Error when using CorrelatedWienerProcess with VectorContinuousCallback

Question

I am trying to simulate stochastic population dynamics using CorrelatedWienerProcess to add the noise. To prevent populations going negative I am using callbacks. Ideally I would like to use VectorContinuousCallback so I can automatically generate the callbacks for any number of species.

However, I keep getting ERROR: LoadError: MethodError: no method matching (::DiffEqNoiseProcess.var"#101#103")... when trying to use both CorrelatedWienerProcess & VectorContinuousCallback. The solver runs fine when I have either no callbacks, use VectorContinuousCallback with the default noise process, or use a CallbackSet of DiscreteCallback alongside the CorrelatedWienerProcess - it is just the VectorContinuousCallback-CorrelatedWienerProcess combination that is giving me the error, so I am unsure what I am doing wrong.

Here is the code I have used to produce the error and the other working callback-noise combinations:

# Set up the equations

using DifferentialEquations, Plots

function test_equations(du, u, p, t)
    du[1] = 0.0
    du[2] = 0.0
    du[3] = 0.0
end

function test_noise(du, u, p, t)
    du[1] = 1.0
    du[2] = 1.0
    du[3] = 1.0
end

# Vector of continuous callbacks
function condition(out, u, t, integrator)
    for i in eachindex(u)
        out[i] = u[i]
    end
end
function affect!(integrator, idx)
    for i in eachindex(u)
        if idx == i
            integrator.u[i] = 0.001
        end
    end
end
cont_cb = VectorContinuousCallback(condition, affect!, 3)

# Discrete callback set
condition1(u,t,integrator) = u[1] <= 0.0
affect1!(integrator) = integrator.u[1] = 0.0
cb1 = DiscreteCallback(condition1,affect1!)

condition2(u,t,integrator) = u[2] <= 0.0
affect2!(integrator) = integrator.u[2] = 0.0
cb2 = DiscreteCallback(condition2,affect2!)        

condition3(u,t,integrator) = u[3] <= 0.0
affect3!(integrator) = integrator.u[3] = 0.0
cb3 = DiscreteCallback(condition3,affect3!)

disc_cb = CallbackSet(cb1, cb2, cb3)

# Set up problems
u = [1.0, 1.0, 1.0]
tspan = (0.0, 150.0)
W = CorrelatedWienerProcess([0.25 -0.25 0.0; -0.25 0.25 0.0; 0.0 0.0 0.0], 0.0, zeros(3))

default_noise_prob = SDEProblem(test_equations, test_noise, u, tspan)
corr_noise_prob = SDEProblem(test_equations, test_noise, u, tspan, noise = W)

# Noise-callback combinations
default_noise_no_callback = solve(default_noise_prob)
corr_noise_no_callback = solve(corr_noise_prob)

default_noise_disc_callback = solve(default_noise_prob, callback = disc_cb)
corr_noise_disc_callback = solve(corr_noise_prob, callback = disc_cb)

default_noise_cont_callback = solve(default_noise_prob, callback = cont_cb)
# This is the only one that errors
corr_noise_cont_callback = solve(corr_noise_prob, callback = cont_cb)