This function runs a simulation of a compartment model using a set of ordinary differential equations. The model describes a simple SIR model with an additional environmental source of infection The user provides initial conditions and parameter values for the system. The function simulates the ODE using an ODE solver from the deSolve package.

simulate_fit_noro(
  S = 100,
  I = 1,
  R = 0,
  b = 0.001,
  blow = 1e-10,
  bhigh = 0.1,
  g = 0.5,
  glow = 0.001,
  ghigh = 100,
  n = 0,
  nlow = 0,
  nhigh = 1000,
  t1 = 8,
  t2 = 15,
  fitmodel = 1,
  iter = 100,
  solvertype = 1
)

Arguments

S

: starting value for Susceptible : numeric

I

: starting value for Infected : numeric

R

: starting value for Recovered : numeric

b

: infection rate : numeric

blow

: lower bound for infection rate : numeric

bhigh

: upper bound for infection rate : numeric

g

: recovery rate : numeric

glow

: lower bound for g : numeric

ghigh

: upper bound for g : numeric

n

: rate of infection from common source : numeric

nlow

: lower bound for n : numeric

nhigh

: upper bound for n : numeric

t1

: start time of infection from common source : numeric

t2

: end time of infection from common source: numeric

fitmodel

: fitting model variant 1, 2 or 3 : 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 the best fit timeseries, the best fit parameters, the data and the AICc for the model.

Details

Three versions of a simple SIR type compartmental ODE model are fit to cases of norovirus during an outbreak. #' @section 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.

Examples

# To run the code with default parameters just call the function: if (FALSE) result <- simulate_fit_noro() # To apply different settings, provide them to the simulator function, like such: result <- simulate_fit_noro(iter = 5, fitmodel = 2)