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jangevaare / Pathogen.jl

Simulation, visualization, and inference of individual level infectious disease models with Julia
MIT License
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Difficulty replicating SIR TN-ILM Simulation and Inference #40

Open Rock910 opened 1 year ago

Rock910 commented 1 year ago

Hi, I am having some difficulty replicating the SIR-TN-ILM Simulation and Inference.ipynb

Specifically at this line: obs = observe(sim, Uniform(0.5, 2.5), Uniform(0.5, 2.5), force=true) I get an argument error, ArgumentError: Uniform: the condition a < b is not satisfied. The stacktrace brings me to EventObservations.jl, where I modified default_rng to be MersenneTwister based on an error that I got previously(i..e that default_rng is not defined).

Would you have any guidance on this? Thank you

jangevaare commented 1 year ago

Can you confirm which version you are using - master branch, latest release etc.

I was in the process in enabling RNG specification throughout the package where I last left off IIRC.

Do you have an infection -> recovered transition that occurs in less than your observation lowerbound of 0.5 time units?

Rock910 commented 1 year ago

Sure, I'm using the master branch. I don't believe so, when I try to check using the SIR plot(I've modified it to show time 0.00 to 0.05), it seems that the number of susceptible and infected is changing but not recovered, same goes for the network plot, where I have only purple and green nodes and no yellow before 0.05 time units.

jangevaare commented 1 year ago

I don't mean between 0 and 0.5 time units for the simulation, but rather, relative to each individual infection time

e.g. if an individual becomes infected at t=3.0, and by chance recovers at t=3.4, that infection would not be observable and not compatible with the force=true option.

Rock910 commented 1 year ago

Oh I see, Sorry about that, how do I check this? Thank you!

jangevaare commented 1 year ago

Something like this:

# Initialize Simulation
sim = Simulation(...)

# Simulate up to t=200
simulate!(sim, tmax=200.0)

# Length of individual infectious periods
infection_lengths = sim.events.removal .- sim.events.infection
Rock910 commented 1 year ago

Thank you, From running this, I get: [Inf, 65.9763967045659, NaN, 59.785132556856624, NaN, 17.63821967448159, 6.495348159419166, 14.667494259013868, 121.96081143417587, 5.941628720911943, 15.09647199170498, 11.332993309900417, 11.139411747684413, 77.53960969417591, 59.408711555105285, 2.2318466276152975, 103.7446297847919, 115.47789369628848, 3.946466194216235, 4.603277041282851, 17.74839218309205, 5.773807919014622, 4.933024633955242, 36.18666132904736, 11.433886050676342, 0.5432687968268368, 164.73225862410607, 4.885180635284534, 4.364415812635588, 2.0477903057130877, 12.95558234985645, NaN, 32.34392390237885, 49.03365521906527, 10.037282007442542, 0.9848841280562368, 15.312208572807679, 22.439238168515402, 18.796788903480866, 42.76173957562435, 0.778588742937167, 3.905699205882655, 13.432002159723146, 1.223713184491288, 60.719296042807116, 74.76988796699553, 23.455269322980136, 2.3528757836759873, 2.010669263092325, 49.06768872547122, 101.43135907599984, 1.1207628816670905, 69.45484779805943, 50.49673067544691, NaN, 16.80180859270157, 5.786430224613923, 3.571711856812911, 0.6594190112128402, 0.6182614968302484, 15.901649134440369, 43.16403994954648, 47.75416348134662, 8.227456455168081, 3.4564250793034503, 14.872374172201468, 3.4475857742651286, 1.9176480444762163, 22.4540593835777, 34.94849435627415, 81.25777471317318, 15.03365871497158, 35.57845651671822, 29.17946170479275, 1.0780794407542018, 4.962185549070298, 174.06052303373272, 15.829750932582568, 41.027625718523744, 3.747325399065115, 4.453627302859971, 6.373850244834813, 1.9561169794708242, 9.123913068720974, 3.5943881697514577, 2.7971511018056177, 18.96481386779506, 1.4045833327382358, 4.056101132293304, 13.674972473368364, 2.2383623354867055, 7.617903321726896, 1.1180999771063647, 0.8807766245960593, 20.229824604040395, 2.394861906149443, 1.3249286204349247, 44.983499229139476, 70.19544845663906, 7.002246365638555]