Simulation to illustrate uncertainty and sensitivity analysis

This function performs uncertainty and sensitivity analysis using the SIRS model.

simulate_SIR_usanalysis(
  Smin = 1000,
  Smax = 1000,
  Imin = 10,
  Imax = 10,
  bmin = 0.005,
  bmax = 0.01,
  gmean = 0.5,
  gvar = 0.01,
  nmin = 0,
  nmax = 0,
  mmin = 0,
  mmax = 0,
  wmin = 0,
  wmax = 0,
  samples = 5,
  rngseed = 100,
  tstart = 0,
  tfinal = 500,
  dt = 0.1
)

Arguments

Smin

: lower bound for initial susceptible : numeric

Smax

: upper bound for initial susceptible : numeric

Imin

: lower bound for initial infected : numeric

Imax

: upper bound for initial infected : numeric

bmin

: lower bound for infection rate : numeric

bmax

: upper bound for infection rate : numeric

gmean

: mean for recovery rate : numeric

gvar

: variance for recovery rate : numeric

nmin

: lower bound for birth rate : numeric

nmax

: upper bound for birth rate : numeric

mmin

: lower bound for death rate : numeric

mmax

: upper bound for death rate : numeric

wmin

: lower bound for waning immunity rate : numeric

wmax

: upper bound for waning immunity rate : numeric

samples

: number of LHS samples to run : numeric

rngseed

: seed for random number generator : numeric

tstart

: Start time of simulation : numeric

tfinal

: Final time of simulation : numeric

dt

: times for which result is returned : numeric

Value

The function returns the output as a list. The list element 'dat' contains a data frame. The simulation returns for each parameter sample the peak and final value for I and final for S. Also returned are all parameter values as individual columns and an indicator stating if steady state was reached. A final variable 'steady' is returned for each simulation. It is TRUE if the simulation did reach steady state, otherwise FALSE.

Details

The SIRS model with demographics is simulated for different parameter values. The user provides ranges for the initial conditions and parameter values and the number of samples. The function does Latin Hypercube Sampling (LHS) of the parameters and runs the model for each sample. Distribution for all parameters is assumed to be uniform between the min and max values. The only exception is the recovery parameter, which (for illustrative purposes) is assumed to be gamma distributed with the specified mean and variance. This code is part of the DSAIDE R package. For additional model details, see the corresponding app in the DSAIDE package.

Warning

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

See also

See the Shiny app documentation corresponding to this simulator function for more details on this model.

Author

Andreas Handel

Examples

# To run the simulation with default parameters just call the function:
if (FALSE) result <- simulate_SIR_usanalysis()
# To choose parameter values other than the standard one, specify them, like such:
result <- simulate_SIR_usanalysis(gmean = 2, gvar = 0.2, samples = 5, tfinal = 50)
# You should then use the simulation result returned from the function, like this:
plot(result$dat[,"g"],result$dat[,"Ipeak"],xlab='values for g',ylab='Peak Bacteria',type='l')