Simulation of a stochastic SEIR type model with the following compartments: Susceptibles (S), Infected and pre-symptomatic/exposed (E), Infected and Symptomatic (I), Recovered and Immune (R)

simulate_seir_stochastic(
  S = 1000,
  I = 10,
  bE = 0,
  bI = 0.001,
  gE = 0.5,
  gI = 0.5,
  w = 0,
  m = 0,
  n = 0,
  tmax = 100,
  rngseed = 100
)

Arguments

S

: initial number of susceptible hosts : numeric

I

: initial number of infected, symptomatic hosts : numeric

bE

: level/rate of infectiousness for hosts in the E compartment : numeric

bI

: level/rate of infectiousness for hosts in the I compartment : numeric

gE

: rate at which a person leaves the E compartment : numeric

gI

: rate at which a person leaves the I compartment : numeric

w

: rate at which recovered lose immunity and return to susceptible : numeric

m

: the rate at which new individuals enter the model (are born) : numeric

n

: the rate of natural death (the inverse is the average lifespan) : numeric

tmax

: maximum simulation time : numeric

rngseed

: seed for random number generator to allow reproducibility : numeric

Value

The function returns a list. The list has one element, a data frame ts which contains the time series of the simulated model, with one column per compartment/variable. The first column is time.

Details

A compartmental ID model with several states/compartments is simulated. Initial conditions for the E and R variables are 0. Units of time depend on the time units chosen for model parameters. The simulation runs as a stochastic model using the adaptive-tau algorithm as implemented by ssa.adaptivetau() in the adpativetau package. See the manual of this package for more details.

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. See the manual for the adaptivetau package for details on the stochastic algorithm.

Examples

# To run the simulation with default parameters, just call the function: result <- simulate_seir_stochastic() # To choose parameter values other than the standard one, specify them like this: result <- simulate_seir_stochastic(S = 2000, tmax = 200, bE = 0.01) # You can display or further process the result, like this: plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Number Susceptible',type='l')
print(paste('Max number of infected: ',max(result$ts[,'I'])))
#> [1] "Max number of infected: 739"