Simulation of a compartmental infectious disease transmission model illustrating the impact of ID surveillance

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.

Author

Andreas Handel, Ronald Galiwango

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')