# Non-Markovian SIR Model

using Random
using Plots
using Distributions
using CompetingClocks

In this tutorial we demonstrate construction of a SIR (susceptible-infectious-removed) model with non-exponential recovery times. Infection events occur at the points of a Poisson process, which is equivalent to using a single exponential clock whose rate corresponds to the overall rate of infection in the population. The infection rate is $\beta c S I / N$ where $N$ is the total population size, making this a frequency-dependent force of infection term rather than pure mass action (although as $N$ does not change in this example, the difference is moot). Clocks for recovery events follow an arbitrary distribution.

## Model structure

We use a struct that stores the type of the recovery distribution as a type parameter. The model state is stored as a vector of integers, representing S, I, and R. Additionally, because recovery clocks need unique keys, we define the method get_key! which retrives an integer key and increments the stored key counter.

The initialize! method uses the initial state to enable the infection clock and recovery clocks for each initial infectious person. Note that the keys are a tuple where the first element is a Symbol giving the event type, and the second element is an integer.

mutable struct SIRNonMarkov{T<:Distribution}
state::Vector{Int}
parameters::Vector{Float64}
next_key::Int
recovery_distribution::T
time::Float64
end

function get_key!(model::SIRNonMarkov)
key = model.next_key
model.next_key += 1
return key
end

function initialize!(model::SIRNonMarkov, sampler, rng)
(β, c, γ) = model.parameters
# enable the infection clock
enable!(
sampler,
(:infection, get_key!(model)),
Exponential(1.0/(β*c*model.state[2]/sum(model.state)*model.state[1])),
model.time,
model.time,
rng
)
# enable the recovery clocks
for _ in 1:model.state[2]
enable!(sampler, (:recovery, get_key!(model)), model.recovery_distribution, model.time, model.time, rng)
end
end;

## Model update

In the model update function, we use the first element of the clock key to determine the event type corresponding to the clock that fired, and apply the corresponding logic. Infection events disable and enable the infection event with a new rate, and enable a recovery clock for the newly infectious individual. Recovery events simply disable the clock associated to that event. Both events update the state vector.

function step!(model::SIRNonMarkov, sampler::SSA{K,T}, when::T, which::K, rng) where {K,T}
(β, c, γ) = model.parameters
model.time = when
if first(which) == :infection
model.state[1] -= 1
model.state[2] += 1
# disable and reenable the infection clock after accounting for the new rate
disable!(sampler, which, model.time)
enable!(
sampler,
which,
Exponential(1.0/(β*c*model.state[2]/sum(model.state)*model.state[1])),
model.time,
model.time,
rng
)
# enable a recovery event for the newly infected person
enable!(
sampler,
(:recovery, get_key!(model)),
model.recovery_distribution,
model.time,
model.time,
rng
)
elseif first(which) == :recovery
model.state[2] -= 1
model.state[3] += 1
disable!(sampler, which, model.time)
else
error("unrecognized clock key: $(which)") end end; ## Simulation We first set parameters. tmax = 40.0 initial_state = [990, 10, 0] p = [0.05, 10.0, 4.0] seed = 456959517 rng = MersenneTwister(seed); Next we generate the model struct and the sampler object. Here we choose the CombinedNextReaction sampler type. We choose to use a Dirac delta distribution to simulate deterministic recovery times. sirmodel = SIRNonMarkov(deepcopy(initial_state), p, 0, Dirac(p[3]), 0.0) sampler = CombinedNextReaction{Tuple{Symbol,Int},Float64}(); Now we may run the model, using a function run_sir!. We preallocate a matrix to store model output. Note that in the simple SIR model with only infection and recovery events, a maximum of$2S + I\$ events is possible.

function run_sir!(model, sampler, tmax, rng)

output = zeros(2*model.state[1]+model.state[2]+1, 4)
nout = 1
output[nout,:] = [sirmodel.time; sirmodel.state]
nout += 1;

initialize!(model, sampler, rng)

(when, which) = next(sampler, model.time, rng)

while when <= tmax
step!(model, sampler, when, which, rng)
(when, which) = next(sampler, model.time, rng)

output[nout,:] .= [model.time; model.state]
nout += 1
end

return output, nout
end

(output, nout) = run_sir!(sirmodel, sampler, tmax, rng);

Finally we can plot the sampled trajectory.

plot(
output[1:nout-1,1],
output[1:nout-1,2:end],
label=["S" "I" "R"],
xlabel="Time",
ylabel="Number"
)