def test_inertia_of_model():
"""
Explores delay of H and D relative to C and overshoot/undershoot with variable dwell time
parameters.
"""
# Time period for the run (again relative to Jan 1, 2020)
t_list = np.linspace(
0, 100, 100 + 1) # Here starts from when FL started to ramp up cases
# Need assumptions for R(t) and testing_rate(t) for the future as inputs
rt = lambda t: 1.0 + 0.2 * math.sin(t / 12.0)
model = NowcastingSEIRModel()
delays = []
t_i = 6
t_h = 8
for (t_i, t_h) in [(3, 8), (6, 8), (12, 8), (6, 4), (6, 16), (12, 16)]:
model.t_i = t_i
model.th0 = t_h
# Create a ModelRun (with specific assumptions) based on a Model (potentially with some trained inputs)
run = ModelRun(
model, # TODO experiment with dwell times
20e6, # N, population of jurisdiction
t_list,
None, # tests,
rt,
case_median_age_f=lambda t: 48,
initial_compartments={"nC": 1000.0},
auto_initialize_other_compartments=
True, # By running to steady state at initial R(t=t0)
auto_calibrate=
False, # Override some model constants to ensure continuity with initial compartments
)
# Execute the run, results are a DataFrame, fig is Matplotlib Figure, ratios are key metrics
(results, ratios,
fig) = run.execute_dataframe_ratios_fig(plot=MAKE_PLOTS)
nC_peak_at = results["nC"].argmax()
h_peak_at = results["H"].argmax()
nD_peak_at = results["nD"].argmax()
h_delay = h_peak_at - nC_peak_at
d_delay = nD_peak_at - h_peak_at
h_to_nC = results["H"][h_peak_at] / results["nC"][nC_peak_at]
nD_to_h = results["nD"][nD_peak_at] / results["H"][h_peak_at]
delays.append([t_i, model.t_h(48), h_delay, h_to_nC, d_delay, nD_to_h])
assert h_delay - int(t_i + model.t_h(48) / 2) in [-2, -1, 0, 1, 2,
3] # 0 +/- 1
assert (d_delay) in [2, 1, 0] # 1 +/- 1
if MAKE_PLOTS:
fig.savefig(TEST_OUTPUT_DIR / "test_inertia_of_model.pdf",
bbox_inches="tight")
def test_demonstrate_hospitalization_delay_changes():
"""
Demonstrates that hospitalization peaks are delayed relative to new cases
by varying number of days depending on how well states were prepared at
various points in time
"""
if MAKE_PLOTS:
return
t_list = np.linspace(0, 230, 230 + 1)
sets = {
"early": {
"states": ["NY", "NJ", "CT"],
"delay": 0
},
"late": {
"states": ["FL", "TX", "GA", "CA", "AZ"],
"delay": 12
},
"steady": {
"states": ["PA", "IL"],
"delay": 7
},
}
model = NowcastingSEIRModel()
for name, s in sets.items():
fig, ax = plt.subplots()
for state in s["states"]:
(rt, nc, tests, h,
nd) = HistoricalData.get_state_data_for_dates(state,
t_list,
no_functions=True)
fh = h / (nc * model.t_i)
fd = (nd * model.t_h()) / h
pos = nc / tests
hdelay = h.shift(s["delay"])
plt.scatter(hdelay.values, nd.values, marker=".", label=state)
plt.plot([hdelay.values[-1]], [nd.values[-1]], marker="o")
plt.plot([1e3, 1e4], [2e1, 2e2], linestyle="--")
plt.xlabel(f"Hospitalizations (delayed %s days)" % s["delay"])
plt.ylabel("new Deaths")
plt.yscale("log")
plt.xscale("log")
# plt.ylim((0.01, 1.0))
fig.legend()
fig.savefig(TEST_OUTPUT_DIR /
(f"test_ratio_evolution_%s_states.pdf" % name))