Sensitivity Analysis

# Sensitivity Analysis

Sensitivity analysis for ODE models is provided by the DiffEq suite.

## Local Sensitivity Analysis

The local sensitivity of the solution to a parameter is defined by how much the solution would change by changes in the parameter, i.e. the sensitivity of the ith independent variable to the jth parameter is $\frac{\partial y}{\partial p_{j}}$.

The local sensitivity is computed using the sensitivity ODE:

$\frac{d}{dt}\frac{\partial u}{\partial p_{j}}=\frac{\partial f}{\partial y}\frac{\partial y}{\partial p_{j}}+\frac{\partial f}{\partial p_{j}}=J\cdot S_{j}+F_{j}$

where

$J=\left(\begin{array}{cccc} \frac{\partial f_{1}}{\partial y_{1}} & \frac{\partial f_{1}}{\partial y_{2}} & \cdots & \frac{\partial f_{1}}{\partial y_{k}}\\ \frac{\partial f_{2}}{\partial y_{1}} & \frac{\partial f_{2}}{\partial y_{2}} & \cdots & \frac{\partial f_{2}}{\partial y_{k}}\\ \cdots & \cdots & \cdots & \cdots\\ \frac{\partial f_{k}}{\partial y_{1}} & \frac{\partial f_{k}}{\partial y_{2}} & \cdots & \frac{\partial f_{k}}{\partial y_{k}} \end{array}\right)$

is the Jacobian of the system,

$F_{j}=\left(\begin{array}{c} \frac{\partial f_{1}}{\partial p_{j}}\\ \frac{\partial f_{2}}{\partial p_{j}}\\ \vdots\\ \frac{\partial f_{k}}{\partial p_{j}} \end{array}\right)$

are the parameter derivatives, and

$S_{j}=\left(\begin{array}{c} \frac{\partial y_{1}}{\partial p_{j}}\\ \frac{\partial y_{2}}{\partial p_{j}}\\ \vdots\\ \frac{\partial y_{k}}{\partial p_{j}} \end{array}\right)$

is the vector of sensitivities. Since this ODE is dependent on the values of the independent variables themselves, this ODE is computed simultaneously with the actual ODE system.

### Defining a Sensitivity Problem

To define a sensitivity problem, simply use the ODELocalSensitivityProblem type instead of an ODE type. Note that this requires a ParameterizedFunction with a Jacobian. For example, we generate an ODE with the sensitivity equations attached for the Lotka-Volterra equations by:

f = @ode_def_nohes LotkaVolterraSensitivity begin
dx = a*x - b*x*y
dy = -c*y + d*x*y
end a=>1.5 b=>1 c=>3 d=1

prob = ODELocalSensitivityProblem(f,[1.0;1.0],(0.0,10.0))

This generates a problem which the ODE solvers can solve:

sol = solve(prob,DP8())

Note that the solution is the standard ODE system and the sensitivity system combined. Therefore, the solution to the ODE are the first n components of the solution. This means we can grab the matrix of solution values like:

x = sol[1:sol.prob.indvars,:]

Since each sensitivity is a vector of derivatives for each function, the sensitivities are each of size sol.prob.indvars. We can pull out the parameter sensitivities from the solution as follows:

da = sol[sol.prob.indvars+1:sol.prob.indvars*2,:]
db = sol[sol.prob.indvars*2+1:sol.prob.indvars*3,:]
dc = sol[sol.prob.indvars*3+1:sol.prob.indvars*4,:]

This means that da[1,i] is the derivative of the x(t) by the parameter a at time sol.t[i]. Note that all of the functionality available to ODE solutions is available in this case, including interpolations and plot recipes (the recipes will plot the expanded system).

plot(sol.t,da',lw=3)

Here we see that there is a periodicity to the sensitivity which matches the periodicity of the Lotka-Volterra solutions. However, as time goes on the sensitivity increases. This matches the analysis of Wilkins in Sensitivity Analysis for Oscillating Dynamical Systems.