SEIRS model example

The package ships a worked example in examples/fdp/: an SEIRS epidemic model whose one input, its parameter table, and two outputs, a results table and a figure, are tracked by the FAIR Data Pipeline. It is the example the package's own tests are built on. The example has its own environment, examples/fdp/Project.toml, holding the packages the model uses as well as DataPipeline.

The user config

examples/fdp/SEIRSconfig.yaml is the config handed to fair. Its register: block names the parameter table as an external object, so fair pull downloads and registers it; its write: block declares the two outputs; and run_metadata.script is the command fair run executes.

using DataPipeline
example = joinpath(pkgdir(DataPipeline), "examples", "fdp")
print(read(joinpath(example, "SEIRSconfig.yaml"), String))

run_metadata:
  default_input_namespace: testing
  description: SEIRS Model julia
  script: |
    julia --project=examples/fdp -e "using Pkg; Pkg.instantiate()"
    julia --project=examples/fdp examples/fdp/seirs_sim.jl

register:
- namespace: PSU
  full_name: Pennsylvania State University
  website: https://ror.org/04p491231

- external_object: SEIRS_model/parameters
  namespace_name: PSU
  root: https://raw.githubusercontent.com/
  path: FAIRDataPipeline/rSimpleModel/main/inst/extdata/static_params_SEIRS.csv
  title: Static parameters of the model
  description: Static parameters of the model
  identifier: https://doi.org/10.1038/s41592-020-0856-2
  file_type: csv
  release_date: 2020-06-01T12:00
  version: "1.0.0"
  primary: False

write:
- data_product: model_output
  description: SEIRS model results
  file_type: csv
  use:
    data_product: SEIRS_model/results/model_output/julia

- data_product: figure
  description: SEIRS output plot
  file_type: pdf
  use:
    data_product: SEIRS_model/results/figure/julia

The model script

examples/fdp/seirs_sim.jl brackets the model with the API: DataPipeline.initialise opens a code run from the working config fair run wrote, link_read! gives the path of the parameter table and records it as an input, link_write! gives a path for each output and records it, and DataPipeline.finalise names the output files by their hash, registers them, and attaches everything to the code run.

print(read(joinpath(example, "seirs_sim.jl"), String))

# SPDX-License-Identifier: LGPL-3.0-or-later

### SEIRS model example
using DataPipeline
using CSV
using DataFrames
using Plots
include(joinpath(@__DIR__, "SEIRSModel.jl"))
using .SEIRSModel

# Initialise code run
handle = DataPipeline.initialise()

# Read model parameters
path = link_read!(handle, "SEIRS_model/parameters")
static_params = CSV.read(path, DataFrames.DataFrame)

alpha = getparameter(static_params, "alpha")
beta = getparameter(static_params, "beta")
inv_gamma = getparameter(static_params, "inv_gamma")
inv_omega = getparameter(static_params, "inv_omega")
inv_mu = getparameter(static_params, "inv_mu")
inv_sigma = getparameter(static_params, "inv_sigma")

# Set initial state
timesteps = 1000
years = 5
initial_state = Dict("S" => 0.999, "E" => 0.001, "I" => 0, "R" => 0)

# Run the model
results = modelseirs(initial_state, timesteps, years, alpha, beta,
                     inv_gamma, inv_omega, inv_mu, inv_sigma);

ENV["GKSwstype"]="100"
g = plotseirs(results);

# Save outputs to data store
path = link_write!(handle, "model_output")
CSV.write(path, results)

path = link_write!(handle, "figure")
savefig(g, path)

# Register code run in local registry
DataPipeline.finalise(handle)

The model

examples/fdp/SEIRSModel.jl is the model itself, a module the script includes, which knows nothing of the pipeline: it takes the parameters and returns the results table, and plots it.

print(read(joinpath(example, "SEIRSModel.jl"), String))

# SPDX-License-Identifier: LGPL-3.0-or-later

# The SEIRS model that `seirs_sim.jl` runs through the pipeline: the
# example's own module, which knows nothing of the pipeline
module SEIRSModel

using DataFrames
using Plots

export modelseirs, plotseirs, getparameter

"""
    modelseirs(initial_state, timesteps, years, alpha, beta, inv_gamma, inv_omega, inv_mu, inv_sigma)

Run SEIRS model
"""
function modelseirs(initial_state::Dict, timesteps::Int64, years::Int64,
                    alpha::Float64, beta::Float64, inv_gamma::Float64,
                    inv_omega::Float64, inv_mu::Float64, inv_sigma::Float64)
    S = initial_state["S"]
    E = initial_state["E"]
    I = initial_state["I"]
    R = initial_state["R"]
    time_unit_years = years / timesteps
    time_unit_days = time_unit_years * 365.25

    # Convert parameters to days
    alpha = alpha * time_unit_days
    beta = beta * time_unit_days
    gamma = time_unit_days / inv_gamma
    omega = time_unit_days / (inv_omega * 365.25)
    mu = time_unit_days / (inv_mu * 365.25)
    sigma = time_unit_days / inv_sigma

    results = DataFrames.DataFrame(time = 0, S = S, E = E, I = I, R = R)

    for t in 1:timesteps
        N = S + E + I + R
        birth = mu * N
        infection = (beta * results.I[t] * results.S[t]) / N
        lost_immunity = omega * results.R[t]
        death_S = mu * results.S[t]
        death_E = mu * results.E[t]
        death_I = (mu + alpha) * results.I[t]
        death_R = mu * results.R[t]
        latency = sigma * results.E[t]
        recovery = gamma * results.I[t]

        S_rate = birth - infection + lost_immunity - death_S
        E_rate = infection - latency - death_E
        I_rate = latency - recovery - death_I
        R_rate = recovery - lost_immunity - death_R

        new_S = results.S[t] + S_rate
        new_E = results.E[t] + E_rate
        new_I = results.I[t] + I_rate
        new_R = results.R[t] + R_rate

        new = DataFrames.DataFrame(time = t * time_unit_days,
                                   S = new_S, E = new_E,
                                   I = new_I, R = new_R)

        results = vcat(results, new)
    end

    results.time = results.time / 365.25
    return results
end

"""
    plotseirs(results)

Plot model output
"""
function plotseirs(results::DataFrames.DataFrame)
    # Left plot
    x = results.time
    y1 = Matrix(results[:, 2:5]) .* 100
    p1 = plot(x, y1, label = ["S" "E" "I" "R"], lw = 3)
    xlabel!("Years")
    ylabel!("Relative group size (%)")

    # Right plot
    y2 = y1[:, 2:3]
    p2 = plot(x, y2, label = ["E" "I"], lw = 3)
    xlabel!("Years")
    ylabel!("Relative group size (%)")

    # Join plots together
    pp = plot(p1, p2, plot_title = "SEIRS model trajectories")
    return pp
end

"""
    getparameter(data, parameter)

Get parameter from DataFrame
"""
function getparameter(data::DataFrames.DataFrame, parameter::String)
    output = filter(row -> row.param == parameter, data).value[1]
    return output
end

end

Running it

With the pipeline installed (see the FAIR Data Pipeline documentation), from the root of a clone of this repository:

fair init
fair pull examples/fdp/SEIRSconfig.yaml
fair run examples/fdp/SEIRSconfig.yaml

fair run ends by printing the URL of the registered code run. fair add with that run's uuid, then fair push, sends it and its data products to a remote registry.

The two blocks below do exactly that, through fair"...", which runs the CLI from Julia with its arguments as on the command line, when this documentation is built with the environment variable FDP_DOCS_RUN_EXAMPLE set to true, as the package's own documentation build does; built without it, they only say so.

run_example = get(ENV, "FDP_DOCS_RUN_EXAMPLE", "") == "true"
run_example || println("Not run: FDP_DOCS_RUN_EXAMPLE is not set.")
# fair runs from the root of the repository, where the project is
run_example && cd(pkgdir(DataPipeline)) do
    fair"pull --local examples/fdp/SEIRSconfig.yaml"
end

Pulling, please wait
INFO:FAIRDataPipeline.Register:Registering: SEIRS_model/parameters
Registering: SEIRS_model/parameters
There are no items in the [read] block to pull from registry: 'origin'.

run_example && cd(pkgdir(DataPipeline)) do
    fair"run --local examples/fdp/SEIRSconfig.yaml"
end

Preparing the job and running its script, please wait
    Updating registry at `~/.julia/registries/General.toml`
   Installed DocStringExtensions ─ v0.10.2
    Updating `~/work/DataPipeline.jl/DataPipeline.jl/examples/fdp/Project.toml`
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  52.9 s  ✓ Plots
  4 dependencies successfully precompiled in 57 seconds. 224 already precompiled.
processing config file: /home/runner/work/DataPipeline.jl/DataPipeline.jl/.fair/data_store/jobs/2026-10-30_03_54_12_985923/config.yaml
pipeline initialised.
data product found: SEIRS_model/parameters
finished - code run locally registered as: http://127.0.0.1:8000/api/code_run/1/

Rehearsing it

Before a run is recorded, the script can be rehearsed: run through the pipeline from the same config, reading the registered parameter table and writing its outputs, with nothing registered. From the root of the clone, in Julia:

using DataPipeline
DataPipeline.rehearse("examples/fdp/SEIRSconfig.yaml", local_only = true)
include("examples/fdp/seirs_sim.jl")

rehearse has fair prepare the job as a run would, and the script, unchanged, opens a rehearsal in its DataPipeline.initialise and ends by collecting its two outputs in a folder, whose path DataPipeline.finalise prints and returns. Run after the recorded run above, it finds the results table identical to the version registered, and says so. The block below does this under the same condition as the two above.

run_example && cd(pkgdir(DataPipeline)) do
    DataPipeline.rehearse("examples/fdp/SEIRSconfig.yaml", local_only = true)
    include(joinpath(example, "seirs_sim.jl"))
end

rehearsing the run prepared in /home/runner/work/DataPipeline.jl/DataPipeline.jl/.fair/data_store/jobs/2026-10-30_03_55_51_672274
run the script, or its lines, in this session: DataPipeline.finalise collects its outputs in /tmp/fdp-rehearsal-E1sbkG, and DataPipeline.discard throws them away
processing config file: /home/runner/work/DataPipeline.jl/DataPipeline.jl/.fair/data_store/jobs/2026-10-30_03_55_51_672274/config.yaml
rehearsal: nothing is registered, and the outputs go to /tmp/fdp-rehearsal-E1sbkG
would keep the code repository's object at ee21045641e191c37ef4760629d75f85d4e85db8, http://127.0.0.1:8000/api/object/4/
  with the licence https://spdx.org/licenses/LGPL-3.0-or-later
  with the keywords FAIR data, data management, julia, reproducibility, workflows
data product found: SEIRS_model/parameters
data product identical to v0.0.1: testing:SEIRS_model/results/model_output/julia@v0.0.2
rehearsal finished - nothing registered
  inputs:
    PSU:SEIRS_model/parameters (1.0.0) /home/runner/work/DataPipeline.jl/DataPipeline.jl/.fair/data_store/PSU/SEIRS_model/parameters/1.0.0.csv
  outputs:
    testing:SEIRS_model/results/figure/julia (0.0.2) /tmp/fdp-rehearsal-E1sbkG/testing/SEIRS_model/results/figure/julia/0.0.2.pdf
    testing:SEIRS_model/results/model_output/julia (0.0.2) /tmp/fdp-rehearsal-E1sbkG/testing/SEIRS_model/results/model_output/julia/0.0.2.csv, identical to v0.0.1
  outputs folder: /tmp/fdp-rehearsal-E1sbkG