{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Epidemiological models: Introduction\n",
"\n",
"This tutorial introduces the [pyro.contrib.epidemiology](http://docs.pyro.ai/en/latest/contrib.epidemiology.html) module, an epidemiological modeling language with a number of black box inference algorithms. This tutorial assumes the reader is already familiar with [modeling](http://pyro.ai/examples/intro_long.html), [inference](http://pyro.ai/examples/intro_long.html), and [distribution shapes](http://pyro.ai/examples/tensor_shapes.html).\n",
"\n",
"See also the following scripts:\n",
"\n",
"- [Epidemiological models: Univariate](http://pyro.ai/examples/epi_sir.html)\n",
"- [Epidemiological models: Regional](http://pyro.ai/examples/epi_regional.html)\n",
"- [Epidemiological inference via HMC](http://pyro.ai/examples/sir_hmc.html)\n",
"\n",
"#### Summary\n",
"\n",
"- To create a new model, inherit from the [CompartmentalModel](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel) base class.\n",
"- Override methods [.global_model()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.global_model), [.initialize(params)](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.initialize), and [.transition(params, state, t)](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.transition).\n",
"- Take care to support broadcasting and vectorized interpretation in those methods.\n",
"- For single time series, set `population` to an integer.\n",
"- For batched time series, let `population` be a vector, and use [self.region_plate](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel).\n",
"- For models with complex inter-compartment flows, override the [.compute_flows()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.compute_flows) method. \n",
"- Flows with loops (undirected or directed) are not currently supported.\n",
"- To perform cheap approximate inference via SVI, call the [.fit_svi()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.fit_svi) method.\n",
"- To perform more expensive inference via MCMC, call the [.fit_mcmc()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.fit_mcmc) method.\n",
"- To stochastically predict latent and future variables, call the [.predict()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.predict) method.\n",
"\n",
"#### Table of contents\n",
"\n",
"- [Basic workflow](#Basic-workflow)\n",
" - [Modeling](#Modeling)\n",
" - [Generating data](#Generating-data)\n",
" - [Inference](#Inference)\n",
" - [Prediction](#Prediction)\n",
" - [Forecasting](#Forecasting)\n",
"- [Advanced modeling](#Advanced-modeling)\n",
" - [Regional models](#Regional-models)\n",
" - [Phylogenetic likelihoods](#Phylogenetic-likelihoods)\n",
" - [Heterogeneous models](#Heterogeneous-models)\n",
" - [Complex compartment flow](#Complex-compartment-flow)\n",
"- [References](#References)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import torch\n",
"import pyro\n",
"import pyro.distributions as dist\n",
"from pyro.contrib.epidemiology import CompartmentalModel, binomial_dist, infection_dist\n",
"\n",
"%matplotlib inline\n",
"assert pyro.__version__.startswith('1.9.0')\n",
"torch.set_default_dtype(torch.double) # Required for MCMC inference.\n",
"smoke_test = ('CI' in os.environ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic workflow \n",
"\n",
"The [pyro.contrib.epidemiology](http://docs.pyro.ai/en/latest/contrib.epidemiology.html) module provides a modeling language for a class of stochastic discrete-time discrete-count compartmental models, together with a number of black box inference algorithms to perform joint inference on global parameters and latent variables. This modeling language is more restrictive than the full Pyro probabilistic programming language:\n",
"\n",
"- control flow must be static;\n",
"- compartmental distributions are restricted to [binomial_dist()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.distributions.binomial_dist), [beta_binomial_dist()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.distributions.beta_binomial_dist), and [infection_dist()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.distributions.infection_dist);\n",
"- plates are not allowed, except for the single optional [.region_plate](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.region_plate);\n",
"- all random variables must be either global or Markov and sampled at every time step, so e.g. time-windowed random variables are not supported;\n",
"- models must support broadcasting and vectorization of time `t`.\n",
"\n",
"These restrictions allow inference algorithms to vectorize over the time dimension, leading to inference algorithms with per-iteration parallel complexity sublinear in length of the time axis. The restriction on distributions allows inference algorithms to approximate parts of the model as Gaussian via moment matching, further speeding up inference. Finally, because real data is so often overdispersed relative to Binomial idealizations, the three distribution helpers provide an [overdispersion](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.distributions.binomial_dist) parameter calibrated so that in the large-population limit all distribution helpers converge to log-normal.\n",
"\n",
"Black box inference algorithms currently include: [SVI](http://docs.pyro.ai/en/latest/inference_algos.html#pyro.infer.svi.SVI) with a moment-matching approximation, and [NUTS](http://docs.pyro.ai/en/latest/mcmc.html#pyro.infer.mcmc.NUTS) either with a moment-matched approximation or with an exact auxiliary variable method detailed in the [SIR HMC tutorial](http://pyro.ai/examples/sir_hmc.html). All three algorithms initialize using [SMC](http://docs.pyro.ai/en/latest/inference_algos.html#pyro.infer.smcfilter.SMCFilter) and reparameterize time dependent variables using a fast [Haar wavelet](http://docs.pyro.ai/en/latest/infer.reparam.html#pyro.infer.reparam.haar.HaarReparam) transform. Default inference parameters are set for cheap approximate results; accurate results will require more steps and ideally comparison among different inference algorithms. We recommend that, when running MCMC inference, you use multiple chains, thus making it easier to diagnose mixing issues.\n",
"\n",
"While MCMC inference can be more accurate for a given model, SVI is much faster and thus allows richer model structure (e.g. incorporating neural networks) and more rapid [model iteration](https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-022513-115657?journalCode=statistics). We recommend starting model exploration using mean field SVI (via `.fit_svi(guide_rank=0)`), then optionally increasing accuracy using a low-rank multivariate normal guide (via `.fit_svi(guide_rank=None)`). For even more accurate posteriors you could then try moment-matched MCMC (via `.fit_mcmc(num_quant_bins=1)`), or the most accurate and most expensive enumerated MCMC (via `.fit_mcmc(num_quant_bins=4)`). We recommend that, when fitting models with neural networks, you train via `.fit_svi()`, then freeze the network (say by omitting a `pyro.module()` statement) before optionally running MCMC inference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modeling \n",
"\n",
"The [pyro.contrib.epidemiology.models](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#module-pyro.contrib.epidemiology.models) module provides a number of example models. While in principle these are reusable, we recommend forking and modifying these models for your task. Let's take a look at one of the simplest examples, [SimpleSIRModel](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.models.SimpleSIRModel). This model derives from the [CompartmentalModel](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel) base class and overrides the three standard methods using familiar Pyro modeling code in each method.\n",
"\n",
"- [.global_model()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.global_model) samples global parameters and packs them into a single return value (here a tuple, but any structure is allowed). The return value is available as the `params` argument to the other two methods.\n",
"- [.initialize(params)](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.initialize) samples (or deterministically sets) initial values of time series, returning a dictionary mapping time series name to initial value.\n",
"- [.transition(params, state, t)](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.transition) inputs global `params`, the `state` at the previous time step, and the time index `t` (which may be a slice!). It then samples flows and updates the state dict."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class SimpleSIRModel(CompartmentalModel):\n",
" def __init__(self, population, recovery_time, data):\n",
" compartments = (\"S\", \"I\") # R is implicit.\n",
" duration = len(data)\n",
" super().__init__(compartments, duration, population)\n",
" assert isinstance(recovery_time, float)\n",
" assert recovery_time > 1\n",
" self.recovery_time = recovery_time\n",
" self.data = data\n",
"\n",
" def global_model(self):\n",
" tau = self.recovery_time\n",
" R0 = pyro.sample(\"R0\", dist.LogNormal(0., 1.))\n",
" rho = pyro.sample(\"rho\", dist.Beta(100, 100))\n",
" return R0, tau, rho\n",
"\n",
" def initialize(self, params):\n",
" # Start with a single infection.\n",
" return {\"S\": self.population - 1, \"I\": 1}\n",
"\n",
" def transition(self, params, state, t):\n",
" R0, tau, rho = params\n",
"\n",
" # Sample flows between compartments.\n",
" S2I = pyro.sample(\"S2I_{}\".format(t),\n",
" infection_dist(individual_rate=R0 / tau,\n",
" num_susceptible=state[\"S\"],\n",
" num_infectious=state[\"I\"],\n",
" population=self.population))\n",
" I2R = pyro.sample(\"I2R_{}\".format(t),\n",
" binomial_dist(state[\"I\"], 1 / tau))\n",
"\n",
" # Update compartments with flows.\n",
" state[\"S\"] = state[\"S\"] - S2I\n",
" state[\"I\"] = state[\"I\"] + S2I - I2R\n",
"\n",
" # Condition on observations.\n",
" t_is_observed = isinstance(t, slice) or t < self.duration\n",
" pyro.sample(\"obs_{}\".format(t),\n",
" binomial_dist(S2I, rho),\n",
" obs=self.data[t] if t_is_observed else None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we've stored data in the model. These models have a scikit-learn like interface: we instantiate a model class with data, then call a `.fit_*()` method to train, then call `.predict()` on a trained model.\n",
"\n",
"Note also that we've taken special care so that `t` can be either an integer or a `slice`. Under the hood, `t` is an integer during SMC initialization, a `slice` during SVI or MCMC inference, and an integer again during prediction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generating data \n",
"\n",
"To check that our model generates plausible data, we can create a model with empty data and call the model's [.generate()](http://docs.pyro.ai/en/latest/contrib.epidemiology.html#pyro.contrib.epidemiology.compartmental.CompartmentalModel.generate) method. This method first calls, `.global_model()`, then calls `.initialize()`, then calls `.transition()` once per time step (based on the length of our empty data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulated 4055.0 infections after 6 attempts\n"
]
}
],
"source": [
"population = 10000\n",
"recovery_time = 10.\n",
"empty_data = [None] * 90\n",
"model = SimpleSIRModel(population, recovery_time, empty_data)\n",
"\n",
"# We'll repeatedly generate data until a desired number of infections is found.\n",
"pyro.set_rng_seed(20200709)\n",
"for attempt in range(100):\n",
" synth_data = model.generate({\"R0\": 2.0})\n",
" total_infections = synth_data[\"S2I\"].sum().item()\n",
" if 4000 <= total_infections <= 6000:\n",
" break\n",
"print(\"Simulated {} infections after {} attempts\".format(total_infections, 1 + attempt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The generated data contains both global variables and time series, packed into tensors."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I.shape = (90,)\n",
"I2R.shape = (90,)\n",
"R0.shape = ()\n",
"S.shape = (90,)\n",
"S2I.shape = (90,)\n",
"obs.shape = (90,)\n",
"rho.shape = ()\n"
]
}
],
"source": [
"for key, value in sorted(synth_data.items()):\n",
" print(\"{}.shape = {}\".format(key, tuple(value.shape)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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