Fitting a simple bacteria infection models to Streptococcus pneumoniae infection data

This function runs a simulation of a compartment model using a set of ordinary differential equations. The model describes a simple bacteria infection system.

simulate_bacteria_fit(
  B = 1,
  I = 1,
  g = 0.1,
  glow = 1e-04,
  ghigh = 100,
  Bmax = 1e+08,
  Bmaxlow = 10000,
  Bmaxhigh = 1e+10,
  dB = 0.01,
  dBlow = 1e-04,
  dBhigh = 100,
  kI = 0.001,
  kIlow = 1e-04,
  kIhigh = 100,
  rI = 0.1,
  rIlow = 1e-04,
  rIhigh = 100,
  Imax = 10000,
  Imaxlow = 1,
  Imaxhigh = 1e+10,
  dI = 0.1,
  dIlow = 0.001,
  dIhigh = 100,
  iter = 10,
  solvertype = 1
)

Arguments

B

: initial number of bacteria : numeric

I

: initial number of neutrophils (immune response) : numeric

g

: maximum rate of bacteria growth : numeric

glow

: lower bound for g : numeric

ghigh

: upper bound for g : numeric

Bmax

: bacteria carrying capacity : numeric

Bmaxlow

: lower bound for Bmax : numeric

Bmaxhigh

: upper bound for Bmax : numeric

dB

: bacteria death rate : numeric

dBlow

: lower bound for dB : numeric

dBhigh

: upper bound for dB : numeric

kI

: rate of bacteria killing by immune response : numeric

kIlow

: lower bound for k : numeric

kIhigh

: upper bound for k : numeric

rI

: immune response growth rate : numeric

rIlow

: lower bound for r : numeric

rIhigh

: upper bound for r : numeric

Imax

: immune response carrying capacity : numeric

Imaxlow

: lower bound for Imax : numeric

Imaxhigh

: upper bound for Imax : numeric

dI

: immune response decay rate : numeric

dIlow

: lower bound for dI : numeric

dIhigh

: upper bound for dI : numeric

iter

: max number of steps to be taken by optimizer : numeric

solvertype

: the type of solver/optimizer to use (1-3) : numeric

Value

The function returns a list containing as elements the best fit time series data frame, the best fit parameters, the data and the final SSR

Details

A simple compartmental ODE model for a bacterial infection is fitted to data. The fitting is done using solvers/optimizers from the nloptr package (which is a wrapper for the nlopt library). The package provides access to a large number of solvers. Here, we only implement 3 solvers, namely 1 = NLOPT_LN_COBYLA, 2 = NLOPT_LN_NELDERMEAD, 3 = NLOPT_LN_SBPLX For details on what those optimizers are and how they work, see the nlopt/nloptr documentation.

Warning

This function does not perform any error checking. So if you try to do something nonsensical (e.g. specify negative parameter or starting values, the code will likely abort with an error message.

See also

See the Shiny app documentation corresponding to this function for more details on this model.

Author

Andreas Handel

Examples

# To run the code with default parameters just call the function:
if (FALSE) result <- simulate_bacteria_fit()
# To apply different settings, provide them to the simulator function, like such:
result <- simulate_bacteria_fit(iter = 5)