import matplotlib.pyplot as plt
import numpy as np
from matplotlib.figure import Figure
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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("Isym", length=model.params.nticks + 1, dtype=np.int32, default=0)
model.agents.add_vector_property("Iasym", length=model.params.nticks + 1, dtype=np.int32, default=0)
assert hasattr(model, "patches"), "Infectious: model needs to have a 'patches' attribute."
model.patches.add_vector_property("expected_cases", length=model.params.nticks + 1, dtype=np.int32, default=0)
model.patches.add_vector_property("disease_deaths", length=model.params.nticks + 1, dtype=np.int32, default=0)
assert hasattr(model, "params"), "Infectious: model needs to have a 'params' attribute."
assert hasattr(model.params, "I_j_initial"), (
"Infectious: model params needs to have a 'I_j_initial' (initial infectious population) parameter."
)
assert "sigma" in self.model.params, "Infectious: model params needs to have a 'sigma' (symptomatic fraction) parameter."
model.agents.Isym[0] = np.round(model.params.sigma * model.params.I_j_initial).astype(model.agents.Isym.dtype)
model.agents.Iasym[0] = model.params.I_j_initial - model.agents.Isym[0]
return
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def check(self):
assert hasattr(self.model.agents, "R"), "Infectious: model.agents needs to have a 'S' attribute."
assert "d_jt" in self.model.params, "Infectious: model params needs to have a 'd_jt' (mortality rate) parameter."
assert "mu_jt" in self.model.params, "Infectious: model params needs to have a 'mu_jt' (disease mortality rate) parameter."
assert "gamma_1" in self.model.params, "Infectious: model params needs to have a 'gamma_1' (recovery rate) parameter."
assert "gamma_2" in self.model.params, "Infectious: model params needs to have a 'gamma_2' (recovery rate) parameter."
assert "iota" in self.model.params, "Infectious: model params needs to have a 'iota' (progression rate) parameter."
assert "sigma" in self.model.params, "Infectious: model params needs to have a 'sigma' (symptomatic fraction) parameter."
assert "rho" in self.model.params, "Infectious: model params needs to have a 'rho' (detected/expected cases) parameter."
if not hasattr(self.model.patches, "non_disease_deaths"):
self.model.patches.add_vector_property("non_disease_deaths", length=self.model.params.nticks + 1, dtype=np.int32, default=0)
return
def __call__(self, model, tick: int) -> None:
# Symptomatic
Isym = model.agents.Isym[tick]
Is_next = model.agents.Isym[tick + 1]
Is_next[:] = Isym
## natural deaths (d_jt)
non_disease_deaths = model.prng.binomial(Is_next, -np.expm1(-model.params.d_jt[tick])).astype(Is_next.dtype)
Is_next -= non_disease_deaths
ndd_next = model.patches.non_disease_deaths[tick + 1]
ndd_next += non_disease_deaths
assert np.all(Is_next >= 0), f"Is_next should not go negative ({tick=}\n\t{Is_next=})"
## disease deaths (mu)
disease_deaths = model.prng.binomial(Is_next, -np.expm1(-model.params.mu_jt[tick])).astype(Is_next.dtype)
model.patches.disease_deaths[tick] = disease_deaths
Is_next -= disease_deaths
assert np.all(Is_next >= 0), f"Is_next should not go negative ({tick=}\n\t{Is_next=})"
## recovery (gamma)
recovered = model.prng.binomial(Is_next, -np.expm1(-model.params.gamma_1)).astype(Is_next.dtype)
Is_next -= recovered
R_next = model.agents.R[tick + 1]
R_next += recovered
assert np.all(Is_next >= 0), f"Is_next should not go negative ({tick=}\n\t{Is_next=})"
# Asymptomatic
Iasym = model.agents.Iasym[tick]
Ia_next = model.agents.Iasym[tick + 1]
Ia_next[:] = Iasym
## natural deaths (d_jt)
non_disease_deaths = model.prng.binomial(Ia_next, -np.expm1(-model.params.d_jt[tick])).astype(Ia_next.dtype)
Ia_next -= non_disease_deaths
assert np.all(Ia_next >= 0), f"Ia_next should not go negative ({tick=}\n\t{Ia_next=})"
## recovery
recovered = model.prng.binomial(Ia_next, -np.expm1(-model.params.gamma_2)).astype(Ia_next.dtype)
Ia_next -= recovered
# R_next = model.agents.R[tick + 1]
R_next += recovered
assert np.all(Ia_next >= 0), f"Ia_next should not go negative ({tick=}\n\t{Ia_next=})"
# Use E_next here, can't progress deceased individuals
E_next = model.agents.E[tick + 1]
progress = model.prng.binomial(E_next, -np.expm1(-model.params.iota)).astype(E_next.dtype)
E_next -= progress
assert np.all(E_next >= 0), f"E_next should not go negative ({tick=}\n\t{E_next=})"
## new symptomatic infections
new_symptomatic = np.round(model.params.sigma * progress).astype(Is_next.dtype)
new_asymptomatic = progress - new_symptomatic
Is_next += new_symptomatic
Ia_next += new_asymptomatic
# Update expected cases
expected_cases = model.patches.expected_cases[tick + 1]
expected_cases += np.round(new_symptomatic / model.params.rho).astype(expected_cases.dtype)
# human-to-human infection in humantohuman.py
# environmental infection in envtohuman.py
# recovery from infection in recovered.py
return
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def plot(self, fig: Figure = None): # pragma: no cover
_fig = plt.figure(figsize=(12, 9), dpi=128, num="Infectious (Symptomatic)") if fig is None else fig
for ipatch in np.argsort(self.model.params.S_j_initial)[-10:]:
plt.plot(self.model.agents.Isym[:, ipatch], label=f"{self.model.params.location_name[ipatch]}")
plt.xlabel("Tick")
plt.ylabel("Symptomatic")
plt.legend()
yield "Infectious (Symptomatic)"
_fig = plt.figure(figsize=(12, 9), dpi=128, num="Infectious (Asymptomatic)") if fig is None else fig
for ipatch in np.argsort(self.model.params.S_j_initial)[-10:]:
plt.plot(self.model.agents.Iasym[:, ipatch], label=f"{self.model.params.location_name[ipatch]}")
plt.xlabel("Tick")
plt.ylabel("Asymptomatic")
plt.legend()
yield "Infectious (Asymptomatic)"
_fig = plt.figure(figsize=(12, 9), dpi=128, num="Infectious (Total)") if fig is None else fig
for ipatch in np.argsort(self.model.params.S_j_initial)[-10:]:
plt.plot(
self.model.agents.Isym[:, ipatch] + self.model.agents.Iasym[:, ipatch], label=f"{self.model.params.location_name[ipatch]}"
)
plt.xlabel("Tick")
plt.ylabel("Total Infectious")
plt.legend()
yield "Infectious (Total)"
return