This model allows for the simulation of different direct transmission modes

simulate_directtransmission_ode(
  S = 1000,
  I = 1,
  bd = 0.01,
  bf = 0,
  A = 1,
  m = 0,
  n = 0,
  g = 0.1,
  w = 0,
  scenario = 1,
  tmax = 120
)

Arguments

S

: initial number of susceptibles : numeric

I

: initial number of infected hosts : numeric

bd

: rate of transmission for density-dependent transmission : numeric

bf

: rate of transmission for frequency-dependent transmission : numeric

A

: the size of the area in which the hosts are assumed to reside/interact : numeric

m

: the rate of births : numeric

n

: the rate of natural deaths : numeric

g

: the rate at which infected hosts recover : numeric

w

: the rate of waning immunity : numeric

scenario

: choice between density dependent (=1) and frequency dependent (=2) transmission : numeric

tmax

: maximum simulation time, units of months : 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 list, with the element ts, which is a dataframe whose columns represent time, the number of susceptibles, the number of infected, and the number of recovered.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. any negative values or fractions > 1), the code will likely abort with an error message

References

See e.g. Keeling and Rohani 2008 for SIR models and the documentation for the deSolve package for details on ODE solvers

See also

The UI of the Shiny app 'DirectTransmission', which is part of this package, contains more details on the model.

Examples

# To run the simulation with default parameters just call this function: result <- simulate_directtransmission_ode() # To choose parameter values other than the standard one, specify them like such: result <- simulate_directtransmission_ode(S = 100, tmax = 100, A=10) # 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')