SEIRSd model

A SEIRS model with 4 compartments

simulate_SEIRSd_model_stochastic(
  S = 1000,
  E = 1,
  I = 1,
  R = 0,
  bE = 0,
  bI = 0.001,
  gE = 1,
  gI = 1,
  w = 1,
  n = 0,
  m = 0,
  tfinal = 100,
  rngseed = 123
)

Arguments

S

: starting value for Susceptible : numeric

E

: starting value for Exposed : numeric

I

: starting value for Infected and Symptomatic : numeric

R

: starting value for Recovered : numeric

bE

: infection by exposed : numeric

bI

: infection by symptomatic : numeric

gE

: progression rate : numeric

gI

: recovery rate : numeric

w

: waning immunity : numeric

n

: births : numeric

m

: deaths : numeric

tfinal

: Final time of simulation : numeric

rngseed

: set random number seed for reproducibility : numeric

Value

The function returns the output as a list. The time-series from the simulation is returned as a dataframe saved as list element ts. The ts dataframe has one column per compartment/variable. The first column is time.

Details

The model includes susceptible, exposed/asymptomatic, infected/symptomatic, and recovered compartments. The processes that are modeled are infection, progression to infectiousness, recovery and waning immunity. Demographics through natural births and deaths are also included.

This code was generated by the modelbuilder R package. The model is implemented as a set of stochastic equations using the adaptivetau package. The following R packages need to be loaded for the function to work: adpativetau

Warning

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

Model Author

Andreas Handel

Model creation date

2020-09-28

Code Author

generated by the modelbuilder R package

Code creation date

2021-07-19

Examples

 
# To run the simulation with default parameters:  
result <- simulate_SEIRSd_model_stochastic() 
# To choose values other than the standard one, specify them like this:  
result <- simulate_SEIRSd_model_stochastic(S = 2000,E = 2,I = 2,R = 0) 
# You can display or further process the result, like this:  
plot(result$ts[,'time'],result$ts[,'S'],xlab='Time',ylab='Numbers',type='l') 

print(paste('Max number of S: ',max(result$ts[,'S']))) 
#> [1] "Max number of S:  2004"