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* Add wires and wiring diagrams * Add examples on wires * Add Graphviz runner * Fixing Agents.jl integration
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| Original file line number | Diff line number | Diff line change |
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| ```@meta | ||
| EditURL = "../../../../tutorials/agents/agents.jl" | ||
| ``` | ||
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| # Agents.jl Integration | ||
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| We instantiate an agent-based SIR model based on [Agents.jl: SIR model for the spread of COVID-19](https://juliadynamics.github.io/Agents.jl/stable/examples/sir/) make use of a SIR model constructor from an Agents.jl' [SIR model for the spread of COVID-19](https://juliadynamics.github.io/Agents.jl/stable/examples/sir/), and then we simulate the model using AlgebraicAgents.jl | ||
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| ## SIR Model in Agents.jl | ||
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| To begin with, we define the Agents.jl model: | ||
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| ````@example agents | ||
| # SIR model for the spread of COVID-19 | ||
| # taken from https://juliadynamics.github.io/Agents.jl/stable/examples/sir/ | ||
| using AlgebraicAgents | ||
| using Agents, Random | ||
| using Agents.DataFrames, Agents.Graphs | ||
| using Distributions: Poisson, DiscreteNonParametric | ||
| using DrWatson: @dict | ||
| using Plots | ||
| @agent PoorSoul GraphAgent begin | ||
| days_infected::Int ## number of days since is infected | ||
| status::Symbol ## 1: S, 2: I, 3:R | ||
| end | ||
| ```` | ||
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| Let's provide the constructors: | ||
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| ````@example agents | ||
| function model_initiation(; | ||
| Ns, | ||
| migration_rates, | ||
| β_und, | ||
| β_det, | ||
| infection_period = 30, | ||
| reinfection_probability = 0.05, | ||
| detection_time = 14, | ||
| death_rate = 0.02, | ||
| Is = [zeros(Int, length(Ns) - 1)..., 1], | ||
| seed = 0) | ||
| rng = MersenneTwister(seed) | ||
| @assert length(Ns)== | ||
| length(Is)== | ||
| length(β_und)== | ||
| length(β_det)== | ||
| size(migration_rates, 1) "length of Ns, Is, and B, and number of rows/columns in migration_rates should be the same " | ||
| @assert size(migration_rates, 1)==size(migration_rates, 2) "migration_rates rates should be a square matrix" | ||
| C = length(Ns) | ||
| # normalize migration_rates | ||
| migration_rates_sum = sum(migration_rates, dims = 2) | ||
| for c in 1:C | ||
| migration_rates[c, :] ./= migration_rates_sum[c] | ||
| end | ||
| properties = @dict(Ns, | ||
| Is, | ||
| β_und, | ||
| β_det, | ||
| β_det, | ||
| migration_rates, | ||
| infection_period, | ||
| infection_period, | ||
| reinfection_probability, | ||
| detection_time, | ||
| C, | ||
| death_rate) | ||
| space = GraphSpace(complete_digraph(C)) | ||
| model = ABM(PoorSoul, space; properties, rng) | ||
| # Add initial individuals | ||
| for city in 1:C, n in 1:Ns[city] | ||
| ind = add_agent!(city, model, 0, :S) ## Susceptible | ||
| end | ||
| # add infected individuals | ||
| for city in 1:C | ||
| inds = ids_in_position(city, model) | ||
| for n in 1:Is[city] | ||
| agent = model[inds[n]] | ||
| agent.status = :I ## Infected | ||
| agent.days_infected = 1 | ||
| end | ||
| end | ||
| return model | ||
| end | ||
| using LinearAlgebra: diagind | ||
| function create_params(; | ||
| C, | ||
| max_travel_rate, | ||
| infection_period = 30, | ||
| reinfection_probability = 0.05, | ||
| detection_time = 14, | ||
| death_rate = 0.02, | ||
| Is = [zeros(Int, C - 1)..., 1], | ||
| seed = 19) | ||
| Random.seed!(seed) | ||
| Ns = rand(50:5000, C) | ||
| β_und = rand(0.3:0.02:0.6, C) | ||
| β_det = β_und ./ 10 | ||
| Random.seed!(seed) | ||
| migration_rates = zeros(C, C) | ||
| for c in 1:C | ||
| for c2 in 1:C | ||
| migration_rates[c, c2] = (Ns[c] + Ns[c2]) / Ns[c] | ||
| end | ||
| end | ||
| maxM = maximum(migration_rates) | ||
| migration_rates = (migration_rates .* max_travel_rate) ./ maxM | ||
| migration_rates[diagind(migration_rates)] .= 1.0 | ||
| params = @dict(Ns, | ||
| β_und, | ||
| β_det, | ||
| migration_rates, | ||
| infection_period, | ||
| reinfection_probability, | ||
| detection_time, | ||
| death_rate, | ||
| Is) | ||
| return params | ||
| end | ||
| ```` | ||
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| It remains to provide the SIR stepping functions: | ||
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| ````@example agents | ||
| function agent_step!(agent, model) | ||
| @get_model model | ||
| extract_agent(model, agent) | ||
| migrate!(agent, model) | ||
| transmit!(agent, model) | ||
| update!(agent, model) | ||
| recover_or_die!(agent, model) | ||
| end | ||
| function migrate!(agent, model) | ||
| pid = agent.pos | ||
| d = DiscreteNonParametric(1:(model.C), model.migration_rates[pid, :]) | ||
| m = rand(model.rng, d) | ||
| if m ≠ pid | ||
| move_agent!(agent, m, model) | ||
| end | ||
| end | ||
| function transmit!(agent, model) | ||
| agent.status == :S && return | ||
| rate = if agent.days_infected < model.detection_time | ||
| model.β_und[agent.pos] | ||
| else | ||
| model.β_det[agent.pos] | ||
| end | ||
| d = Poisson(rate) | ||
| n = rand(model.rng, d) | ||
| n == 0 && return | ||
| for contactID in ids_in_position(agent, model) | ||
| contact = model[contactID] | ||
| if contact.status == :S || | ||
| (contact.status == :R && rand(model.rng) ≤ model.reinfection_probability) | ||
| contact.status = :I | ||
| n -= 1 | ||
| n == 0 && return | ||
| end | ||
| end | ||
| end | ||
| update!(agent, model) = agent.status == :I && (agent.days_infected += 1) | ||
| function recover_or_die!(agent, model) | ||
| if agent.days_infected ≥ model.infection_period | ||
| if rand(model.rng) ≤ model.death_rate | ||
| @a kill_agent!(agent, model) | ||
| else | ||
| agent.status = :R | ||
| agent.days_infected = 0 | ||
| end | ||
| end | ||
| end | ||
| ```` | ||
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| That's it! | ||
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| ## Simulation Using AlgebraicAgents.jl | ||
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| We instantiate a sample `ABM` model: | ||
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| ````@example agents | ||
| # create a sample agent based model | ||
| params = create_params(C = 8, max_travel_rate = 0.01) | ||
| abm = model_initiation(; params...) | ||
| ```` | ||
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| Let's specify what data to collect: | ||
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| ````@example agents | ||
| infected(x) = count(i == :I for i in x) | ||
| recovered(x) = count(i == :R for i in x) | ||
| to_collect = [(:status, f) for f in (infected, recovered, length)] | ||
| ```` | ||
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| We wrap the model as an agent: | ||
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| ````@example agents | ||
| m = ABMAgent("sir_model", abm; agent_step!, tspan=(0., 100.), adata=to_collect) | ||
| ```` | ||
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| And we simulate the dynamics: | ||
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| ````@example agents | ||
| simulate(m) | ||
| ```` | ||
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| ````@example agents | ||
| draw(m) | ||
| ```` | ||
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