This model allows for the exploration of the impact of ID surveillance on transmission dynamics

simulate_idsurveillance_ode(
S = 1000,
P = 1,
tmax = 200,
bP = 0,
bA = 0,
bI = 0.001,
gP = 0.5,
f = 0,
d = 0,
w = 0,
m = 0,
n = 0,
rP = 0,
rA = 0,
rI = 0.5
)

## Arguments

S : initial number of susceptible hosts : numeric : initial number of infected pre-symptomatic hosts : numeric : maximum simulation time : numeric : rate of transmission from presymptomatic to susceptible host : numeric : rate of transmission from asymptomatic to susceptible host : numeric : rate of transmission from symptomatic to susceptible host : numeric : the rate at which presymptomatic hosts move to the next stage : numeric : fraction of asymptomatic hosts : numeric : rate at which infected hosts die : numeric : the rate at which host immunity wanes : numeric : the rate of births : numeric : the rate of natural deaths : numeric : rate of pre-symptomatic host removal due to surveillance : numeric : rate of asymptomatic host removal due to surveillance : numeric : rate of symptomatic host removal due to surveillance : numeric

## Value

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.

## 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.

  # To run the simulation with default parameters just call the function:
plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')