Characteristics of ID

A compartmental model with several different compartments: Susceptibles (S), Infected and Pre-symptomatic (P), Infected and Asymptomatic (A), Infected and Symptomatic (I), Recovered and Immune (R) and Dead (D)

simulate_Characteristics_of_ID_ode(
  S = 1000,
  P = 1,
  A = 0,
  I = 0,
  R = 0,
  D = 0,
  bP = 0,
  bA = 0,
  bI = 0.001,
  gP = 0.1,
  gA = 0.1,
  gI = 0.1,
  f = 0,
  d = 0,
  tstart = 0,
  tfinal = 200,
  dt = 0.1
)

Arguments

S

: starting value for Susceptible : numeric

P

: starting value for Presymptomatic : numeric

A

: starting value for Asymptomatic : numeric

I

: starting value for Symptomatic : numeric

R

: starting value for Recovered : numeric

D

: starting value for Dead : numeric

bP

: rate of transmission from P to S : numeric

bA

: rate of transmission from A to S : numeric

bI

: rate of transmission from I to S : numeric

gP

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

gA

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

gI

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

f

: fraction of asymptomatic infections : numeric

d

: fraction of symptomatic hosts that die : 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 tracks the dynamics of susceptible, presymptomatic, asymptomatic, symptomatic, recovered, and dead individuals. Susceptible (S) individuals can become infected by presymptomatic (P), asymptomatic (A), or infected (I) hosts. All infected individuals enter the presymptomatic stage first, from which they can become symptomatic or asymptomatic. Asymptomatic hosts recover within some specified duration of time, while infected hosts either recover or die, thus entering either R or D. Recovered individuals are immune to reinfection. This model is part of the DSAIDE R package, more information can be found there.

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

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, Alexis Vittengl

Model creation date

2020-09-29

Code Author

generated by the modelbuilder R package

Code creation date

2021-07-19

Examples

 
# To run the simulation with default parameters:  
result <- simulate_Characteristics_of_ID_ode() 
# To choose values other than the standard one, specify them like this:  
result <- simulate_Characteristics_of_ID_ode(S = 2000,P = 2,A = 0,I = 0,R = 0,D = 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"