Add a factory for Poisson / NegativeBinomial / Binomial / BetaBinomial

docs/source/contrib.epidemiology.rst

@@ -32,3 +32,9 @@ SEIR Models
 .. automodule:: pyro.contrib.epidemiology.seir
     :members:
     :member-order: bysource
+
+Distributions
+-------------
+.. automodule:: pyro.contrib.epidemiology.distributions
+    :members:
+    :member-order: bysource

pyro/contrib/epidemiology/__init__.py

@@ -2,11 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from .compartmental import CompartmentalModel
+from .distributions import infection_dist
 from .seir import SimpleSEIRModel
 from .sir import SimpleSIRModel
 
 __all__ = [
     "CompartmentalModel",
     "SimpleSEIRModel",
     "SimpleSIRModel",
+    "infection_dist",
 ]

pyro/contrib/epidemiology/distributions.py

@@ -0,0 +1,98 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+
+import torch
+
+import pyro.distributions as dist
+
+
+def infection_dist(*,
+                   individual_rate,
+                   num_infectious,
+                   num_susceptible=math.inf,
+                   population=math.inf,
+                   concentration=math.inf):
+    """
+    Create a :class:`~pyro.distributions.Distribution` over the number of new
+    infections at a discrete time step.
+
+    This returns a Poisson, Negative-Binomial, Binomial, or Beta-Binomial
+    distribution depending on whether ``population`` and ``concentration`` are
+    finite. In Pyro models, the population is usually finite. In the limit
+    ``population → ∞`` and ``num_susceptible/population → 1``, the Binomial
+    converges to Poisson and the Beta-Binomial converges to Negative-Binomial.
+    In the limit ``concentration → ∞``, the Negative-Binomial converges to
+    Poisson and the Beta-Binomial converges to Binomial.
+
+    The overdispersed distributions (Negative-Binomial and Beta-Binomial
+    returned when ``concentration < ∞``) are useful for modeling superspreader
+    individuals [1,2]. The finitely supported distributions Binomial and
+    Negative-Binomial are useful in small populations and in probabilistic
+    programming systems where truncation or censoring are expensive [3].
+
+    **References**
+
+    [1] J. O. Lloyd-Smith, S. J. Schreiber, P. E. Kopp, W. M. Getz (2005)
+        "Superspreading and the effect of individual variation on disease
+        emergence"
+        https://www.nature.com/articles/nature04153.pdf
+    [2] Lucy M. Li, Nicholas C. Grassly, Christophe Fraser (2017)
+        "Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies
+        and Incidence Time Series"
+        https://academic.oup.com/mbe/article/34/11/2982/3952784
+    [3] Lawrence Murray et al. (2018)
+        "Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic
+        Programs"
+        https://arxiv.org/pdf/1708.07787.pdf
+
+    :param individual_rate: The mean number of infections per infectious
+        individual per time step in the limit of large population, equal to
+        ``R0 / tau`` where ``R0`` is the basic reproductive number and ``tau``
+        is the mean duration of infectiousness.
+    :param num_infectious: The number of infectious individuals at this
+        time step, sometimes ``I``, sometimes ``E+I``.
+    :param num_susceptible: The number ``S`` of susceptible individuals at this
+        time step. This defaults to an infinite population.
+    :param population: The total number of individuals in a population.
+        This defaults to an infinite population.
+    :concentration: The concentration or dispersion parameter ``k`` in
+        overdispersed models of superspreaders [1,2]. This defaults to minimum
+        variance ``concentration = ∞``.
+    """
+    # Convert to colloquial variable names.
+    R = individual_rate
+    I = num_infectious
+    S = num_susceptible
+    N = population
+    k = concentration
+
+    if population == math.inf:
+        if k == math.inf:
+            # Return a Poisson distribution.
+            return dist.Poisson(R * I)
+        else:
+            # Return an overdispersed Negative-Binomial distribution.
+            combined_k = k * I
+            logits = torch.as_tensor(R / k).log()
+            return dist.NegativeBinomial(combined_k, logits=logits)
+    else:
+        # Compute the probability that any given (susceptible, infectious)
+        # pair of individuals results in an infection at this time step.
+        p = torch.as_tensor(R / N).clamp(max=1 - 1e-6)
+        # Combine infections from all individuals.
+        combined_p = p.neg().log1p().mul(I).expm1().neg()  # = 1 - (1 - p)**I
+        combined_p = combined_p.clamp(min=1e-6)
+
+        if k == math.inf:
+            # Return a pure Binomial model, combining the independent Binomial
+            # models of each infectious individual.
+            return dist.ExtendedBinomial(S, combined_p)
+        else:
+            # Return an overdispersed Beta-Binomial model, combining
+            # independent BetaBinomial(c1,c0,S) models for each infectious
+            # individual.
+            c1 = k * I
+            c0 = c1 * (combined_p.reciprocal() - 1).clamp(min=1e-6)
+            return dist.ExtendedBetaBinomial(c1, c0, S)

pyro/contrib/epidemiology/seir.py

@@ -9,6 +9,7 @@
 from pyro.ops.tensor_utils import convolve
 
 from .compartmental import CompartmentalModel
+from .distributions import infection_dist
 
 
 class SimpleSEIRModel(CompartmentalModel):
@@ -79,29 +80,25 @@ def global_model(self):
         tau_i = self.recovery_time
         R0 = pyro.sample("R0", dist.LogNormal(0., 1.))
         rho = pyro.sample("rho", dist.Uniform(0, 1))
-
-        # Convert interpretable parameters to distribution parameters.
-        rate_s = -R0 / (tau_i * self.population)
-        prob_e = 1 / tau_e
-        prob_i = 1 / tau_i
-
-        return rate_s, prob_e, prob_i, rho
+        return R0, tau_e, tau_i, rho
 
     def initialize(self, params):
         # Start with a single infection.
         return {"S": self.population - 1, "E": 0, "I": 1}
 
     def transition_fwd(self, params, state, t):
-        rate_s, prob_e, prob_i, rho = params
+        R0, tau_e, tau_i, rho = params
 
         # Sample flows between compartments.
-        prob_s = -(rate_s * state["I"]).expm1()
         S2E = pyro.sample("S2E_{}".format(t),
-                          dist.Binomial(state["S"], prob_s))
+                          infection_dist(individual_rate=R0 / tau_i,
+                                         num_susceptible=state["S"],
+                                         num_infectious=state["I"],
+                                         population=self.population))
         E2I = pyro.sample("E2I_{}".format(t),
-                          dist.Binomial(state["E"], prob_e))
+                          dist.Binomial(state["E"], 1 / tau_e))
         I2R = pyro.sample("I2R_{}".format(t),
-                          dist.Binomial(state["I"], prob_i))
+                          dist.Binomial(state["I"], 1 / tau_i))
 
         # Update compartments with flows.
         state["S"] = state["S"] - S2E
@@ -114,23 +111,25 @@ def transition_fwd(self, params, state, t):
                     obs=self.data[t] if t < self.duration else None)
 
     def transition_bwd(self, params, prev, curr, t):
-        rate_s, prob_e, prob_i, rho = params
+        R0, tau_e, tau_i, rho = params
 
         # Reverse the flow computation.
         S2E = prev["S"] - curr["S"]
         E2I = prev["E"] - curr["E"] + S2E
         I2R = prev["I"] - curr["I"] + E2I
 
         # Condition on flows between compartments.
-        prob_s = -(rate_s * prev["I"]).expm1()
         pyro.sample("S2E_{}".format(t),
-                    dist.ExtendedBinomial(prev["S"], prob_s),
+                    infection_dist(individual_rate=R0 / tau_i,
+                                   num_susceptible=prev["S"],
+                                   num_infectious=prev["I"],
+                                   population=self.population),
                     obs=S2E)
         pyro.sample("E2I_{}".format(t),
-                    dist.ExtendedBinomial(prev["E"], prob_e),
+                    dist.ExtendedBinomial(prev["E"], 1 / tau_e),
                     obs=E2I)
         pyro.sample("I2R_{}".format(t),
-                    dist.ExtendedBinomial(prev["I"], prob_i),
+                    dist.ExtendedBinomial(prev["I"], 1 / tau_i),
                     obs=I2R)
 
         # Condition on observations.

pyro/contrib/epidemiology/sir.py

@@ -8,6 +8,7 @@
 from pyro.ops.tensor_utils import convolve
 
 from .compartmental import CompartmentalModel
+from .distributions import infection_dist
 
 
 class SimpleSIRModel(CompartmentalModel):
@@ -65,26 +66,23 @@ def global_model(self):
         tau = self.recovery_time
         R0 = pyro.sample("R0", dist.LogNormal(0., 1.))
         rho = pyro.sample("rho", dist.Uniform(0, 1))
-
-        # Convert interpretable parameters to distribution parameters.
-        rate_s = -R0 / (tau * self.population)
-        prob_i = 1 / tau
-
-        return rate_s, prob_i, rho
+        return R0, tau, rho
 
     def initialize(self, params):
         # Start with a single infection.
         return {"S": self.population - 1, "I": 1}
 
     def transition_fwd(self, params, state, t):
-        rate_s, prob_i, rho = params
+        R0, tau, rho = params
 
         # Sample flows between compartments.
-        prob_s = -(rate_s * state["I"]).expm1()
         S2I = pyro.sample("S2I_{}".format(t),
-                          dist.Binomial(state["S"], prob_s))
+                          infection_dist(individual_rate=R0 / tau,
+                                         num_susceptible=state["S"],
+                                         num_infectious=state["I"],
+                                         population=self.population))
         I2R = pyro.sample("I2R_{}".format(t),
-                          dist.Binomial(state["I"], prob_i))
+                          dist.Binomial(state["I"], 1 / tau))
 
         # Update compartments with flows.
         state["S"] = state["S"] - S2I
@@ -96,19 +94,21 @@ def transition_fwd(self, params, state, t):
                     obs=self.data[t] if t < self.duration else None)
 
     def transition_bwd(self, params, prev, curr, t):
-        rate_s, prob_i, rho = params
+        R0, tau, rho = params
 
         # Reverse the flow computation.
         S2I = prev["S"] - curr["S"]
         I2R = prev["I"] - curr["I"] + S2I
 
         # Condition on flows between compartments.
-        prob_s = -(rate_s * prev["I"]).expm1()
         pyro.sample("S2I_{}".format(t),
-                    dist.ExtendedBinomial(prev["S"], prob_s),
+                    infection_dist(individual_rate=R0 / tau,
+                                   num_susceptible=prev["S"],
+                                   num_infectious=prev["I"],
+                                   population=self.population),
                     obs=S2I)
         pyro.sample("I2R_{}".format(t),
-                    dist.ExtendedBinomial(prev["I"], prob_i),
+                    dist.ExtendedBinomial(prev["I"], 1 / tau),
                     obs=I2R)
 
         # Condition on observations.

tests/contrib/epidemiology/test_distributions.py

@@ -0,0 +1,112 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+
+import pyro.distributions as dist
+from pyro.contrib.epidemiology import infection_dist
+
+from tests.common import assert_close
+
+
+def assert_dist_close(d1, d2):
+    x = torch.arange(float(200))
+    p1 = d1.log_prob(x).exp()
+    p2 = d2.log_prob(x).exp()
+
+    assert (p1.sum() - 1).abs() < 1e-3, "incomplete mass"
+    assert (p2.sum() - 1).abs() < 1e-3, "incomplete mass"
+
+    mean1 = (p1 * x).sum()
+    mean2 = (p2 * x).sum()
+    assert_close(mean1, mean2, rtol=0.05)
+
+    max_prob = torch.max(p1.max(), p2.max())
+    assert (p1 - p2).abs().max() / max_prob < 0.05
+
+
+@pytest.mark.parametrize("R0,I", [
+    (1., 1),
+    (1., 10),
+    (10., 1),
+    (5., 5),
+])
+def test_binomial_vs_poisson(R0, I):
+    R0 = torch.tensor(R0)
+    I = torch.tensor(I)
+
+    d1 = infection_dist(individual_rate=R0, num_infectious=I)
+    d2 = infection_dist(individual_rate=R0, num_infectious=I,
+                        num_susceptible=1000., population=1000.)
+
+    assert isinstance(d1, dist.Poisson)
+    assert isinstance(d2, dist.Binomial)
+    assert_dist_close(d1, d2)
+
+
+@pytest.mark.parametrize("R0,I,k", [
+    (1., 1., 0.5),
+    (1., 1., 1.),
+    (1., 1., 2.),
+    (1., 10., 0.5),
+    (1., 10., 1.),
+    (1., 10., 2.),
+    (10., 1., 0.5),
+    (10., 1., 1.),
+    (10., 1., 2.),
+    (5., 5, 0.5),
+    (5., 5, 1.),
+    (5., 5, 2.),
+])
+def test_beta_binomial_vs_negative_binomial(R0, I, k):
+    R0 = torch.tensor(R0)
+    I = torch.tensor(I)
+
+    d1 = infection_dist(individual_rate=R0, num_infectious=I, concentration=k)
+    d2 = infection_dist(individual_rate=R0, num_infectious=I, concentration=k,
+                        num_susceptible=1000., population=1000.)
+
+    assert isinstance(d1, dist.NegativeBinomial)
+    assert isinstance(d2, dist.BetaBinomial)
+    assert_dist_close(d1, d2)
+
+
+@pytest.mark.parametrize("R0,I", [
+    (1., 1.),
+    (1., 10.),
+    (10., 1.),
+    (5., 5.),
+])
+def test_beta_binomial_vs_binomial(R0, I):
+    R0 = torch.tensor(R0)
+    I = torch.tensor(I)
+
+    d1 = infection_dist(individual_rate=R0, num_infectious=I,
+                        num_susceptible=20., population=30.)
+    d2 = infection_dist(individual_rate=R0, num_infectious=I,
+                        num_susceptible=20., population=30.,
+                        concentration=200.)
+
+    assert isinstance(d1, dist.Binomial)
+    assert isinstance(d2, dist.BetaBinomial)
+    assert_dist_close(d1, d2)
+
+
+@pytest.mark.parametrize("R0,I", [
+    (1., 1.),
+    (1., 10.),
+    (10., 1.),
+    (5., 5.),
+])
+def test_negative_binomial_vs_poisson(R0, I):
+    R0 = torch.tensor(R0)
+    I = torch.tensor(I)
+
+    d1 = infection_dist(individual_rate=R0, num_infectious=I)
+    d2 = infection_dist(individual_rate=R0, num_infectious=I,
+                        concentration=200.)
+
+    assert isinstance(d1, dist.Poisson)
+    assert isinstance(d2, dist.NegativeBinomial)
+    assert_dist_close(d1, d2)

tests/contrib/epidemiology/test_seir.py

@@ -13,7 +13,7 @@
     {"dct": 1.},
     {"num_quant_bins": 8},
 ], ids=str)
-def test_smoke(duration, forecast, options):
+def test_simple_smoke(duration, forecast, options):
     population = 100
     incubation_time = 2.0
     recovery_time = 7.0
@@ -36,3 +36,5 @@ def test_smoke(duration, forecast, options):
     # Predict and forecast.
     samples = model.predict(forecast=forecast)
     assert samples["S"].shape == (num_samples, duration + forecast)
+    assert samples["E"].shape == (num_samples, duration + forecast)
+    assert samples["I"].shape == (num_samples, duration + forecast)