Drug Resistance Evolution

An SIR-type model that includes drug treatment and resistance.

simulate_Drug_Resistance_Evolution_stochastic(
  S = 1000,
  Iu = 1,
  It = 1,
  Ir = 1,
  R = 0,
  bu = 0.002,
  bt = 0.002,
  br = 0.002,
  gu = 1,
  gt = 1,
  gr = 1,
  f = 0,
  cu = 0,
  ct = 0,
  tfinal = 100,
  rngseed = 123
)

Arguments

S

: starting value for Susceptible : numeric

Iu

: starting value for Infected Untreated : numeric

It

: starting value for Infected Treated : numeric

Ir

: starting value for Infected Resistant : numeric

R

: starting value for Recovered : numeric

bu

: untreated infection rate : numeric

bt

: treated infection rate : numeric

br

: resistant infection rate : numeric

gu

: untreated recovery rate : numeric

gt

: treated recovery rate : numeric

gr

: resistant recovery rate : numeric

f

: fraction treated : numeric

cu

: resistance emergence untreated : numeric

ct

: resistance emergence treated : 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, infected untreated, treated and resistant, and recovered compartments. The processes which are modeled are infection, treatment, resistance generation and recovery.

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

2020-10-05

Code Author

generated by the modelbuilder R package

Code creation date

2021-07-19

Examples

 
# To run the simulation with default parameters:  
result <- simulate_Drug_Resistance_Evolution_stochastic() 
# To choose values other than the standard one, specify them like this:  
result <- simulate_Drug_Resistance_Evolution_stochastic(S = 2000,Iu = 2,It = 2,Ir = 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"