Hello,
I am developing an agent-based model to simulate the spread of infectious
diseases in heterogeneous landscapes composed of habitat polygons (or clumps of
connected cells). To simplify the model, I consider a habitat grid (or raster)
containing the polygon ID of each cell. In addition, I have epidemiological
parameters associated with each polygon ID. At each time step, the parameter
values change in the polygon. Thus, the data frame �landscape� (see below) is
updated at each time step. Here is an example at t = 0:
landscape <- data.frame(polygon_ID = seq(1, 10, by = 1),
beta = sample(c(100, 200, 400, 600), 10, replace =
TRUE),
gamma = sample(c(25, 26, 27, 28), 10, replace = TRUE))
To study the disease dynamics, I also am developing a compartmental model based
on a system of ordinary differential equations (ODEs). Here is an example to
represent the system of ODEs:
solve_sir_model <- function (times, parameters) {
sir_model <- function (times, states, parameters) {
with(as.list(c(states, parameters)), {
dSdt <- -beta*S*I
dIdt <- beta*S*I-gamma*I
dRdt <- gamma*I
dNdt <- dSdt + dIdt + dRdt
return(list(c(dSdt, dIdt, dRdt, dNdt)))
})
}
states <- c(S = 99, I = 1, R = 0, N = 100)
return(ode(y = states, times = times, func = sir_model, parms = parameters))
}
require(deSolve)
output <- as.data.frame(solve_sir_model(times = seq(0, 5, by = 1), parameters =
c(beta = 400, gamma = 28)))
Here is my question: at each time step, is it possible to apply the system of
ODEs to each habitat polygon (thus each row) in the data frame �landscape�? I
am using lsoda as an ODE solver. Do I need to use another solver to apply the
ODEs at each time step?
Thank you very much for your advice.
Have a nice day
Marine
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