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

P

: initial number of infected pre-symptomatic hosts : numeric

tmax

: maximum simulation time : numeric

bP

: rate of transmission from presymptomatic to susceptible host : numeric

bA

: rate of transmission from asymptomatic to susceptible host : numeric

bI

: rate of transmission from symptomatic to susceptible host : numeric

gP

: the rate at which presymptomatic hosts move to the next stage : numeric

f

: fraction of asymptomatic hosts : numeric

d

: rate at which infected hosts die : numeric

w

: the rate at which host immunity wanes : numeric

m

: the rate of births : numeric

n

: the rate of natural deaths : numeric

rP

: rate of pre-symptomatic host removal due to surveillance : numeric

rA

: rate of asymptomatic host removal due to surveillance : numeric

rI

: 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.

See also

The UI of the app 'Parasite Model', which is part of the DSAIDE package, contains more details.

Examples

# To run the simulation with default parameters just call the function: result <- simulate_idsurveillance_ode() # To choose parameter values other than the standard one, # specify the parameters you want to change, e.g. like such: result <- simulate_idsurveillance_ode(S = 2000, tmax = 100, f = 0.5) # You should then use the simulation result returned from the function, like this: plot(result$ts[ , "time"],result$ts[ , "S"],xlab='Time',ylab='Number Susceptible',type='l')