000
#> 30 6 5 0.037037037
#> 31 1 6 0.000000000
#> 32 2 6 0.019607843
#> 33 3 6 0.000000000
#> 34 4 6 0.028571429
#> 35 5 6 0.000000000
#> 36 6 6 0.037037037
#> 37 1 7 0.000000000
#> 38 2 7 0.019607843
#> 39 3 7 0.000000000
#> 40 4 7 0.028571429
#> 41 5 7 0.000000000
#> 42 6 7 0.055555556
#> 43 1 8 0.000000000
#> 44 2 8 0.009803922
#> 45 3 8 0.000000000
#> 46 4 8 0.028571429
#> 47 5 8 0.000000000
#> 48 6 8 0.055555556
#> 49 1 9 0.000000000
#> 50 2 9 0.009803922
#> 51 3 9 0.000000000
#> 52 4 9 0.028571429
#> 53 5 9 0.000000000
#> 54 6 9 0.046296296
#> 55 1 10 0.000000000
#> 56 2 10 0.009803922
#> 57 3 10 0.000000000
#> 58 4 10 0.028571429
#> 59 5 10 0.000000000
#> 60 6 10 0.055555556
## 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)
#> node time prevalence
#> 1 1 1 0.000000000
#> 2 2 1 0.009803922
#> 3 3 1 0.000000000
#> 4 4 1 0.019047619
#> 5 5 1 0.000000000
#> 6 6 1 0.037037037
#> 7 1 2 0.000000000
#> 8 2 2 0.009803922
#> 9 3 2 0.000000000
#> 10 4 2 0.019047619
#> 11 5 2 0.000000000
#> 12 6 2 0.046296296
#> 13 1 3 0.000000000
#> 14 2 3 0.009803922
#> 15 3 3 0.000000000
#> 16 4 3 0.019047619
#> 17 5 3 0.000000000
#> 18 6 3 NaN
#> 19 1 4 0.000000000
#> 20 2 4 0.019607843
#> 21 3 4 0.000000000
#> 22 4 4 0.019047619
#> 23 5 4 0.000000000
#> 24 6 4 NaN
#> 25 1 5 0.000000000
#> 26 2 5 0.019607843
#> 27 3 5 0.000000000
#> 28 4 5 0.028571429
#> 29 5 5 0.000000000
#> 30 6 5 NaN
#> 31 1 6 0.000000000
#> 32 2 6 0.019607843
#> 33 3 6 0.000000000
#> 34 4 6 0.028571429
#> 35 5 6 0.000000000
#> 36 6 6 NaN
#> 37 1 7 0.000000000
#> 38 2 7 0.019607843
#> 39 3 7 0.000000000
#> 40 4 7 0.028571429
#> 41 5 7 0.000000000
#> 42 6 7 NaN
#> 43 1 8 0.000000000
#> 44 2 8 NaN
#> 45 3 8 0.000000000
#> 46 4 8 0.028571429
#> 47 5 8 0.000000000
#> 48 6 8 NaN
#> 49 1 9 0.000000000
#> 50 2 9 NaN
#> 51 3 9 0.000000000
#> 52 4 9 0.028571429
#> 53 5 9 0.000000000
#> 54 6 9 NaN
#> 55 1 10 0.000000000
#> 56 2 10 NaN
#> 57 3 10 0.000000000
#> 58 4 10 0.028571429
#> 59 5 10 0.000000000
#> 60 6 10 NaN