def test_sir_phg_expon(do_plot=False):
N = 1000
reproductive_factor = 2.2
infectious_period_mean = 10
dist = ph_expon(lambd=1 / infectious_period_mean)
assert dist.mean() == infectious_period_mean
gam = 1 / dist.mean()
beta = reproductive_factor * gam
sir = sir_classical(
name="SIR",
population=N,
reproductive_factor=reproductive_factor,
infectious_period_mean=infectious_period_mean,
num_days=160,
)
sir_ph_exp = sir_phg(name="SIR-PH-Expo",
population=N,
beta=beta,
infectious_time_distribution=dist,
num_days=160)
error = print_error(sir, sir_ph_exp)
if do_plot:
from matplotlib import pyplot as plt
from epistoch.utils.plotting import plot_sir
fig = plot_sir(sir, title="PH-expo-tests")
plot_sir(sir_ph_exp, N, fig, linestyle="--")
plt.show()
assert 0.0 == pytest.approx(error, abs=1e-2)
def test_sir_phg_erlang(do_plot=False):
N = 1000
reproductive_factor = 2.2
infectious_period_mean = 10
n = 3
dist = ph_erlang(n=n, lambd=n / infectious_period_mean)
assert dist.mean() == infectious_period_mean
gam = 1 / dist.mean()
beta = reproductive_factor * gam
num_days = 160
sir_g_model = sir_g(
"SIR-G",
population=N,
reproductive_factor=reproductive_factor,
infectious_time_distribution=dist,
num_days=num_days,
num_periods=2000,
)
sir_ph_erlang = sir_phg("SIR-PHG",
population=N,
beta=beta,
infectious_time_distribution=dist)
error = print_error(sir_g_model, sir_ph_erlang)
if do_plot:
from matplotlib import pyplot as plt
from epistoch.utils.plotting import plot_sir
fig = plot_sir(sir_g_model, title="PH-erlang-tests")
plot_sir(sir_ph_erlang, fig, linestyle="--")
plt.show()
assert 0.0 == pytest.approx(error, abs=1e-2)
def compare_models(
name, dist, N, I0=1, num_days=100, R0=2.25, do_plots=True, use_latex=False, plot_path=".", plot_ext="pdf"
):
name1 = "SIR"
SIR_classic = sir_classical(
name=name1, population=N, I0=I0, reproductive_factor=R0, infectious_period_mean=dist.mean(), num_days=num_days
)
name2 = "SIR-G"
SIR_general = sir_g(
name=name2,
population=N,
I0=I0,
num_days=num_days,
num_periods=2000,
reproductive_factor=R0,
infectious_time_distribution=dist,
method="loss",
)
report_summary(SIR_classic)
report_summary(SIR_general)
print_error(SIR_classic, SIR_general)
if do_plots:
fig = plot_sir(SIR_classic, title=name + ": SIR and SIR-G models", use_latex=use_latex)
plot_sir(SIR_general, fig, linestyle="--")
plt.show()
filename = os.path.join(plot_path, name + "-SIR-comp." + plot_ext)
print(f"Saving picture in file {os.path.abspath(filename)}")
fig.savefig(filename, bbox_inches="tight")
fig2 = plot_IR(SIR_classic, title=name + ": I, R as function of S")
plot_IR(SIR_general, fig2, linestyle="--")
plt.show()
filename = os.path.join(plot_path, name + "-IR-comp." + plot_ext)
print(f"Saving picture in file {os.path.abspath(filename)}")
fig2.savefig(filename, bbox_inches="tight")
print("Done")
def variance_analysis(
N=1000000,
I0=1,
num_days=100,
R0=2.25,
infectious_period_mean=4.4,
cvs=[0.5, 1.0, 2.0],
use_latex=False,
plot_path=".",
plot_ext="pdf",
):
dists = {}
models = {}
print("Computing Classical")
sir_classic = sir_classical(
name="SIR",
population=N,
I0=I0,
reproductive_factor=R0,
infectious_period_mean=infectious_period_mean,
num_days=num_days,
)
fig = plot_sir(sir_classic, formats={"I": "r-"}, title="SIR-G with Different Distributions", linewidth=3)
print("Computing Constant SIR-G")
dist = utils.stats.constant(infectious_period_mean)
sir_constant = sir_g(
name="Const",
population=N,
I0=I0,
num_days=num_days,
num_periods=2000,
reproductive_factor=R0,
infectious_time_distribution=dist,
method="loss",
)
fig = plot_sir(sir_constant, fig, formats={"I": "b-."}, linewidth=3, use_latex=use_latex)
dists["gamma"] = utils.stats.get_gamma
dists["lognorm"] = utils.stats.get_lognorm
for cv in cvs:
for dist_name, dist_getter in dists.items():
infectious_period_sd = cv * infectious_period_mean
dist = dist_getter(infectious_period_mean, infectious_period_sd)
mod_name = f"{dist_name}-{cv:.2}"
print(f"Running {mod_name}")
num_days = round(100 * max(1, cv))
models[(dist, cv)] = sir_g(
name=mod_name,
population=N,
I0=I0,
num_days=num_days,
num_periods=2000,
reproductive_factor=R0,
infectious_time_distribution=dist,
method="loss",
)
# models[(dist, cv)].name = mod_name
plot_sir(models[(dist, cv)], fig, formats={"I": ""}, linestyle="--")
plt.show()
filename = os.path.join(plot_path, "Variance-Analysis." + plot_ext)
print(f"Saving picture in file {os.path.abspath(filename)}")
fig.savefig(filename, bbox_inches="tight")
return models
def test_seird(do_plot=False):
N = 1000
beta = 0.2
exposed_time_mean = 1
die_time_mean = 7
recover_time_mean = 12
exposed_time_exp = ph_expon(mean=exposed_time_mean)
die_time_exp = ph_expon(mean=die_time_mean)
recover_time_exp = ph_expon(mean=recover_time_mean)
exposed_time = ph_erlang(n=10, mean=exposed_time_mean)
die_time = ph_erlang(n=15, mean=die_time_mean)
recover_time = ph_erlang(n=8, mean=recover_time_mean)
fatality_rate = 0.2
I0 = 10
num_days = 200
model_expo = seird_ph(
"SEIRD-Expo",
population=N,
beta=beta,
exposed_time=exposed_time_exp,
time_to_die=die_time_exp,
time_to_recover=recover_time_exp,
fatality_rate=fatality_rate,
I0=I0,
num_days=num_days,
)
model_gen = seird_ph(
"SEIRD-Gen",
population=N,
beta=beta,
exposed_time=exposed_time,
time_to_die=die_time,
time_to_recover=recover_time,
fatality_rate=fatality_rate,
I0=I0,
num_days=num_days,
)
if do_plot:
from matplotlib import pyplot as plt
from epistoch.utils.plotting import plot_sir
formats = {
"S": "b-",
"E": "c-",
"I": "r-",
"R": "g-",
"Rc": "y-",
"D": "m-"
}
legend_fmt = {
"loc": "upper right",
"shadow": True,
"framealpha": 1.0,
"bbox_to_anchor": (1, 1)
}
report_summary(model_expo)
fig = plot_sir(model_expo,
title="SEIRD Expo vs Erlang",
formats=formats)
report_summary(model_gen)
fig = plot_sir(model_gen,
formats=formats,
fig=fig,
linestyle=":",
legend_fmt=legend_fmt)
plt.show()
filename = os.path.join("./paper/epistoch/figures/", "SEIRD.pdf")
print(f"Saving picture in file {os.path.abspath(filename)}")
fig.savefig(filename, bbox_inches="tight")