Nothing

```
#' Simulation of a compartmental infectious disease transmission model illustrating
#' parasite infection dynamics with intermediate hosts
#'
#' @description This model allows for the simulation of a parasitic infection that requires
#' an intermediate host for transmission
#'
#' @param Sh : initial number of susceptible definitive hosts : numeric
#' @param Ih : initial number of infected definitive hosts : numeric
#' @param E : initial number of pathogens in the environment : numeric
#' @param Si : initial number of susceptible intermediate hosts : numeric
#' @param Ii : initial number of infected intermediate hosts : numeric
#' @param tmax : maximum simulation time : numeric
#' @param bi : rate of transmission from infected intermediate host to susceptible host : numeric
#' @param be : rate of transmission from environment to susceptible intermediate host : numeric
#' @param m : the rate of births of intermediate hosts : numeric
#' @param n : the rate of natural intermediate hosts : numeric
#' @param g : the rate at which infected hosts recover/die : numeric
#' @param w : the rate at which host immunity wanes in host : numeric
#' @param p : rate at which infected host shed the pathogen in the environment : numeric
#' @param c : rate at which the pathogen decays in the environment : numeric
#' @return This function returns the simulation result as obtained from a call
#' to the deSolve ode solver.
#' @details A compartmental ID model with several states/compartments
#' is simulated as a set of ordinary differential
#' equations. The function returns the output from the odesolver as a matrix,
#' with one column per compartment/variable. The first column is time.
#' @section Warning:
#' This function does not perform any error checking. So if you try to do
#' something nonsensical (e.g. negative values or fractions > 1),
#' the code will likely abort with an error message.
#' @examples
#' # To run the simulation with default parameters just call the function:
#' result <- simulate_parasites_ode()
#' # To choose parameter values other than the standard one,
#' # specify the parameters you want to change, e.g. like such:
#' result <- simulate_parasites_ode(Sh = 2000, Ih = 10, tmax = 100, g = 0.5)
#' # You should then use the simulation result returned from the function, like this:
#' plot(result$ts[ , "time"],result$ts[ , "Sh"],xlab='Time',ylab='Number Susceptible',type='l')
#' @seealso The UI of the app 'Parasite Model', which is part of the DSAIDE package, contains more details.
#' @author Andreas Handel, Christine Casey
#' @export
simulate_parasites_ode <- function(Sh = 1e3, Ih = 1, E = 1, Si = 0, Ii = 0, tmax = 120, bi = 0.01, be = 0.01, m = 0, n = 0, g = 0, w = 0, p = 0.01, c = 0.001)
{
# This function is used in the solver function and has no independent usages
parasiteodes <- function(t, y, parms)
{
with(
as.list(c(y,parms)), #lets us access variables and parameters stored in y and pars by name
{
#the ordinary differential equations
dSh = - Sh * bi * Ii + (w * Rh) #susceptible definitive hosts
dIh = Sh * bi * Ii - (g * Ih) #infected and symptomatic definitive hosts
dRh = g * Ih - (w * Rh) #recovered and immune definitive hosts
dE = p * Ih - (c *E) # pathogens in the environment
dSi = m - n * Si - (be * E * Si) #susceptible intermediate hosts
dIi = Si*be * E - (n * Ii) #infected intermediate hosts
list(c(dSh, dIh, dRh, dE, dSi, dIi))
}
) #close with statement
} #end function specifying the ODEs
############################################################
Y0 = c(Sh = Sh, Ih = Ih, Rh = 0, E=E, Si = Si, Ii = Ii); #combine initial conditions into a vector
dt = min(0.1, tmax / 1000); #time step for which to get results back
timevec = seq(0, tmax, dt); #vector of times for which solution is returned (not that internal timestep of the integrator is different)
############################################################
#vector of parameters which is sent to the ODE function
pars=c(bi = bi, be = be, m = m, n = n, g = g, w = w, p = p, c = c);
#this line runs the simulation, i.e. integrates the differential equations describing the infection process
#the result is saved in the odeoutput matrix, with the 1st column the time, the 2nd, 3rd, 4th column the variables S, I, R
#This odeoutput matrix will be re-created every time you run the code, so any previous results will be overwritten
odeoutput = deSolve::lsoda(Y0, timevec, func = parasiteodes, parms=pars, atol=1e-12, rtol=1e-12);
colnames(odeoutput) <- c('time',"Sh","Ih","Rh","E","Si","Ii")
result <- list()
result$ts <- as.data.frame(odeoutput)
return(result)
}
```

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