u0_SISe3 {SimInf} | R Documentation |
Example data to initialize a population of 1600 nodes and
demonstrate the SISe3
model.
data(u0_SISe3)
A data.frame
A data.frame
with the number of individuals in the
‘S_1’, ‘S_2’, ‘S_3’, ‘I_1’,
‘I_2’ and ‘I_3’ compartments in 1600 nodes. Note
that the ‘I_1’, ‘I_2’ and ‘I_3’ compartments
are zero.
## Create an 'SISe3' model with 1600 nodes and initialize it to ## run over 4*365 days and record data at weekly time-points. ## Load the initial population and add ten infected individuals to ## I_1 in the first node. u0 <- u0_SISe3 u0$I_1[1] <- 10 ## Define 'tspan' to run the simulation over 4*365 and record the ## state of the system at weekly time-points. tspan <- seq(from = 1, to = 4*365, by = 7) ## Load scheduled events for the population of nodes with births, ## deaths and between-node movements of individuals. events <- events_SISe3 ## Create a 'SISe3' model model <- SISe3(u0 = u0, tspan = tspan, events = events, 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) ## Run the model to generate a single stochastic trajectory. result <- run(model, threads = 1) ## Summarize trajectory summary(result) ## Plot the proportion of nodes with at least one infected ## individual. plot(prevalence(result, I_1 + I_2 + I_3 ~ ., "nop"), type = "l")