SIR model

A basic SIR model with 3 compartments and infection and recovery processes

simulate_SIR_model_discrete(
  S = 1000,
  I = 1,
  R = 0,
  b = 0.002,
  g = 1,
  tstart = 0,
  tfinal = 100,
  dt = 0.1
)

Arguments

S

: starting value for Susceptible : numeric

I

: starting value for Infected : numeric

R

: starting value for Recovered : numeric

b

: infection rate : numeric

g

: recovery rate : numeric

tstart

: Start time of simulation : numeric

tfinal

: Final time of simulation : numeric

dt

: Time step : 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, infected, and recovered compartments. The two processes that are modeled are infection and recovery.

This code was generated by the modelbuilder R package. The model is implemented as a set of discrete time equations using a for loop. The following R packages need to be loaded for the function to work: none

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

2020-09-01

Code Author

generated by the modelbuilder R package

Code creation date

2021-07-19

Examples

 
# To run the simulation with default parameters:  
result <- simulate_SIR_model_discrete() 
# To choose values other than the standard one, specify them like this:  
result <- simulate_SIR_model_discrete(S = 2000,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:  2000"