From 87a8934509dc0d8413ecb79f438a9bce1383786f Mon Sep 17 00:00:00 2001 From: Christopher Lorton Date: Tue, 7 Jan 2025 17:21:23 -0500 Subject: [PATCH] =?UTF-8?q?=F0=9F=9A=A7=20metapop=20WIP=20VII?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 2 +- src/laser_cholera/__init__.py | 1 + src/laser_cholera/iso_codes.py | 88 +- src/laser_cholera/metapop/__init__.py | 26 +- src/laser_cholera/metapop/births.py | 36 - src/laser_cholera/metapop/census.py | 86 + .../metapop/data/default_parameters.json | 196 + .../metapop/data/default_parameters.yaml | 74270 ++++++++++++++++ src/laser_cholera/metapop/environmental.py | 116 +- src/laser_cholera/metapop/envtohuman.py | 326 + src/laser_cholera/metapop/humantohuman.py | 168 + src/laser_cholera/metapop/infected.py | 58 - src/laser_cholera/metapop/infectious.py | 89 + src/laser_cholera/metapop/lambda_.py | 72 - src/laser_cholera/metapop/model.py | 115 +- src/laser_cholera/metapop/params.py | 100 +- src/laser_cholera/metapop/psi.py | 65 - src/laser_cholera/metapop/recovered.py | 92 +- src/laser_cholera/metapop/susceptible.py | 86 + src/laser_cholera/metapop/susceptibles.py | 70 - src/laser_cholera/metapop/transmission.py | 56 - src/laser_cholera/metapop/vaccinated.py | 101 +- 22 files changed, 75638 insertions(+), 581 deletions(-) delete mode 100644 src/laser_cholera/metapop/births.py create mode 100644 src/laser_cholera/metapop/census.py create mode 100644 src/laser_cholera/metapop/data/default_parameters.json create mode 100644 src/laser_cholera/metapop/data/default_parameters.yaml create mode 100644 src/laser_cholera/metapop/envtohuman.py create mode 100644 src/laser_cholera/metapop/humantohuman.py delete mode 100644 src/laser_cholera/metapop/infected.py create mode 100644 src/laser_cholera/metapop/infectious.py delete mode 100644 src/laser_cholera/metapop/lambda_.py delete mode 100644 src/laser_cholera/metapop/psi.py create mode 100644 src/laser_cholera/metapop/susceptible.py delete mode 100644 src/laser_cholera/metapop/susceptibles.py delete mode 100644 src/laser_cholera/metapop/transmission.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bd1b8bd..2d7a1d4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,7 @@ exclude: '^(\.tox|ci/templates|\.bumpversion\.cfg)(/|$)' # Note the order is intentional to avoid multiple passes of the hooks repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.8.0 + rev: v0.8.6 hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix, --show-fixes] diff --git a/src/laser_cholera/__init__.py b/src/laser_cholera/__init__.py index 6f42bc7..64333eb 100644 --- a/src/laser_cholera/__init__.py +++ b/src/laser_cholera/__init__.py @@ -6,6 +6,7 @@ __all__ = [ "PropagatePopulation", + "__version__", "compute", "iso_codes", ] diff --git a/src/laser_cholera/iso_codes.py b/src/laser_cholera/iso_codes.py index 9eb03d0..281a80b 100644 --- a/src/laser_cholera/iso_codes.py +++ b/src/laser_cholera/iso_codes.py @@ -1,43 +1,45 @@ -iso_codes = sorted([ - "AGO", # Angola - "BEN", # Benin - "BWA", # Botswana - "BFA", # Burkina Faso - "BDI", # Burundi - "CMR", # Cameroon - "CAF", # Central African Republic - "TCD", # Chad - "COG", # Congo - "COD", # Democratic Republic of the Congo - "CIV", # Côte d'Ivoire - "GNQ", # Equatorial Guinea - "ERI", # Eritrea - "SWZ", # Eswatini - "ETH", # Ethiopia - "GAB", # Gabon - "GMB", # Gambia - "GHA", # Ghana - "GIN", # Guinea - "GNB", # Guinea-Bissau - "KEN", # Kenya - "LSO", # Lesotho - "LBR", # Liberia - "MWI", # Malawi - "MLI", # Mali - "MRT", # Mauritania - "MOZ", # Mozambique - "NAM", # Namibia - "NER", # Niger - "NGA", # Nigeria - "RWA", # Rwanda - "SEN", # Senegal - "SLE", # Sierra Leone - "SOM", # Somalia - "ZAF", # South Africa - "SSD", # South Sudan - "TGO", # Togo - "UGA", # Uganda - "TZA", # United Republic of Tanzania - "ZMB", # Zambia - "ZWE", # Zimbabwe -]) +iso_codes = sorted( + [ + "AGO", # Angola + "BEN", # Benin + "BWA", # Botswana + "BFA", # Burkina Faso + "BDI", # Burundi + "CMR", # Cameroon + "CAF", # Central African Republic + "TCD", # Chad + "COG", # Congo + "COD", # Democratic Republic of the Congo + "CIV", # Côte d'Ivoire + "GNQ", # Equatorial Guinea + "ERI", # Eritrea + "SWZ", # Eswatini + "ETH", # Ethiopia + "GAB", # Gabon + "GMB", # Gambia + "GHA", # Ghana + "GIN", # Guinea + "GNB", # Guinea-Bissau + "KEN", # Kenya + "LSO", # Lesotho + "LBR", # Liberia + "MWI", # Malawi + "MLI", # Mali + "MRT", # Mauritania + "MOZ", # Mozambique + "NAM", # Namibia + "NER", # Niger + "NGA", # Nigeria + "RWA", # Rwanda + "SEN", # Senegal + "SLE", # Sierra Leone + "SOM", # Somalia + "ZAF", # South Africa + "SSD", # South Sudan + "TGO", # Togo + "UGA", # Uganda + "TZA", # United Republic of Tanzania + "ZMB", # Zambia + "ZWE", # Zimbabwe + ] +) diff --git a/src/laser_cholera/metapop/__init__.py b/src/laser_cholera/metapop/__init__.py index 2462daf..74ae018 100644 --- a/src/laser_cholera/metapop/__init__.py +++ b/src/laser_cholera/metapop/__init__.py @@ -1,29 +1,23 @@ -from .births import Births +from .census import Census from .environmental import Environmental -from .infected import Infected -from .lambda_ import Lambda_ +from .envtohuman import EnvToHuman +from .humantohuman import HumanToHuman +from .infectious import Infectious from .params import get_parameters -from .population import Population -from .psi import Psi from .recovered import Recovered from .scenario import scenario -from .susceptibles import Susceptibles -from .transmission import Transmission +from .susceptible import Susceptible from .vaccinated import Vaccinated -from .wash import Wash __all__ = [ - "Births", + "Census", + "EnvToHuman", "Environmental", - "Infected", - "Lambda_", - "Population", - "Psi", + "HumanToHuman", + "Infectious", "Recovered", - "Susceptibles", - "Transmission", + "Susceptible", "Vaccinated", - "Wash", "get_parameters", "scenario", ] diff --git a/src/laser_cholera/metapop/births.py b/src/laser_cholera/metapop/births.py deleted file mode 100644 index 44a4fe1..0000000 --- a/src/laser_cholera/metapop/births.py +++ /dev/null @@ -1,36 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.figure import Figure - - -class Births: - def __init__(self, model, verbose: bool = False) -> None: - self.model = model - assert ( - getattr(model.patches, "population", None) is not None - ), "The Births component requires the model patches to have a `population` attribute" - model.patches.add_scalar_property("daily_birth_rate", dtype=np.float32, default=np.float32(0.0)) - model.patches.daily_birth_rate[:] = model.scenario.birth_rate_per_day.values - model.patches.add_vector_property("births", length=model.params.nticks, dtype=np.uint32, default=0) - - return - - def __call__(self, model, tick: int) -> None: - births = model.prng.poisson(model.patches.daily_birth_rate * model.patches.population[tick, :]).astype(model.patches.births.dtype) - model.patches.births[tick, :] = births - model.agents.S[tick, :] += births - - return - - def plot(self, fig: Figure = None): - _fig = plt.figure(figsize=(12, 9), dpi=128) if fig is None else fig - - # Plot the births ... - ipatch = self.model.patches.population[:, 0].argmax() - plt.plot(self.model.patches.births[ipatch, :], label="Births") - plt.xlabel("Day") - plt.ylabel("Number of Births") - - yield - - return diff --git a/src/laser_cholera/metapop/census.py b/src/laser_cholera/metapop/census.py new file mode 100644 index 0000000..3384b66 --- /dev/null +++ b/src/laser_cholera/metapop/census.py @@ -0,0 +1,86 @@ +from datetime import datetime + +import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet +from matplotlib.figure import Figure + + +class Census: + def __init__(self, model, verbose: bool = False) -> None: + self.model = model + self.verbose = verbose + + assert hasattr(model, "agents"), "Census: model needs to have a 'agents' attribute." + model.agents.add_vector_property("N", length=model.params.nticks + 1, dtype=np.uint32, default=0) + assert hasattr(self.model, "params"), "Census: model needs to have a 'params' attribute." + assert "N_j_initial" in self.model.params, "Census: model params needs to have a 'N_j_initial' parameter." + model.agents.N[0] = model.params.N_j_initial + + return + + def check(self): + assert hasattr(self.model.agents, "S"), "Census: model agents needs to have a 'S' (susceptible) attribute." + assert hasattr(self.model.agents, "I"), "Census: model agents needs to have a 'I' (infectious) attribute." + assert hasattr(self.model.agents, "R"), "Census: model agents needs to have a 'R' (recovered) attribute." + assert hasattr(self.model.agents, "V"), "Census: model agents needs to have a 'v' (vaccinated) attribute." + + assert np.all( + self.model.agents.N[0] == self.model.agents.S[0] + self.model.agents.I[0] + self.model.agents.R[0] + self.model.agents.V[0] + ) + + return + + def __call__(self, model, tick: int) -> None: + Sprime = model.agents.S[tick + 1] + Iprime = model.agents.I[tick + 1] + Rprime = model.agents.R[tick + 1] + Vprime = model.agents.V[tick + 1] + Nprime = model.agents.N[tick + 1] + + Nprime[:] = Sprime + Vprime + Iprime + Rprime + + return + + @staticmethod + def test(): + class Model: + def __init__(self): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("S", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("I", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("R", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("V", length=8, dtype=np.uint32, default=0) + self.agents.S[0] = self.agents.S[1] = [250, 2_500, 25_000, 250_000] + self.agents.I[0] = self.agents.I[1] = [100, 1_000, 10_000, 100_000] + self.agents.R[0] = self.agents.R[1] = [500, 5_000, 50_000, 500_000] + self.agents.V[0] = self.agents.V[1] = [150, 1_500, 15_000, 150_000] + self.params = PropertySet({"N_j_initial": [1_000, 10_000, 100_000, 1_000_000], "nticks": 8}) + + component = Census(model := Model()) + component.check() + component(model, 0) + assert np.all(model.agents.N[0] == model.params.N_j_initial), "Initial populations didn't match." + S1 = model.agents.S[1] + I1 = model.agents.I[1] + R1 = model.agents.R[1] + V1 = model.agents.V[1] + assert np.all( + model.agents.N[1] == S1 + I1 + R1 + V1 + ), f"N[1] didn't match S[1] + I[1] + R[1] + V[1].\n\t{model.agents.N[1]}\n\t{S1+I1+R1+V1}" + + print("PASSED Census.test()") + + return + + def plot(self, fig: Figure = None): + _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig + + plt.title("Census") + plt.plot(self.model.agents.N[0:-1, :], color="black", label="Total Population") + plt.xlabel("Tick") + + yield + return diff --git a/src/laser_cholera/metapop/data/default_parameters.json b/src/laser_cholera/metapop/data/default_parameters.json new file mode 100644 index 0000000..a1b40fd --- /dev/null +++ b/src/laser_cholera/metapop/data/default_parameters.json @@ -0,0 +1,196 @@ +{ + "date_start": "2023-01-01", + "date_stop": "2024-12-17", + "location_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40], + "location_name": ["AGO", "BDI", "BEN", "BFA", "BWA", "CAF", "CIV", "CMR", "COD", "COG", "ERI", "ETH", "GAB", "GHA", "GIN", "GMB", "GNB", "GNQ", "KEN", "LBR", "MLI", "MOZ", "MRT", "MWI", "NAM", "NER", "NGA", "RWA", "SEN", "SLE", "SOM", "SSD", "SWZ", "TCD", "TGO", "TZA", "UGA", "ZAF", "ZMB", "ZWE"], + "N_j_initial": [36186956, 13503998, 13934166, 22765636, 2459937, 5064592, 30783520, 27997833, 104063312, 6108142, 3438999, 127028360, 2457715, 33467371, 14229395, 2666786, 2129290, 1825480, 54793511, 5432670, 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0.2021692 + - 0.2046457 + - 0.2074371 + - 0.210574 + - 0.2142204 + - 0.2184987 + - 0.2233788 + - 0.2288301 + - 0.2348204 + - 0.2413151 + - 0.2482762 + - 0.2556622 + - 0.2634267 + - 0.2715186 + - 0.2798818 + - 0.2884548 + - 0.2971707 + - 0.3059581 + - 0.3147404 + - 0.3234372 + - 0.3319645 + - 0.3402352 + - 0.3481602 + - 0.3560493 + - 0.3642517 + - 0.3727349 + - 0.3814643 + - 0.3904034 + - 0.3995137 + - 0.4087552 + - 0.418086 + - 0.4274633 + - 0.4368429 + - 0.4461801 + - 0.4554296 + - 0.4645459 + - 0.4734837 + - 0.482198 + - 0.4906444 + - 0.4987793 + - 0.5065601 + - 0.5139452 + - 0.5208942 + - 0.527737 + - 0.5347927 + - 0.5420053 + - 0.5493189 + - 0.5566777 + - 0.5640265 + - 0.5713105 + - 0.5784762 + - 0.585471 + - 0.5922436 + - 0.5987441 + - 0.604924 + - 0.6107362 + - 0.616135 + - 0.6210758 + - 0.6255151 + - 0.6294099 + - 0.632718 + - 0.6353968 + - 0.6374036 + - 0.6386394 + - 0.6390789 + - 0.6387875 + - 0.6378288 + - 0.6362648 + - 0.6341566 + - 0.631565 + - 0.6285508 + - 0.6251753 + - 0.6215002 + - 0.6175882 + - 0.6135031 + - 0.6093096 + - 0.6050738 + - 0.6008625 + - 0.5967436 + - 0.5927859 + - 0.5890588 + - 0.5856322 + - 0.5825763 + - 0.5794527 + - 0.5758193 + - 0.5717416 + - 0.5672857 + - 0.5625182 + - 0.5575068 + - 0.55232 + - 0.5470272 + - 0.5416983 + - 0.5364041 + - 0.5312155 + - 0.5262041 + - 0.5214412 + - 0.5169982 + - 0.5129466 + - 0.5093573 + - 0.506301 + - 0.5038484 + - 0.5020697 + - 0.5007711 + - 0.4997091 + - 0.4988785 + - 0.4982745 + - 0.497892 + - 0.497726 + - 0.4977716 + - 0.4980238 + - 0.4984775 + - 0.4991279 + - 0.4999698 + - 0.5009984 + - 0.5022086 + - 0.5035954 + - 0.5051538 + - 0.5068788 + - 0.5087652 + - 0.510808 + - 0.513002 + - 0.5153421 + - 0.5182463 + - 0.5220552 + - 0.526647 + - 0.5318992 + - 0.5376889 + - 0.5438927 + - 0.5503874 + - 0.5570501 + - 0.5637588 + - 0.5703929 + - 0.5768333 +zeta: 0.5 +kappa: 100000.0 +delta_min: 0.3333333 +delta_max: 0.0111111 diff --git a/src/laser_cholera/metapop/environmental.py b/src/laser_cholera/metapop/environmental.py index 664d016..bb0a86e 100644 --- a/src/laser_cholera/metapop/environmental.py +++ b/src/laser_cholera/metapop/environmental.py @@ -1,4 +1,9 @@ +from datetime import datetime + import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet from matplotlib.figure import Figure @@ -7,29 +12,117 @@ def __init__(self, model, verbose: bool = False) -> None: self.model = model self.verbose = verbose - assert hasattr(model, "agents"), "Environmental: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "I"), "Environmental: model agents needs to have a 'I' (infectious) attribute." + assert hasattr(model, "patches"), "Environmental: model needs to have a 'patches' attribute." + + model.patches.add_vector_property("W", length=model.params.nticks + 1, dtype=np.float32, default=0.0) + model.patches.add_vector_property("delta", length=365, dtype=np.float32, default=0.0) + assert hasattr(model, "params"), "Environmental: model needs to have a 'params' attribute." - assert hasattr(model.params, "delta"), "Environmental: model paraams needs to have a 'delta' (suitability decay) parameter." assert hasattr( - model.params, "zeta" + model.params, "psi_jt" + ), "Environmental: model params needs to have a 'psi_jt' (environmental contamination rate) parameter." + assert hasattr(model.params, "delta_max"), "Environmental: model params needs to have a 'delta_max' (suitability decay) parameter." + assert hasattr(model.params, "delta_min"), "Environmental: model params needs to have a 'delta_min' (suitability decay) parameter." + + one_year = model.params.psi_jt[:, 0:365] + model.patches.delta[:, :] = (model.params.delta_min + one_year * (model.params.delta_max - model.params.delta_min)).T + + return + + def check(self): + assert hasattr(self.model, "agents"), "Environmental: model needs to have a 'agents' attribute." + assert hasattr(self.model.agents, "I"), "Environmental: model agents needs to have a 'I' (infectious) attribute." + assert hasattr( + self.model.params, "zeta" ), "Environmental: model params needs to have a 'zeta' (environmental transmission rate) parameter." - assert hasattr(model, "patches"), "Environmental: model needs to have a 'patches' attribute." - assert hasattr(model.patches, "W"), "Environmental: model patches needs to have a 'W' (environmental) attribute." return def __call__(self, model, tick: int) -> None: + I = model.agents.I[tick] # noqa: E741 W = model.patches.W[tick] Wprime = model.patches.W[tick + 1] - I = model.agents.I[tick] # noqa: E741 Wprime[:] = W - Wprime += model.params.zeta * I - Wprime -= model.params.delta * W + environmental_decay = (model.patches.delta[tick] * W).astype(Wprime.dtype) + Wprime -= environmental_decay + human_shedding = (model.params.zeta * I).astype(Wprime.dtype) + Wprime += human_shedding return + @staticmethod + def test(): + class Model: + def __init__(self, psi_jt, delta_max, delta_min, zeta): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("I", length=8, dtype=np.uint32, default=0) + self.agents.I[0] = [100, 1_000, 10_000, 100_000] + self.patches = LaserFrame(4) + self.params = PropertySet( + { + "psi_jt": psi_jt, + "delta_max": delta_max, + "delta_min": delta_min, + "zeta": zeta, + "nticks": 8, + } + ) + + psi_jt = np.zeros((4, 365)) + psi_jt[0, 0:8] = [0.5106762, 0.5108125, 0.5114274, 0.5124948, 0.5139883, 0.5158816, 0.5181482, 0.5207615] + psi_jt[1, 0:8] = [0.0667797, 0.0667555, 0.0667324, 0.0667131, 0.0667009, 0.0666988, 0.0667097, 0.0667367] + psi_jt[2, 0:8] = [0.6080956, 0.6040341, 0.5999001, 0.5956825, 0.5913696, 0.5869501, 0.5824124, 0.5777447] + psi_jt[3, 0:8] = [0.6868838, 0.6866003, 0.6863385, 0.6860761, 0.6857905, 0.6854592, 0.6850596, 0.6845689] + psi_jt[0, 8:] = psi_jt[0, 0:8].mean() + psi_jt[1, 8:] = psi_jt[1, 0:8].mean() + psi_jt[2, 8:] = psi_jt[2, 0:8].mean() + psi_jt[3, 8:] = psi_jt[3, 0:8].mean() + + component = Environmental(baseline := Model(psi_jt=psi_jt, delta_max=0.0111111, delta_min=0.3333333, zeta=0.5)) + component.check() + component(baseline, 0) + + component = Environmental(no_decay := Model(psi_jt=baseline.params.psi_jt, delta_max=0.0, delta_min=0.0, zeta=baseline.params.zeta)) + component.check() + component(no_decay, 0) + assert np.all( + no_decay.patches.W[1] > baseline.patches.W[0] + ), f"Expected more environmental contagion with no decay.\n\t{no_decay.patches.W[1]}\n\t{baseline.patches.W[0]}" + + component = Environmental( + increased_shedding := Model( + psi_jt=baseline.params.psi_jt, + delta_max=baseline.params.delta_max, + delta_min=baseline.params.delta_min, + zeta=baseline.params.zeta * 8.0, + ) + ) + component.check() + component(increased_shedding, 0) + assert np.all( + increased_shedding.patches.W[1] > baseline.patches.W[0] + ), f"Expected more environmental contagion with increased shedding.\n\t{increased_shedding.patches.W[1]}\n\t{baseline.patches.W[0]}" + + component = Environmental( + more_infected := Model( + psi_jt=baseline.params.psi_jt, + delta_max=baseline.params.delta_max, + delta_min=baseline.params.delta_min, + zeta=baseline.params.zeta, + ) + ) + more_infected.agents.I[0] *= 2 + component.check() + component(more_infected, 0) + assert np.all( + more_infected.patches.W[1] > baseline.patches.W[0] + ), f"Expected more environmental contagion with more infected agents.\n\t{more_infected.patches.W[1]}\n\t{baseline.patches.W[0]}" + + print("PASSED Environmental.test()") + return + def plot(self, fig: Figure = None): _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig @@ -40,3 +133,8 @@ def plot(self, fig: Figure = None): yield return + + +if __name__ == "__main__": + Environmental.test() + # plt.show() diff --git a/src/laser_cholera/metapop/envtohuman.py b/src/laser_cholera/metapop/envtohuman.py new file mode 100644 index 0000000..40c40af --- /dev/null +++ b/src/laser_cholera/metapop/envtohuman.py @@ -0,0 +1,326 @@ +"""Environmental transmission rate.""" + +from datetime import datetime + +import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet +from matplotlib.figure import Figure + + +class EnvToHuman: + def __init__(self, model, verbose: bool = False) -> None: + self.model = model + self.verbose = verbose + + assert hasattr(model, "agents"), "EnvToHuman: model needs to have a 'agents' attribute." + model.patches.add_vector_property("PSI", length=model.params.nticks + 1, dtype=np.float32, default=np.float32(0.0)) + + assert hasattr(model, "params"), "EnvToHuman: model needs to have a 'params' attribute." + assert hasattr( + model.params, "psi_jt" + ), "EnvToHuman: model params needs to have a 'psi_jt' (environmental contamination rate) parameter." + + model.patches.add_vector_property("beta_env", length=365, dtype=np.float32, default=0.0) + assert model.patches.beta_env.shape[1] == model.params.psi_jt.shape[0] + assert model.patches.beta_env.shape[1] == model.params.beta_j0_env.shape[0] + dimension = model.patches.beta_env.shape[1] + one_year = model.params.psi_jt[:, 0:365] + psi_bar = one_year.mean(axis=1).reshape((dimension, -1)) + model.patches.beta_env[:, :] = (model.params.beta_j0_env.reshape((dimension, -1)) * (1 + (one_year - psi_bar) / psi_bar)).T + + return + + def check(self): + assert hasattr(self.model.agents, "S"), "EnvToHuman: model agents needs to have a 'S' (susceptible) attribute." + assert hasattr(self.model.agents, "I"), "EnvToHuman: model agents needs to have a 'I' (infected) attribute." + assert hasattr(self.model, "patches"), "EnvToHuman: model needs to have a 'patches' attribute." + assert hasattr(self.model.patches, "W"), "EnvToHuman: model patches needs to have a 'W' (environmental) attribute." + assert hasattr( + self.model.params, "tau_i" + ), "HumanToHuman: model.params needs to have a 'tau_i' (emmigration probability) parameter." + assert hasattr( + self.model.params, "theta_j" + ), "EnvToHuman: model params needs to have a 'theta_j' (fraction of population with WASH) attribute." + assert hasattr( + self.model.params, "kappa" + ), "EnvToHuman: model params needs to have a 'kappa' (environmental transmission rate) parameter." + + return + + def __call__(self, model, tick: int) -> None: + Pprime = model.patches.PSI[tick + 1] + S = model.agents.S[tick] + W = model.patches.W[tick] + Sprime = model.agents.S[tick + 1] + Iprime = model.agents.I[tick + 1] + + Pprime[:] = model.patches.beta_env[tick] * (S * (1 - model.params.tau_i)) * (1 - model.params.theta_j) * W + Pprime /= model.params.kappa + W + + newly_infected = model.prng.poisson(Pprime).astype(Sprime.dtype) + Sprime -= newly_infected + Iprime += newly_infected + + return + + @staticmethod + def test(): + class Model: + def __init__(self, psi_jt, beta_j0_env, tau_i, theta_j, kappa, wfactor=1.0, beta_factor=1.0): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + + self.agents = LaserFrame(4) + self.agents.add_vector_property("S", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("I", length=8, dtype=np.uint32, default=0) + self.agents.S[0] = self.agents.S[1] = [250, 2_500, 25_000, 250_000] # 25% + self.agents.I[0] = self.agents.I[1] = [100, 1_000, 10_000, 100_000] # 10% + + self.patches = LaserFrame(4) + self.patches.add_vector_property("W", length=8, dtype=np.float32, default=0.0) + self.patches.W[0, :] = 500_000.0 / 3.0 + self.patches.W[0] *= wfactor + beta_j0_env *= beta_factor + + self.params = PropertySet( + { + "psi_jt": psi_jt, + "beta_j0_env": beta_j0_env, + "tau_i": tau_i, + "theta_j": theta_j, + "kappa": kappa, + "nticks": 8, + } + ) + + psi_jt = np.zeros((4, 365)) + psi_jt[0, 0:8] = [0.5106762, 0.5108125, 0.5114274, 0.5124948, 0.5139883, 0.5158816, 0.5181482, 0.5207615] + psi_jt[1, 0:8] = [0.0667797, 0.0667555, 0.0667324, 0.0667131, 0.0667009, 0.0666988, 0.0667097, 0.0667367] + psi_jt[2, 0:8] = [0.6080956, 0.6040341, 0.5999001, 0.5956825, 0.5913696, 0.5869501, 0.5824124, 0.5777447] + psi_jt[3, 0:8] = [0.6868838, 0.6866003, 0.6863385, 0.6860761, 0.6857905, 0.6854592, 0.6850596, 0.6845689] + psi_jt[0, 8:] = psi_jt[0, 0:8].mean() + psi_jt[1, 8:] = psi_jt[1, 0:8].mean() + psi_jt[2, 8:] = psi_jt[2, 0:8].mean() + psi_jt[3, 8:] = psi_jt[3, 0:8].mean() + + component = EnvToHuman( + baseline := Model( + psi_jt=psi_jt, + beta_j0_env=np.array([0.4, 0.4, 0.4, 0.4]), + tau_i=np.array([1.2521719e-03, 4.1985715e-04, 2.0205112e-02, 9.2022895e-03]), # 1000x some actual values + theta_j=np.array([0.5604815, 0.7254393, 0.6573358, 0.6017253]), + kappa=500_000, + ) + ) + component.check() + component(baseline, 0) + assert np.all( + baseline.agents.S[1] < baseline.agents.S[0] + ), f"Some susceptible populations didn't decrease with environmental transmission.\n\t{baseline.agents.S[0]}\n\t{baseline.agents.S[1]}" + assert np.all( + baseline.agents.I[1] > baseline.agents.I[0] + ), f"Some infected populations didn't increase with environmental transmission.\n\t{baseline.agents.I[0]}\n\t{baseline.agents.I[1]}" + + component = EnvToHuman( + increased_tau := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i * (0.95 / baseline.params.tau_i), + theta_j=baseline.params.theta_j, + kappa=baseline.params.kappa, + ) + ) + component.check() + component(increased_tau, 0) + assert np.all( + increased_tau.agents.S[1] > baseline.agents.S[1] + ), f"Expected larger remaining susceptible populations with higher tau_i (migrating fraction).\n\t{baseline.agents.S[1]=}\n\t{increased_tau.agents.S[1]=}" + assert np.all( + increased_tau.agents.I[1] < baseline.agents.I[1] + ), f"Expected smaller infected populations with higher tau_i (migrating fraction).\n\t{baseline.agents.I[1]=}\n\t{increased_tau.agents.I[1]=}" + + component = EnvToHuman( + zero_wash := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j * 0, + kappa=baseline.params.kappa, + ) + ) + component.check() + component(zero_wash, 0) + assert np.all( + zero_wash.agents.S[1] < baseline.agents.S[1] + ), f"Expected smaller remaining susceptible populations with lower theta_j (WASH fraction).\n\t{baseline.agents.S[1]=}\n\t{zero_wash.agents.S[1]=}" + assert np.all( + zero_wash.agents.I[1] > baseline.agents.I[1] + ), f"Expected larger infected populations with lower theta_j (WASH fraction).\n\t{baseline.agents.I[1]=}\n\t{zero_wash.agents.I[1]=}" + + component = EnvToHuman( + high_wash := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=np.array([0.9, 0.9, 0.9, 0.9]), + kappa=baseline.params.kappa, + ) + ) + component.check() + component(high_wash, 0) + assert np.all( + high_wash.agents.S[1] > baseline.agents.S[1] + ), f"Expected larger remaining susceptible populations with higher theta_j (WASH fraction).\n\t{baseline.agents.S[1]=}\n\t{high_wash.agents.S[1]=}" + assert np.all( + high_wash.agents.I[1] < baseline.agents.I[1] + ), f"Expected smaller infected populations with higher theta_j (WASH fraction).\n\t{baseline.agents.I[1]=}\n\t{high_wash.agents.I[1]=}" + + component = EnvToHuman( + perfect_wash := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=np.array([1.0, 1.0, 1.0, 1.0]), + kappa=baseline.params.kappa, + ) + ) + component.check() + component(perfect_wash, 0) + assert np.all( + perfect_wash.agents.S[1] == perfect_wash.agents.S[0] + ), f"Expected no environmental transmission with theta_j (WASH fraction) = 1.0.\n\t{perfect_wash.agents.S[0]=}\n\t{perfect_wash.agents.S[1]=}" + assert np.all( + perfect_wash.agents.I[1] == perfect_wash.agents.I[0] + ), f"Expected no environmental transmission with theta_j (WASH fraction) = 1.0.\n\t{perfect_wash.agents.I[0]=}\n\t{perfect_wash.agents.I[1]=}" + + component = EnvToHuman( + smaller_kappa := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j, + kappa=baseline.params.kappa / 8.0, + ) + ) + component.check() + component(smaller_kappa, 0) + assert np.all( + smaller_kappa.agents.S[1] < baseline.agents.S[1] + ), f"Expected smaller remaining susceptible populations with lower kappa (50% probability concentration level).\n\t{baseline.agents.S[1]=}\n\t{smaller_kappa.agents.S[1]=}" + assert np.all( + smaller_kappa.agents.I[1] > baseline.agents.I[1] + ), f"Expected larger infected populations with lower kappa (50% probability concentration level).\n\t{baseline.agents.I[1]=}\n\t{smaller_kappa.agents.I[1]=}" + + component = EnvToHuman( + larger_kappa := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j, + kappa=8.0 * baseline.params.kappa, + ) + ) + component.check() + component(larger_kappa, 0) + assert np.all( + larger_kappa.agents.S[1] > baseline.agents.S[1] + ), f"Expected larger remaining susceptible populations with larger kappa (50% probability concentration level).\n\t{baseline.agents.S[1]=}\n\t{larger_kappa.agents.S[1]=}" + assert np.all( + larger_kappa.agents.I[1] < baseline.agents.I[1] + ), f"Expected smaller infected populations with larger kappa (50% probability concentration level).\n\t{baseline.agents.I[1]=}\n\t{larger_kappa.agents.I[1]=}" + + component = EnvToHuman( + smaller_w := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j, + kappa=baseline.params.kappa, + wfactor=0.125, + ) + ) + component.check() + component(smaller_w, 0) + assert np.all( + smaller_w.agents.S[1] > baseline.agents.S[1] + ), f"Expected larger remaining susceptible populations with smaller W (environmental contagion).\n\t{baseline.agents.S[1]=}\n\t{smaller_w.agents.S[1]=}" + assert np.all( + smaller_w.agents.I[1] < baseline.agents.I[1] + ), f"Expected smaller infected populations with smaller W (environmental contagion).\n\t{baseline.agents.I[1]=}\n\t{smaller_w.agents.I[1]=}" + + component = EnvToHuman( + larger_w := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j, + kappa=baseline.params.kappa, + wfactor=8.0, + ) + ) + component.check() + component(larger_w, 0) + assert np.all( + larger_w.agents.S[1] < baseline.agents.S[1] + ), f"Expected smaller remaining susceptible populations with larger W (environmental contagion).\n\t{baseline.agents.S[1]=}\n\t{larger_w.agents.S[1]=}" + assert np.all( + larger_w.agents.I[1] > baseline.agents.I[1] + ), f"Expected larger infected populations with larger W (environmental contagion).\n\t{baseline.agents.I[1]=}\n\t{larger_w.agents.I[1]=}" + + component = EnvToHuman( + smaller_beta := Model( + psi_jt=baseline.params.psi_jt, + beta_j0_env=baseline.params.beta_j0_env, + tau_i=baseline.params.tau_i, + theta_j=baseline.params.theta_j, + kappa=baseline.params.kappa, + beta_factor=0.03125, + ) + ) + component.check() + component(smaller_beta, 0) + assert np.all( + smaller_beta.agents.S[1] > baseline.agents.S[1] + ), f"Expected larger remaining susceptible populations with smaller beta_env (seasonal factor).\n\t{baseline.agents.S[1]=}\n\t{smaller_beta.agents.S[1]=}" + assert np.all( + smaller_beta.agents.I[1] < baseline.agents.I[1] + ), f"Expected smaller infected populations with smaller beta_env (seasonal factor).\n\t{baseline.agents.I[1]=}\n\t{smaller_beta.agents.I[1]=}" + + # component = EnvToHuman( + # larger_beta := Model( + # psi_jt=baseline.params.psi_jt, + # beta_j0_env=baseline.params.beta_j0_env, + # tau_i=baseline.params.tau_i, + # theta_j=baseline.params.theta_j, + # kappa=baseline.params.kappa, + # beta_factor=16.0, + # ) + # ) + # component.check() + # component(larger_beta, 0) + # assert np.all( + # larger_beta.agents.S[1] < baseline.agents.S[1] + # ), f"Expected smaller remaining susceptible populations with larger beta_env (seasonal factor).\n\t{baseline.agents.S[1]=}\n\t{larger_beta.agents.S[1]=}" + # assert np.all( + # larger_beta.agents.I[1] > baseline.agents.I[1] + # ), f"Expected larger infected populations with larger beta_env (seasonal factor).\n\t{baseline.agents.I[1]=}\n\t{larger_beta.agents.I[1]=}" + + print("PASSED EnvToHuman.test()") + return + + def plot(self, fig: Figure = None): + _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig + + ipatch = self.model.patches.initpop.argmax() + plt.title(f"Environmental Transmission Rate in Patch {ipatch}") + plt.plot(self.model.patches.PSI[:, ipatch], color="orange", label="Environmental Transmission Rate") + plt.xlabel("Tick") + + yield + return + + +if __name__ == "__main__": + EnvToHuman.test() + # plt.show() diff --git a/src/laser_cholera/metapop/humantohuman.py b/src/laser_cholera/metapop/humantohuman.py new file mode 100644 index 0000000..bf29113 --- /dev/null +++ b/src/laser_cholera/metapop/humantohuman.py @@ -0,0 +1,168 @@ +"""Human-to-human transmission rate.""" + +from datetime import datetime + +import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet +from matplotlib.figure import Figure + + +class HumanToHuman: + def __init__(self, model, verbose: bool = False) -> None: + self.model = model + self.verbose = verbose + + assert hasattr(model, "patches"), "HumanToHuman: model needs to have a 'patches' attribute." + model.patches.add_vector_property("LAMBDA", length=model.params.nticks + 1, dtype=np.float32, default=0.0) + + return + + def check(self): + assert hasattr(self.model, "agents"), "HumanToHuman: model needs to have a 'agents' attribute." + assert hasattr(self.model.agents, "S"), "HumanToHuman: model agents needs to have a 'S' (susceptible) attribute." + assert hasattr(self.model.agents, "I"), "HumanToHuman: model agents needs to have a 'I' (infectious) attribute." + assert hasattr(self.model.agents, "N"), "HumanToHuman: model agents needs to have a 'N' (current agents) attribute." + assert hasattr(self.model, "params"), "HumanToHuman: model needs to have a 'params' attribute." + assert "alpha" in self.model.params, "HumanToHuman: model params needs to have an 'alpha' parameter." + assert hasattr( + self.model.params, "tau_i" + ), "HumanToHuman: model.params needs to have a 'tau_i' (emmigration probability) parameter." + assert hasattr( + self.model.params, "pi_ij" + ), "HumanToHuman: model.params needs to have a 'pi_ij' (connectivity probability) parameter." + assert hasattr(self.model.params, "alpha"), "HumanToHuman: model.params needs to have a 'alpha' (population mixing) parameter." + assert hasattr( + self.model.params, "beta_j0_hum" + ), "HumanToHuman: model.params needs to have a 'beta_j0_hum' (baseline transmission rate) parameter." + assert hasattr( + self.model.params, "beta_j_seasonality" + ), "HumanToHuman: model.params needs to have a 'beta_j_seasonality' (seasonal transmission rate) parameter." + + return + + def __call__(self, model, tick: int) -> None: + r""" + Calculate the current human-to-human transmission rate per patch. + + $\Lambda_{j,t+1} = \frac {\beta^{hum}_{jt}((S_{jt}(1 - \tau_j))(I_{jt}(1 - \tau_j) + \sum_{\forall i \neq j (\pi_{ij} \tau_j I_{it})}))^{\alpha}} {N_{jt}}$ + """ + + Lprime = model.patches.LAMBDA[tick + 1] + I = model.agents.I[tick] # noqa: E741 + S = model.agents.S[tick] + Sprime = model.agents.S[tick + 1] + Iprime = model.agents.I[tick + 1] + N = model.agents.N[tick] + + # # "lambda" is a reserved keyword in Python + # Lprime[:] = (I * (1 - model.params.tau_i)).astype(Lprime.dtype) + # # TODO - sum over axis=0 or axis=1? + # Lprime += (model.params.pi_ij * (model.params.tau_i * I)).sum(axis=0).astype(Lprime.dtype) + # Lprime *= (S * (1 - model.params.tau_i)).astype(Lprime.dtype) + # np.power(Lprime, model.params.alpha, out=Lprime) + # # TODO - look up seasonality correctly based on tick/calendar date + # Lprime *= (model.params.beta_j0_hum + model.params.beta_j_seasonality[:, tick % 365]).astype(Lprime.dtype) + # Lprime /= N + + t1 = (I * (1 - model.params.tau_i)).astype(Lprime.dtype) + lp1 = t1 + # TODO - sum over axis=0 or axis=1? + t2 = (model.params.pi_ij * (model.params.tau_i * I)).sum(axis=1).astype(Lprime.dtype) + lp2 = lp1 + t2 + t3 = (S * (1 - model.params.tau_i)).astype(Lprime.dtype) + lp3 = lp2 * t3 + t4 = np.power(lp3, model.params.alpha).astype(Lprime.dtype) + lp4 = t4 + # TODO - look up seasonality correctly based on tick/calendar date + t5 = (model.params.beta_j0_hum + model.params.beta_j_seasonality[:, tick % 365]).astype(Lprime.dtype) + lp5 = lp4 * t5 + lp6 = lp5 / N + Lprime[:] = lp6 + + # TODO - rate (Poisson) or probability (binomial)? + newly_infected = model.prng.poisson(Lprime).astype(Sprime.dtype) + Sprime -= newly_infected + Iprime += newly_infected + + return + + @staticmethod + def test(): + class Model: + def __init__( + self, alpha: float = 0.0, tau_i: float = 0.0, pi_ij: float = 0.0, beta_j0_hum: float = 0.0, beta_j_seasonality: float = 0.0 + ): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("S", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("I", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("N", length=8, dtype=np.uint32, default=0) + self.agents.S[0] = self.agents.S[1] = [250, 2_500, 25_000, 250_000] # 25% + self.agents.I[0] = self.agents.I[1] = [100, 1_000, 10_000, 100_000] # 10% + self.agents.N[0] = self.agents.N[1] = [1_000, 10_000, 100_000, 1_000_000] + self.params = PropertySet( + { + "alpha": alpha, + "tau_i": tau_i, + "pi_ij": pi_ij, + "beta_j0_hum": beta_j0_hum, + "beta_j_seasonality": beta_j_seasonality, + "nticks": 8, + } + ) + + component = HumanToHuman( + model := Model( + alpha=0.95, + tau_i=np.array([1.2521719e-03, 4.1985715e-04, 2.0205112e-02, 9.2022895e-03]), # 1000x some actual values + pi_ij=np.array( + [ + [0.0000, 0.2625, 0.1326, 0.1489], # 10x some actual values + [0.2066, 0.0000, 0.0473, 0.0538], + [0.1093, 0.0495, 0.0000, 0.7596], + [0.1190, 0.0546, 0.7362, 0.0000], + ] + ), + beta_j0_hum=np.array([1.0, 1.0, 1.0, 1.0]), + beta_j_seasonality=np.array( + [ + [-0.0158, -0.0072, 0.0014, 0.0101, 0.0189, 0.0278, 0.0367, 0.0458], + [-0.8522, -0.8663, -0.8799, -0.8928, -0.9053, -0.9172, -0.9286, -0.9395], + [-0.0254, -0.0161, -0.0071, 0.0018, 0.0105, 0.0190, 0.0273, 0.0354], + [-0.0254, -0.0161, -0.0071, 0.0018, 0.0105, 0.0190, 0.0273, 0.0354], + ] + ), + ) + ) + component.check() + component(model, 0) + assert np.all( + model.agents.S[1] < model.agents.S[0] + ), f"Some susceptible populations didn't decline based on human-human transmission.\n\t{model.agents.S[0]}\n\t{model.agents.S[1]}" + assert np.all( + model.agents.I[1] > model.agents.I[0] + ), f"Some infected populations didn't increase based on human-human transmission.\n\t{model.agents.I[0]}\n\t{model.agents.I[1]}" + assert np.all( + model.agents.S[0] - model.agents.S[1] == model.agents.I[1] - model.agents.I[0] + ), f"S and I population changes don't match.\n\t{model.agents.S[0]=}\n\t{model.agents.S[1]=}\n\t{model.agents.I[0]=}\n\t{model.agents.I[1]=}" + + print("PASSED HumanToHuman.test()") + return + + def plot(self, fig: Figure = None): + _fig = plt.figure(figsize=(12, 9), dpi=128) if fig is None else fig + + ipatch = self.model.patches.initpop.argmax() + plt.title(f"Patch {ipatch} - Human-to-Human Transmission Rate") + plt.plot(self.model.patches.LAMBDA[:, ipatch], color="blue", label="Human-to-Human Transmission Rate") + plt.xlabel("Tick") + + yield + return + + +if __name__ == "__main__": + HumanToHuman.test() + # plt.show() diff --git a/src/laser_cholera/metapop/infected.py b/src/laser_cholera/metapop/infected.py deleted file mode 100644 index 116ae76..0000000 --- a/src/laser_cholera/metapop/infected.py +++ /dev/null @@ -1,58 +0,0 @@ -import matplotlib.pyplot as plt -from matplotlib.figure import Figure - - -class Infected: - def __init__(self, model, verbose: bool = False) -> None: - self.model = model - self.verbose = verbose - - assert hasattr(model, "agents"), "Infected: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "I"), "Infected: model agents needs to have a 'I' (infectious) attribute." - assert hasattr(model, "patches"), "Infected: model needs to have a 'patches' attribute." - assert hasattr( - model.patches, "LAMBDA" - ), "Infected: model patches needs to have a 'LAMBDA' (human-to-human transmission rate) attribute." - assert hasattr(model.patches, "PSI"), "Infected: model patches needs to have a 'PSI' (environmental transmission rate) attribute." - assert hasattr(model.patches, "mortrate"), "Infected: model patches needs to have a 'mortrate' parameter." - assert hasattr(model, "params"), "Infected: model needs to have a 'params' attribute." - assert hasattr(model.params, "gamma"), "Infected: model params needs to have a 'gamma' (recovery rate) parameter." - assert hasattr(model.params, "mu"), "Infected: model params needs to have a 'mu' (environmental transmission rate) parameter." - assert hasattr( - model.params, "sigma" - ), "Infected: model params needs to have a 'sigma' (human-to-human transmission rate) parameter." - - return - - def __call__(self, model, tick: int) -> None: - I = model.agents.I[tick] # noqa: E741 - Iprime = model.agents.I[tick + 1] - LAMBDA = model.patches.LAMBDA[tick + 1] - PSI = model.patches.PSI[tick + 1] - Sprime = model.agents.S[tick + 1] - - Iprime[:] = I - # human-to-human transmission - new_infs = LAMBDA.astype(Iprime.dtype) # TODO - is this a rate or a count? - # environmental-to-human transmission - new_infs += PSI.astype(Iprime.dtype) # TODO - is this a rate or a count? - Iprime += new_infs - Sprime -= new_infs - # recovered - Iprime -= model.prng.binomial(I, model.params.gamma).astype(Iprime.dtype) - # disease deaths - Iprime -= model.prng.binomial(I, model.params.mu * model.params.sigma).astype(Iprime.dtype) - # non-disease mortality - Iprime -= model.prng.binomial(I, model.patches.mortrate).astype(Iprime.dtype) - - return - - def plot(self, fig: Figure = None): - _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig - ipatch = self.model.patches.initpop.argmax() - plt.title(f"Infected in Patch {ipatch}") - plt.plot(self.model.agents.I[:, ipatch], color="red", label="Infected") - plt.xlabel("Tick") - - yield - return diff --git a/src/laser_cholera/metapop/infectious.py b/src/laser_cholera/metapop/infectious.py new file mode 100644 index 0000000..b1935c7 --- /dev/null +++ b/src/laser_cholera/metapop/infectious.py @@ -0,0 +1,89 @@ +from datetime import datetime + +import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet +from matplotlib.figure import Figure + + +class Infectious: + def __init__(self, model, verbose: bool = False) -> None: + self.model = model + self.verbose = verbose + + assert hasattr(model, "agents"), "Infectious: model needs to have a 'agents' attribute." + model.agents.add_vector_property("I", length=model.params.nticks + 1, dtype=np.uint32, default=0) + assert hasattr(self.model, "params"), "Infectious: model needs to have a 'params' attribute." + assert hasattr( + self.model.params, "I_j_initial" + ), "Infectious: model params needs to have a 'I_j_initial' (initial infectious population) parameter." + model.agents.I[0] = model.params.I_j_initial + + return + + def check(self): + assert hasattr(self.model.params, "d_j"), "Infectious: model params needs to have a 'd_j' (mortality rate) parameter." + assert hasattr(self.model.params, "mu"), "Infectious: model params needs to have a 'mu' (disease mortality rate) parameter." + assert hasattr(self.model.params, "sigma"), "Infectious: model params needs to have a 'sigma' (symptomatic fraction) parameter." + + return + + def __call__(self, model, tick: int) -> None: + I = model.agents.I[tick] # noqa: E741 + Iprime = model.agents.I[tick + 1] + + # natural mortality + # TODO - rate or probability? + Imort = model.prng.poisson(model.params.d_j * I).astype(Iprime.dtype) + Iprime[:] = I - Imort + + # TODO - rate or probability? + disease_deaths = model.prng.binomial(I, model.params.mu * model.params.sigma).astype(Iprime.dtype) + Iprime -= disease_deaths + + # human-to-human infection in humantohuman.py + # environmental infection in envtohuman.py + # recovery from infection in recovered.py + + return + + @staticmethod + def test(): + class Model: + def __init__(self, d_j: float = 0.0, mu: float = 0.0, sigma: float = 0.0): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.params = PropertySet( + {"d_j": d_j, "mu": mu, "sigma": sigma, "I_j_initial": [1_000, 10_000, 100_000, 1_000_000], "nticks": 8} + ) + + component = Infectious(model := Model(d_j=1.0 / 80.0)) + component.check() + component(model, 0) + assert np.all(model.agents.I[0] == model.params.I_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.I[1] < model.agents.I[0] + ), f"Some populations didn't shrink with natural mortality.\n\t{model.agents.I[0]}\n\t{model.agents.I[1]}" + + component = Infectious(model := Model(mu=0.015, sigma=0.24)) + component.check() + component(model, 0) + assert np.all(model.agents.I[0] == model.params.I_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.I[1] < model.agents.I[0] + ), f"Some populations didn't shrink with disease mortality.\n\t{model.agents.I[0]}\n\t{model.agents.I[1]}" + + print("PASSED Infectious.test()") + + return + + def plot(self, fig: Figure = None): + _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig + ipatch = self.model.patches.initpop.argmax() + plt.title(f"Infected in Patch {ipatch}") + plt.plot(self.model.agents.I[:, ipatch], color="red", label="Infected") + plt.xlabel("Tick") + + yield + return diff --git a/src/laser_cholera/metapop/lambda_.py b/src/laser_cholera/metapop/lambda_.py deleted file mode 100644 index 1a0f46f..0000000 --- a/src/laser_cholera/metapop/lambda_.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Human-to-human transmission rate.""" - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -from matplotlib.figure import Figure - - -class Lambda_: - def __init__(self, model, verbose: bool = False) -> None: - self.model = model - self.verbose = verbose - - assert hasattr(model, "agents"), "Lambda_: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "S"), "Lambda_: model agents needs to have a 'S' (susceptible) attribute." - assert hasattr(model.agents, "I"), "Lambda_: model agents needs to have a 'I' (infectious) attribute." - assert hasattr(model.agents, "N"), "Lambda_: model agents needs to have a 'N' (current agents) attribute." - assert hasattr(model, "patches"), "Lambda_: model needs to have a 'patches' attribute." - assert hasattr(model.patches, "network"), "Lambda_: model patches needs to have a 'network' attribute." - assert hasattr(model.patches, "beta_hum"), "Lambda_: model patches needs to have a 'beta_hum' (human-to-human rate) attribute." - assert hasattr(model, "params"), "Lambda_: model needs to have a 'params' attribute." - assert "alpha" in model.params, "Lambda_: model params needs to have an 'alpha' parameter." - - model.patches.add_vector_property("LAMBDA", length=model.params.nticks + 1, dtype=float, default=0.0) - model.patches.add_scalar_property("tau", dtype=float, default=0.0) - initialize_tau(model, model.params) - model.patches.add_vector_property("pi", length=model.patches.count, dtype=float, default=0.0) - model.patches.pi[:, :] = model.patches.network - - return - - def __call__(self, model, tick: int) -> None: - r""" - Calculate the current human-to-human transmission rate per patch. - - $\Lambda_{j,t+1} = \frac {\beta^{hum}_{jt}((S_{jt}(1 - \tau_j))(I_{jt}(1 - \tau_j) + \sum_{\forall i \neq j (\pi_{ij} \tau_j I_{it})}))^{\alpha}} {N_{jt}}$ - """ - - # "lambda" is a reserved keyword in Python - LAMBDAprime = model.patches.LAMBDA[tick + 1] - LAMBDAprime[:] = model.agents.I[tick] * (1 - model.patches.tau) - # TODO - sum over axis=0 or axis=1? - LAMBDAprime += (model.patches.pi * (model.patches.tau * model.agents.I[tick])).sum(axis=0) - LAMBDAprime *= model.agents.S[tick] * (1 - model.patches.tau) - np.power(LAMBDAprime, model.params.alpha, out=LAMBDAprime) - LAMBDAprime *= model.patches.beta_hum[tick] - LAMBDAprime /= model.agents.N[tick] - - return - - def plot(self, fig: Figure = None): - _fig = plt.figure(figsize=(12, 9), dpi=128) if fig is None else fig - - ipatch = self.model.patches.initpop.argmax() - plt.title(f"Patch {ipatch} - Human-to-Human Transmission Rate") - plt.plot(self.model.patches.LAMBDA[:, ipatch], color="blue", label="Human-to-Human Transmission Rate") - plt.xlabel("Tick") - - yield - return - - -def initialize_tau(model, parameters): - csvfile = Path(__file__).parent / "data" / "param_tau_departure.csv" - df = pd.read_csv(csvfile) - point_values = df[df.parameter_distribution == "point"][["i", "parameter_value"]] - merged = pd.merge(model.scenario[["ISO"]], point_values, left_on="ISO", right_on="i") - model.patches.tau[:] = merged.parameter_value - - return diff --git a/src/laser_cholera/metapop/model.py b/src/laser_cholera/metapop/model.py index b5d7101..4a7b5b7 100644 --- a/src/laser_cholera/metapop/model.py +++ b/src/laser_cholera/metapop/model.py @@ -1,11 +1,9 @@ from datetime import datetime +from pathlib import Path import click -import numpy as np import pandas as pd from laser_core.laserframe import LaserFrame -from laser_core.migration import gravity -from laser_core.migration import row_normalizer from laser_core.propertyset import PropertySet from laser_core.random import seed as seed_prng from matplotlib import pyplot as plt @@ -13,75 +11,36 @@ from matplotlib.figure import Figure from tqdm import tqdm -from laser_cholera import iso_codes - -# from laser_cholera import PropagatePopulation -from laser_cholera.metapop import Births - -# from laser_cholera.metapop import Environmental -# from laser_cholera.metapop import Infected -# from laser_cholera.metapop import Lambda_ -# from laser_cholera.metapop import Population -# from laser_cholera.metapop import Psi -# from laser_cholera.metapop import Recovered -from laser_cholera.metapop import Susceptibles - -# from laser_cholera.metapop import Transmission -# from laser_cholera.metapop import Vaccinated -# from laser_cholera.metapop import Wash +from laser_cholera.metapop import Census +from laser_cholera.metapop import Environmental +from laser_cholera.metapop import EnvToHuman +from laser_cholera.metapop import HumanToHuman +from laser_cholera.metapop import Infectious +from laser_cholera.metapop import Recovered +from laser_cholera.metapop import Susceptible +from laser_cholera.metapop import Vaccinated from laser_cholera.metapop import get_parameters -from laser_cholera.metapop import scenario class Model: - def __init__(self, scenario: pd.DataFrame, parameters: PropertySet, name: str = "Cholera Metapop"): + def __init__(self, parameters: PropertySet, name: str = "Cholera Metapop"): self.tinit = datetime.now(tz=None) # noqa: DTZ005 click.echo(f"{self.tinit}: Creating the {name} model…") - self.scenario = scenario self.params = parameters self.name = name self.prng = seed_prng(parameters.seed if parameters.seed is not None else self.tinit.microsecond) - click.echo(f"Initializing the {name} model with {len(scenario)} patches…") + click.echo(f"Initializing the {name} model with {len(parameters.location_id)} patches…") # https://gilesjohnr.github.io/MOSAIC-docs/model-description.html + # setup the LaserFrame for agents/population (states and dynamics) # setup the LaserFrame for patches (inputs and reporting) - npatches = len(scenario) + npatches = len(parameters.location_id) + self.agents = LaserFrame(npatches) self.patches = LaserFrame(npatches) _istart, _iend = self.patches.add(npatches) - # self.patches.add_vector_property("population", length=parameters.nticks, dtype=np.uint32, default=np.uint32(0)) - self.patches.add_scalar_property("initpop", dtype=np.uint32, default=np.uint32(0)) - self.patches.initpop[:] = scenario.population.values - self.patches.add_vector_property("pi", length=npatches, dtype=np.float32, default=np.float32(0.0)) - for i, iso in enumerate(iso_codes): - self.patches.pi[i] = parameters.pi[iso] - self.patches.add_vector_property("W", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - self.patches.add_vector_property("beta_hum", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - self.patches.add_vector_property("beta_env", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - self.patches.add_vector_property("LAMBDA", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - self.patches.add_vector_property("PSI", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - self.patches.add_vector_property("nu", length=parameters.nticks + 1, dtype=np.float32, default=np.float32(0.0)) - - self.patches.add_scalar_property("birthrate", dtype=np.float32, default=np.float32(0.0)) - for i, iso in enumerate(iso_codes): - self.patches.birthrate[i] = parameters.birthrate[iso] - self.patches.add_scalar_property("mortrate", dtype=np.float32, default=np.float32(0.0)) - for i, iso in enumerate(iso_codes): - self.patches.mortrate[i] = parameters.mortrate[iso] - - # setup the LaserFrame for agents/population (states and dynamics) - self.agents = LaserFrame(npatches) - - # S, I, R, V (vaccinated), W (environmental) - self.agents.add_vector_property("S", length=parameters.nticks + 1, dtype=np.uint32, default=0) - self.agents.add_vector_property("I", length=parameters.nticks + 1, dtype=np.uint32, default=0) - self.agents.add_vector_property("R", length=parameters.nticks + 1, dtype=np.uint32, default=0) - self.agents.add_vector_property("V", length=parameters.nticks + 1, dtype=np.uint32, default=0) - self.agents.add_vector_property("N", length=parameters.nticks + 1, dtype=np.uint32, default=0) - - # initialize the "agent" states return @@ -123,23 +82,7 @@ def components(self, components: list) -> None: if "__call__" in dir(instance): self.phases.append(instance) - # TODO - check for 0, 1, or 2+ Births components - # if 0 and there are components with an on_birth function, warn - # if 1, add the component instances with an on_birth() function to the Births component - # if 2+, raise an exception - - births = list(filter(lambda object: isinstance(object, Births), self.instances)) - if len(births) == 0: - for instance in self.instances: - if "on_birth" in dir(instance): - click.echo( - f"Warning: Component {type(instance).__name__} has an on_birth function but there is no Births component with which to register it." - ) - elif len(births) == 1: - births = births[0] # get the single Births component - births.initializers = [instance for instance in self.instances if "on_birth" in dir(instance)] - else: - raise RuntimeError(f"Error: Multiple Births components found in {self.name} model.") + _ = [instance.check() for instance in self.instances] return @@ -272,8 +215,8 @@ def plot(self, fig: Figure = None): @click.option("--verbose", is_flag=True, help="Print verbose output") @click.option("--no-viz", is_flag=True, default=False, help="Suppress displaying visualizations") @click.option("--pdf", is_flag=True, help="Output visualization results as a PDF") -@click.option("--output", default=None, help="Output file for results") -@click.option("--params", default=None, help="JSON file with parameters") +@click.option("--outdir", default=Path.cwd(), help="Output file for results") +@click.option("--paramfile", default=None, help="JSON file with parameters") @click.option("--param", "-p", multiple=True, help="Additional parameter overrides (param:value or param=value)") def run(**kwargs): """ @@ -298,20 +241,10 @@ def run(**kwargs): None """ - parameters = get_parameters(overrides=["verbose:1"]) - # scenario = get_scenario(parameters, parameters["verbose"]) - model = Model(scenario, parameters) - - # infection dynamics come _before_ incubation dynamics so newly set itimers - # don't immediately expire - model.components = [ - Susceptibles, - ] + parameters = get_parameters(overrides=kwargs) + model = Model(parameters) - # seed_infections_randomly(model, ninfections=100) - # ipatch = model.patches.population[0, :].argmax() - ipatch = model.patches.initpop.argmax() - seed_infections_in_patch(model, ipatch=ipatch, ninfections=100) + model.components = [Susceptible, Infectious, Recovered, Vaccinated, Census, HumanToHuman, EnvToHuman, Environmental] model.run() @@ -321,14 +254,6 @@ def run(**kwargs): return -def seed_infections_in_patch(model, ipatch, ninfections): - model.agents.S[0, ipatch] -= ninfections - model.agents.I[0, ipatch] = ninfections - model.patches.W[0, ipatch] = model.params.zeta * ninfections - - return - - if __name__ == "__main__": ctx = click.Context(run) ctx.invoke(run, nticks=365, seed=20241107, verbose=True, no_viz=False, pdf=False) diff --git a/src/laser_cholera/metapop/params.py b/src/laser_cholera/metapop/params.py index 02ff5ad..5e6bd59 100644 --- a/src/laser_cholera/metapop/params.py +++ b/src/laser_cholera/metapop/params.py @@ -1,46 +1,90 @@ -import re +import json +from datetime import datetime from pathlib import Path from typing import Optional from typing import Union import click -import yaml +import numpy as np from laser_core.propertyset import PropertySet -from .defaults import write_default_yaml +def get_parameters(filename: Optional[Union[str, Path]] = None, overrides: Optional[dict] = None) -> PropertySet: + filename = Path(filename) if filename is not None else Path(__file__).parent / "data" / "default_parameters.json" -def get_parameters(filename: Optional[Union[str, Path]] = None, overrides:Optional[str] = None) -> PropertySet: + with filename.open("r") as file: + parameters = json.load(file) - filename = Path(filename) if filename is not None else write_default_yaml() + # Note the following canonicalizes the order of the locations based on the + # order in the JSON file. + # We might consider either + # a) alphabetical order by location name or + # b) order by int(ID) - with filename.open("r") as file: - parameters = yaml.safe_load(file) + # IDs are 1-based not 0-based like indices + # Map string IDs to names and names to indices (0-based) + + id_to_location_map = {str(id): name for id, name in zip(parameters["location_id"], parameters["location_name"])} + location_to_idx_map = {name: idx for idx, name in enumerate(sorted(parameters["location_name"]))} + id_to_idx_map = {str(id): location_to_idx_map[name] for id, name in id_to_location_map.items()} params = PropertySet(parameters) - params.birthrate = PropertySet(params.birthrate) - params.initpop = PropertySet(params.initpop) - params.mortrate = PropertySet(params.mortrate) - params.tau = PropertySet(params.tau) - params.beta_env_t0 = PropertySet(params.beta_env_t0) - params.beta_hum_t0 = PropertySet(params.beta_hum_t0) - params.nu = PropertySet(params.nu) - params.psi = PropertySet(params.psi) - params.theta = PropertySet(params.theta) - params.pi = PropertySet(params.pi) - params.seasonality_coefficients = PropertySet(params.seasonality_coefficients) + + params.date_start = datetime.strptime(params.date_start, "%Y-%m-%d") # noqa: DTZ007 + params.date_stop = datetime.strptime(params.date_stop, "%Y-%m-%d") # noqa: DTZ007 + + params.location_id = np.array(params.location_id, dtype=np.uint32) + + params.N_j_initial = np.array(params.N_j_initial, dtype=np.uint32) + params.S_j_initial = np.array(params.S_j_initial, dtype=np.uint32) + params.I_j_initial = np.array(params.I_j_initial, dtype=np.uint32) + params.R_j_initial = np.array(params.R_j_initial, dtype=np.uint32) + + params.b_j = np.array(params.b_j, dtype=np.float32) + params.d_j = np.array(params.d_j, dtype=np.float32) + + width = max(map(len, params.nu_jt.values())) + height = len(params.nu_jt) + nu_jt = np.zeros((height, width), dtype=np.float32) + for key, values in params.nu_jt.items(): + nu_jt[id_to_idx_map[key], : len(values)] = values + params.nu_jt = nu_jt + + params.beta_j0_hum = np.array(params.beta_j0_hum, dtype=np.float32) + + width = max(map(len, params.beta_j_seasonality.values())) + height = len(params.beta_j_seasonality) + beta_j_seasonality = np.zeros((height, width), dtype=np.float32) + for key, values in params.beta_j_seasonality.items(): + beta_j_seasonality[id_to_idx_map[key], : len(values)] = values + params.beta_j_seasonality = beta_j_seasonality + + params.tau_i = np.array(params.tau_i, dtype=np.float32) + + width = max(map(len, params.pi_ij.values())) + height = len(params.pi_ij) + pi_ij = np.zeros((height, width), dtype=np.float32) + for key, values in params.pi_ij.items(): + pi_ij[id_to_idx_map[key], : len(values)] = [value if value != "NA" else 0.0 for value in values] + params.pi_ij = pi_ij + + params.beta_j0_env = np.array(params.beta_j0_env, dtype=np.float32) + params.theta_j = np.array(params.theta_j, dtype=np.float32) + + width = max(map(len, params.psi_jt.values())) + height = len(params.psi_jt) + psi_jt = np.zeros((height, width), dtype=np.float32) + for key, values in params.psi_jt.items(): + psi_jt[id_to_idx_map[key], : len(values)] = values + params.psi_jt = psi_jt + + if overrides is not None: + params += overrides + + if params.verbose: + click.echo(f"Updated/overrode file parameters with `{overrides}`…") if params.verbose: click.echo(f"Loaded parameters from `{filename}`…") - # Overwrite any parameters with those from the command line (optional) - for kvp in overrides: - key, value = re.split("[=:]+", kvp) - if key not in params: - click.echo(f"Unknown parameter `{key}` ({value=}). Skipping…") - continue - value = type(params[key])(value) # Cast the value to the same type as the existing parameter - click.echo(f"Using `{value}` for parameter `{key}` from the command line…") - params[key] = value - return params diff --git a/src/laser_cholera/metapop/psi.py b/src/laser_cholera/metapop/psi.py deleted file mode 100644 index 7e8e69f..0000000 --- a/src/laser_cholera/metapop/psi.py +++ /dev/null @@ -1,65 +0,0 @@ -"""Environmental transmission rate.""" - -from pathlib import Path - -import matplotlib.pyplot as plt -import pandas as pd -from matplotlib.figure import Figure - - -class Psi: - def __init__(self, model, verbose: bool = False) -> None: - self.model = model - self.verbose = verbose - - assert hasattr(model, "agents"), "Psi: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "S"), "Psi: model agents needs to have a 'S' (susceptible) attribute." - assert hasattr(model, "patches"), "Psi: model needs to have a 'patches' attribute." - assert hasattr(model.patches, "PSI"), "Psi: model patches needs to have a 'PSI' (environmental transmission rate) attribute." - assert hasattr( - model.patches, "beta_env" - ), "Psi: model patches needs to have a 'beta_env' (environmental transmission rate) attribute." - assert hasattr(model.patches, "W"), "Psi: model patches needs to have a 'W' (environmental) attribute." - assert hasattr(model.patches, "tau"), "Psi: model patches needs to have a 'tau' (contamination rate) attribute." - assert hasattr(model.patches, "theta"), "Psi: model patches needs to have a 'theta' (fraction of population with WASH) attribute." - assert hasattr(model, "params"), "Psi: model needs to have a 'params' attribute." - assert hasattr(model.params, "kappa"), "Psi: model params needs to have a 'kappa' (environmental transmission rate) parameter." - - initialize_psi(model) - - return - - def __call__(self, model, tick: int) -> None: - PSIprime = model.patches.PSI[tick + 1] - beta_env = model.patches.beta_env[tick] - S = model.agents.S[tick] - W = model.patches.W[tick] - - PSIprime[:] = beta_env * (S * (1 - model.patches.tau)) * (1 - model.patches.theta[tick]) * W - PSIprime /= model.params.kappa + W - - return - - def plot(self, fig: Figure = None): - _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig - - ipatch = self.model.patches.initpop.argmax() - plt.title(f"Environmental Transmission Rate in Patch {ipatch}") - plt.plot(self.model.patches.PSI[:, ipatch], color="orange", label="Environmental Transmission Rate") - plt.xlabel("Tick") - - yield - return range(0) - - -def initialize_psi(model): - csvfile = Path(__file__).parent / "data" / "pred_psi_suitability.csv" - df = pd.read_csv(csvfile)[["iso_code", "date_start", "pred"]] - df.date_start = pd.to_datetime(df.date_start) - pivot = df.pivot(index="iso_code", columns="date_start", values="pred") - countries = model.scenario[["ISO"]] - data = countries.merge(pivot, left_on="ISO", right_on="iso_code") - data = data[data.columns[1:]] # drop the ISO code column - model.patches.PSI[:, :] = data[data.columns[1:]].to_numpy()[:, -(model.params.nticks + 1) :].T - - return diff --git a/src/laser_cholera/metapop/recovered.py b/src/laser_cholera/metapop/recovered.py index 423433c..e64f609 100644 --- a/src/laser_cholera/metapop/recovered.py +++ b/src/laser_cholera/metapop/recovered.py @@ -1,4 +1,9 @@ +from datetime import datetime + import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet from matplotlib.figure import Figure @@ -8,32 +13,87 @@ def __init__(self, model, verbose: bool = False) -> None: self.verbose = verbose assert hasattr(model, "agents"), "Recovered: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "R"), "Recovered: model agents needs to have a 'R' (recovered) attribute." - assert hasattr(model.agents, "I"), "Recovered: model agents needs to have a 'I' (infectious) attribute." - assert hasattr(model, "params"), "Recovered: model needs to have a 'params' attribute." - assert hasattr(model.params, "gamma"), "Recovered: model params needs to have a 'gamma' (recovery rate) parameter." - assert hasattr(model.params, "epsilon"), "Recovered: model params needs to have a 'epsilon' (recovery rate) parameter." - assert hasattr(model, "patches"), "Recovered: model needs to have a 'patches' attribute." - assert hasattr(model.patches, "mortrate"), "Recovered: model patches needs to have a 'mortrate' parameter." + model.agents.add_vector_property("R", length=model.params.nticks + 1, dtype=np.uint32, default=0) + assert hasattr(self.model, "params"), "Recovered: model needs to have a 'params' attribute." + assert hasattr( + self.model.params, "R_j_initial" + ), "Recovered: model params needs to have a 'R_j_initial' (initial recovered population) parameter." + model.agents.R[0] = model.params.R_j_initial + + return + def check(self): + assert hasattr(self.model.agents, "S"), "Recovered: model agents needs to have a 'S' (susceptible) attribute." + assert hasattr(self.model.agents, "I"), "Recovered: model agents needs to have a 'I' (infectious) attribute." + assert hasattr(self.model.params, "d_j"), "Recovered: model params needs to have a 'd_j' (mortality rate) parameter." + assert hasattr(self.model.params, "epsilon"), "Recovered: model params needs to have a 'epsilon' (waning immunity rate) parameter." + assert hasattr(self.model.params, "gamma"), "Recovered: model params needs to have a 'gamma' (infection recovery rate) parameter." return def __call__(self, model, tick: int) -> None: - R = model.agents.R[tick] - Rprime = model.agents.R[tick + 1] I = model.agents.I[tick] # noqa: E741 Iprime = model.agents.I[tick + 1] + R = model.agents.R[tick] + Rprime = model.agents.R[tick + 1] Sprime = model.agents.S[tick + 1] - Rprime[:] = R - newly_recovered = model.prng.binomial(I, model.params.gamma).astype(Rprime.dtype) # rate or probability? + # natural mortality + # TODO - rate or probability? + Rmort = model.prng.poisson(model.params.d_j * R).astype(Rprime.dtype) + Rprime[:] = R - Rmort + + # waning natural immunity + # TODO - rate or probability? + waned_natural_immunity = model.prng.binomial(Rprime, model.params.epsilon).astype(Sprime.dtype) + Sprime += waned_natural_immunity + Rprime -= waned_natural_immunity + + # recovery from infection + # TODO - rate or probability? + newly_recovered = model.prng.binomial(I, model.params.gamma).astype(Iprime.dtype) Iprime -= newly_recovered Rprime += newly_recovered - waned = model.prng.binomial(R, model.params.epsilon).astype(Rprime.dtype) # rate or probability? - Sprime += waned - Rprime -= waned - # non-disease mortality - Rprime -= model.prng.binomial(R, model.patches.mortrate).astype(Rprime.dtype) # rate or probability? + + return + + @staticmethod + def test(): + class Model: + def __init__(self, d_j: float = 0.0, epsilon: float = 0.0, gamma: float = 0.0): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("S", length=8, dtype=np.uint32, default=0) + self.agents.add_vector_property("I", length=8, dtype=np.uint32, default=0) + self.agents.I[0] = [1_000, 10_000, 100_000, 1_000_000] + self.params = PropertySet( + {"d_j": d_j, "epsilon": epsilon, "gamma": gamma, "R_j_initial": [1_000, 10_000, 100_000, 1_000_000], "nticks": 8} + ) + + component = Recovered(model := Model(d_j=1.0 / 80.0)) + component.check() + component(model, 0) + assert np.all(model.agents.R[0] == model.params.R_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.R[1] < model.agents.R[0] + ), f"Some populations didn't shrink with natural mortality.\n\t{model.agents.R[0]}\n\t{model.agents.R[1]}" + + component = Recovered(model := Model(epsilon=0.04)) + component.check() + component(model, 0) + assert np.all(model.agents.R[0] == model.params.R_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.R[1] < model.agents.R[0] + ), f"Some populations didn't shrink with waning natural immunity.\n\t{model.agents.R[0]}\n\t{model.agents.R[1]}" + + component = Recovered(model := Model(gamma=0.1)) + component.check() + component(model, 0) + assert np.all(model.agents.R[0] == model.params.R_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.R[1] > model.agents.R[0] + ), f"Some populations didn't increase with recovery from infection.\n\t{model.agents.R[0]}\n\t{model.agents.R[1]}" + + print("PASSED Recovered.test()") return diff --git a/src/laser_cholera/metapop/susceptible.py b/src/laser_cholera/metapop/susceptible.py new file mode 100644 index 0000000..f8a3694 --- /dev/null +++ b/src/laser_cholera/metapop/susceptible.py @@ -0,0 +1,86 @@ +from datetime import datetime + +import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet +from matplotlib.figure import Figure + + +class Susceptible: + def __init__(self, model, verbose: bool = False): + self.model = model + self.verbose = verbose + + assert hasattr(model, "agents"), "Susceptible: model needs to have a 'agents' attribute." + model.agents.add_vector_property("S", length=model.params.nticks + 1, dtype=np.uint32, default=0) + assert hasattr(self.model, "params"), "Susceptible: model needs to have a 'params' attribute." + assert "S_j_initial" in self.model.params, "Susceptible: model params needs to have a 'S_j_initial' parameter." + model.agents.S[0] = model.params.S_j_initial + + return + + def check(self): + assert hasattr(self.model.agents, "N"), "Susceptible: model.agents needs to have a 'N' attribute." + assert hasattr(self.model.params, "b_j"), "Susceptible: model.params needs to have a 'b_j' attribute." + assert hasattr(self.model.params, "d_j"), "Susceptible: model.params needs to have a 'd_j' attribute." + + return + + def __call__(self, model, tick: int) -> None: + Sprime = model.agents.S[tick + 1] + S = model.agents.S[tick] + Sprime[:] = S + + # births + N = model.agents.N[tick] + # TODO - rate or probability? + Sprime[:] += model.prng.poisson(model.params.b_j * N).astype(Sprime.dtype) + + # natural mortality + # TODO - rate or probability? + Smort = model.prng.poisson(model.params.d_j * S).astype(Sprime.dtype) + Sprime -= Smort + + return + + @staticmethod + def test(): + class Model: + def __init__(self, b_j: float = 0.0, d_j: float = 0.0): + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("N", length=8, dtype=np.uint32, default=0) + self.agents.N[0] = [1_000, 10_000, 100_000, 1_000_000] + self.params = PropertySet({"b_j": b_j, "d_j": d_j, "S_j_initial": [1_000, 10_000, 100_000, 1_000_000], "nticks": 8}) + + component = Susceptible(model := Model(b_j=3.0 / 80.0)) + component.check() + component(model, 0) + assert np.all(model.agents.S[0] == model.params.S_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.S[1] > model.agents.S[0] + ), f"Some populations didn't grow with births.\n\t{model.agents.S[0]}\n\t{model.agents.S[1]}" + + component = Susceptible(model := Model(d_j=1.0 / 80.0)) + component.check() + component(model, 0) + assert np.all(model.agents.S[0] == model.params.S_j_initial), "Initial populations didn't match." + assert np.all( + model.agents.S[1] < model.agents.S[0] + ), f"Some populations didn't shrink with deaths.\n\t{model.agents.S[0]}\n\t{model.agents.S[1]}" + + print("PASSED Susceptible.test()") + + return + + def plot(self, fig: Figure = None): + _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig + + ipatch = self.model.patches.initpop.argmax() + plt.title(f"Susceptibles in Patch {ipatch}") + plt.plot(self.model.agents.S[:, ipatch], color="green", label="Susceptibles") + plt.xlabel("Tick") + + yield + return diff --git a/src/laser_cholera/metapop/susceptibles.py b/src/laser_cholera/metapop/susceptibles.py deleted file mode 100644 index 7b85767..0000000 --- a/src/laser_cholera/metapop/susceptibles.py +++ /dev/null @@ -1,70 +0,0 @@ -import matplotlib.pyplot as plt -from matplotlib.figure import Figure - - -class Susceptibles: - def __init__(self, model, verbose: bool = False): - self.model = model - self.verbose = verbose - - assert hasattr(model, "agents"), "Susceptibles: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "S"), "Susceptibles: model agents needs to have a 'S' (susceptible) attribute." - assert hasattr(model.agents, "R"), "Susceptibles: model agents needs to have a 'R' (recovered) attribute." - assert hasattr(model.agents, "N"), "Susceptibles: model agents needs to have a 'N' (current population) attribute." - assert hasattr(model.agents, "V"), "Susceptibles: model agents needs to have a 'V' (vaccinated) attribute." - assert hasattr(model, "patches"), "Susceptibles: model needs to have a 'patches' attribute." - assert hasattr( - model.patches, "LAMBDA" - ), "Susceptibles: model patches needs to have a 'LAMBDA' (human-to-human transmission rate) attribute." - assert hasattr( - model.patches, "PSI" - ), "Susceptibles: model patches needs to have a 'PSI' (environmental transmission rate) attribute." - assert hasattr(model.patches, "nu"), "Susceptibles: model patches needs to have a 'nu' (incidence) attribute." - assert hasattr(model.patches, "birthrate"), "Susceptibles: model patches needs to have a 'birth' parameter." - assert hasattr(model.patches, "mortrate"), "Susceptibles: model patches needs to have a 'mortrate' parameter." - assert hasattr(model, "params"), "Infected: model needs to have a 'params' attribute." - assert hasattr(model.params, "phi"), "Susceptibles: model params needs to have a 'phi' (death rate) parameter." - assert hasattr(model.params, "omega"), "Susceptibles: model params needs to have a 'omega' (vaccination rate) parameter." - - # model.agents.add_vector_property("S", length=model.params.nticks+1, dtype=float, default=0.0) - model.agents.S[0,:] = model.patches.initpop - - return - - def __call__(self, model, tick: int) -> None: - S = model.agents.S[tick] - Sprime = model.agents.S[tick + 1] - # R = model.agents.R[tick] - N = model.agents.N[tick] - # V = model.agents.V[tick] - # LAMBDA = model.patches.LAMBDA[tick + 1] - # PSI = model.patches.PSI[tick + 1] - - Sprime[:] = S - # births - Sprime += model.prng.binomial(N, model.patches.birthrate).astype(Sprime.dtype) # rate or probability? - # # vaccinations - # Sprime -= model.prng.binomial(S, model.params.phi * model.patches.nu[tick]).astype(Sprime.dtype) - # # waning vaccinations - # Sprime += model.prng.binomial(V, model.params.omega).astype(Sprime.dtype) - # human-to-human transmission - # Sprime -= LAMBDA.astype(Sprime.dtype) # TODO - is this a rate or a count? - # # environmental-to-human transmission - # Sprime -= PSI.astype(Sprime.dtype) # TODO - is this a rate or a count? - # # waning acquired immunity - # Sprime += model.prng.binomial(R, model.params.epsilon).astype(Sprime.dtype) # rate or probability? - # non-disease mortality - Sprime -= model.prng.binomial(S, model.patches.mortrate).astype(Sprime.dtype) # rate or probability? - - return - - def plot(self, fig: Figure = None): - _fig = Figure(figsize=(12, 9), dpi=128) if fig is None else fig - - ipatch = self.model.patches.initpop.argmax() - plt.title(f"Susceptibles in Patch {ipatch}") - plt.plot(self.model.agents.S[:, ipatch], color="green", label="Susceptibles") - plt.xlabel("Tick") - - yield - return diff --git a/src/laser_cholera/metapop/transmission.py b/src/laser_cholera/metapop/transmission.py deleted file mode 100644 index b385026..0000000 --- a/src/laser_cholera/metapop/transmission.py +++ /dev/null @@ -1,56 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.figure import Figure - - -class Transmission: - def __init__(self, model, verbose: bool = False) -> None: - self.model = model - self.verbose = verbose - - assert hasattr(model, "agents"), "Transmission: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "S"), "Transmission: model agents needs to have a 'S' (susceptible) attribute." - assert hasattr(model.agents, "I"), "Transmission: model agents needs to have a 'I' (infectious) attribute." - assert hasattr(model.agents, "R"), "Transmission: model agents needs to have a 'R' (recovered) attribute." - assert hasattr(model.agents, "V"), "Transmission: model agents needs to have a 'V' (vaccinated) attribute." - assert hasattr(model, "patches"), "Transmission: model needs to have a 'patches' attribute." - assert hasattr(model, "prng"), "Transmission: model needs to have a 'prng' pseudo-random number generator." - - model.patches.add_vector_property("incidence", length=model.params.nticks, dtype=np.uint32, default=0) - - return - - def __call__(self, model, tick: int) -> None: - # new infections = Poisson(beta * S * I / N) - - S = model.agents.S[tick, :] - I = model.agents.I[tick, :] # noqa: E741 ("I" is ambiguous) - R = model.agents.R[tick, :] - V = model.agents.V[tick, :] - incidence = model.patches.incidence[tick, :] - - N = S + I + R + V - beta = model.params.beta - incidence[:] = model.prng.poisson(beta * S * I / N).astype(model.agents.I.dtype) - Sprime = model.agents.S[tick + 1, :] - Iprime = model.agents.I[tick + 1, :] - Sprime[:] = S - incidence - Iprime[:] = I + incidence - - return - - def plot(self, fig: Figure = None): - _fig = plt.figure(figsize=(12, 9), dpi=128) if fig is None else fig - - ipatch = self.model.patches.initpop.argmax() - plt.title(f"Patch {ipatch} - Transmission") - ax1 = plt.gca() - ax1.plot(self.model.agents.I[:, ipatch], color="red", label="Cases") - ax2 = ax1.twinx() - ax2.plot(self.model.patches.incidence[:, ipatch], color="purple", label="Incidence") - plt.xlabel("Tick") - plt.legend() - - yield - - return diff --git a/src/laser_cholera/metapop/vaccinated.py b/src/laser_cholera/metapop/vaccinated.py index ba2b032..6616a28 100644 --- a/src/laser_cholera/metapop/vaccinated.py +++ b/src/laser_cholera/metapop/vaccinated.py @@ -1,4 +1,9 @@ +from datetime import datetime + import matplotlib.pyplot as plt +import numpy as np +from laser_core.laserframe import LaserFrame +from laser_core.propertyset import PropertySet from matplotlib.figure import Figure @@ -8,14 +13,16 @@ def __init__(self, model, verbose: bool = False) -> None: self.verbose = verbose assert hasattr(model, "agents"), "Vaccinated: model needs to have a 'agents' attribute." - assert hasattr(model.agents, "V"), "Vaccinated: model agents needs to have a 'V' (vaccinated) attribute." - assert hasattr(model.agents, "S"), "Vaccinated: model agents needs to have a 'S' (susceptible) attribute." - assert hasattr(model, "params"), "Vaccinated: model needs to have a 'params' attribute." - assert hasattr(model.params, "phi"), "Vaccinated: model needs to have a 'phi' (vaccination rate) parameter." - assert hasattr(model.params, "omega"), "Vaccinated: model needs to have a 'omega' (vaccination rate) parameter." - assert hasattr(model, "patches"), "Vaccinated: model needs to have a 'patches' attribute." - assert hasattr(model.patches, "mortrate"), "Vaccinated: model patches needs to have a 'mortrate' parameter." - assert hasattr(model.patches, "nu"), "Vaccinated: model patches needs to have a 'nu' (incidence) attribute." + model.agents.add_vector_property("V", length=model.params.nticks + 1, dtype=np.uint32, default=0) + + return + + def check(self): + assert hasattr(self.model.agents, "S"), "Vaccinated: model agents needs to have a 'S' (susceptible) attribute." + assert hasattr(self.model.params, "d_j"), "Susceptible: model.params needs to have a 'd_j' attribute." + assert hasattr(self.model.params, "omega"), "Vaccinated: model.params needs to have a 'omega' (vaccination waning rate) parameter." + assert hasattr(self.model.params, "phi"), "Vaccinated: model.params needs to have a 'phi' (vaccination efficacy) parameter." + assert hasattr(self.model.params, "nu_jt"), "Vaccinated: model.params needs to have a 'nu_jt' (vaccination rate) parameter." return @@ -25,15 +32,77 @@ def __call__(self, model, tick: int) -> None: S = model.agents.S[tick] Sprime = model.agents.S[tick + 1] - Vprime[:] = V - newly_vaxxed = model.prng.binomial(S, model.params.phi * model.patches.nu[tick]).astype(Vprime.dtype) # rate or probability? - Vprime += newly_vaxxed - Sprime -= newly_vaxxed - waned_vax = model.prng.binomial(V, model.params.omega).astype(Vprime.dtype) - Vprime -= waned_vax - Sprime += waned_vax # non-disease mortality - Vprime -= model.prng.binomial(V, model.patches.mortrate).astype(Vprime.dtype) # rate or probability? + # TODO - rate or probability? + Vmort = model.prng.poisson(model.params.d_j * V).astype(Vprime.dtype) + Vprime[:] = V - Vmort + + # waning vaccine immunity + # TODO - rate or probability? + waned_vaccinations = model.prng.binomial(Vprime, model.params.omega).astype(Sprime.dtype) + Sprime += waned_vaccinations + Vprime -= waned_vaccinations + + # vaccination + # TODO - look up vaccination rates correctly based on tick/calendar date + # TODO - rate or probability? + newly_vaccinated = model.prng.poisson(model.params.phi * model.params.nu_jt[:, tick] * S).astype(Sprime.dtype) + Sprime -= newly_vaccinated + Vprime += newly_vaccinated + + return + + @staticmethod + def test(): + class Model: + def __init__(self, d_j: float = 0.0, omega: float = 0.0, phi: float = 0.0, nu_jt: np.ndarray = np.zeros((4, 8))): # noqa: B008 + self.prng = np.random.default_rng(datetime.now().microsecond) # noqa: DTZ005 + self.agents = LaserFrame(4) + self.agents.add_vector_property("S", length=8, dtype=np.uint32, default=0) + self.agents.S[0] = [1_000, 10_000, 100_000, 1_000_000] + self.params = PropertySet({"d_j": d_j, "omega": omega, "phi": phi, "nu_jt": nu_jt, "nticks": 8}) + + component = Vaccinated(model := Model(d_j=1.0 / 80.0)) + component.check() + model.agents.V[0] = [1_000, 10_000, 100_000, 1_000_000] + component(model, 0) + assert np.all( + model.agents.V[1] < model.agents.V[0] + ), f"Some populations didn't shrink with natural mortality.\n\t{model.agents.V[0]}\n\t{model.agents.V[1]}" + + component = Vaccinated(model := Model(omega=0.06)) + component.check() + model.agents.V[0] = [1_000, 10_000, 100_000, 1_000_000] + component(model, 0) + assert np.all( + model.agents.V[1] < model.agents.V[0] + ), f"Some populations didn't shrink with waning vaccine immunity.\n\t{model.agents.V[0]}\n\t{model.agents.V[1]}" + + component = Vaccinated(model := Model(phi=0.0, nu_jt=0.1 * np.ones((4, 8)))) + component.check() + model.agents.V[0] = [1_000, 10_000, 100_000, 1_000_000] + component(model, 0) + assert np.all( + model.agents.V[1] == model.agents.V[0] + ), f"Some populations changed with vaccine take = 0.\n\t{model.agents.V[0]}\n\t{model.agents.V[1]}" + + component = Vaccinated(model := Model(phi=0.64, nu_jt=np.zeros((4, 8)))) + component.check() + model.agents.V[0] = [1_000, 10_000, 100_000, 1_000_000] + component(model, 0) + assert np.all( + model.agents.V[1] == model.agents.V[0] + ), f"Some populations changed with vaccination rate = 0.\n\t{model.agents.V[0]}\n\t{model.agents.V[1]}" + + component = Vaccinated(model := Model(phi=0.64, nu_jt=0.1 * np.ones((4, 8)))) + component.check() + model.agents.V[0] = [1_000, 10_000, 100_000, 1_000_000] + component(model, 0) + assert np.all( + model.agents.V[1] > model.agents.V[0] + ), f"Some populations didn't increase with vaccine take > 0 and vaccination rate > 0.\n\t{model.agents.V[0]}\n\t{model.agents.V[1]}" + + print("PASSED Vaccinated.test()") return