The goal of sensitivity analysis is to examine how sensitive a mathematical model responds to variations in its input variables. Here we focus on the sensitivity analysis of ordinary differential equation (ODE) models via Morris screening.

If the assumption of a uniform distribution on the domain intervals doesn’t hold, the Morris screening method cannot be used and the variance-based Sobol’ method should be considered instead. In this case, simply switch from using the function `ODEmorris`

to the function `ODEsobol`

.

The Lotka-Volterra equations describe a predator and its prey’s population development and go back to Lotka (1925) and Volterra (1926). The prey’s population at time \(t\) (in days) will be denoted with \(P(t)\) and the predator’s (or rather consumer’s) population with \(C(t)\). \(P(t)\) and \(C(t)\) are called state variables. This ODE model is two-dimensional, but it should be noted that ODE models of arbitrary dimensions (including one-dimensional ODE models) can be analyzed with ODEsensitivity.

Now we define the model according to the definition in `deSolve::ode()`

.

```
LVmod = function(Time, State, Pars) {
with(as.list(c(State, Pars)), {
Ingestion <- rIng * Prey * Predator
GrowthPrey <- rGrow * Prey * (1 - Prey/K)
MortPredator <- rMort * Predator
dPrey <- GrowthPrey - Ingestion
dPredator <- Ingestion * assEff - MortPredator
return(list(c(dPrey, dPredator)))
})
}
```

Each of the five parameter names, their lower and upper boundaries, the initial values for the state variables and the timepoints of interest are saved in separate vectors:

The sensitivity analysis of a general ODE model can be performed by using the generic function `ODEsensitivity::ODEmorris()`

.

```
set.seed(1618)
LVres_morris = ODEmorris(mod = LVmod, pars = LVpars, state_init = LVinit
, times = LVtimes, binf = LVbinf, bsup = LVbsup
)
```

Let’s take a look at the output `LVres_morris`

.

```
str(LVres_morris, give.attr = FALSE)
#> List of 2
#> $ Prey : num [1:16, 1:51] 1.00e-02 -1.90e-02 2.42e-02 4.78e-05 -3.24e-05 ...
#> $ Predator: num [1:16, 1:51] 0.01 0.009441 0.000063 -0.01796 0.009611 ...
```

The first row of each state variable contains a copy of all timepoints. The other rows contain the Morris sensitivity indices \(\mu\), \(\mu^\star\), and \(\sigma\) for all 5 parameters and all 51 timepoints.

ODEsensitivity provides a `plot()`

method for objects of class `ODEmorris`

:

`plot.ODEmorris()`

has two important arguments: `pars_plot`

and `state_plot`

. Using `pars_plot`

, a subset of the parameters included in the sensitivity analysis can be selected for plotting (the default is to use all parameters). `state_plot`

gives the name of the state variable for which the sensitivity indices shall be plotted (the default being the first state variable):