def demand_curves(ranking_provider,
vax_policy,
phis=[25, 50, 100, 200],
phi_benchmark=25,
N_state=N_TN):
wtp_rankings = {phi: ranking_provider(phi, vax_policy) for phi in phis}
figure = plt.figure()
lines = []
# benchmark
benchmark = wtp_rankings[phi_benchmark]
x_pop = list(
chain(*zip(benchmark.loc[0]["num_vax"].shift(1).fillna(0),
benchmark.loc[0]["num_vax"])))
y_wtp = list(
chain(*zip(benchmark.loc[0]["wtp_pc_usd"], benchmark.loc[0]
["wtp_pc_usd"])))
lines.append(
plt.plot(x_pop, y_wtp, figure=figure, color="black", linewidth=2)[0])
lines.append(plt.plot(0, 0, color="white")[0])
# plot dynamic curve
for (phi, all_wtp) in wtp_rankings.items():
daily_doses = phi * percent * annually * N_state
distributed_doses = 0
x_pop = []
y_wtp = []
t_vax = []
ranking = 0
for t in range(simulation_range):
wtp = all_wtp.loc[t].reset_index()
ranking = wtp[(wtp.index >= ranking)
& (wtp.num_vax > distributed_doses)].index.min()
if np.isnan(ranking):
break
x_pop += [distributed_doses, distributed_doses + daily_doses]
t_vax += [t, t + 1]
y_wtp += [wtp.iloc[ranking].wtp_pc_usd] * 2
distributed_doses += daily_doses
lines.append(
plt.plot(x_pop,
y_wtp,
label=f"dynamic, {vax_policy}, $\phi = ${phi}%",
figure=figure)[0])
plt.legend(lines, [f"static, t = 0, $\phi = ${phi_benchmark}%", ""] +
[f"dynamic, {vax_policy}, $\phi = ${phi}%" for phi in phis],
title="allocation",
title_fontsize="24",
fontsize="20")
plt.xticks(fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("WTP (USD)\n").xlabel("\nnumber vaccinated")
plt.ylim(0, 350)
plt.xlim(left=0, right=N_TN)
plt.show()
def plot_district_age_distribution(percentiles,
ylabel,
fmt,
phi=50,
vax_policy="random",
N_jk=None,
n=5,
district_spacing=1.5,
age_spacing=0.1,
rotation=0):
fig = plt.figure()
district_ordering = list(districts_to_run.index)[:n]
for (i, district) in enumerate(district_ordering):
ylls = percentiles[district, phi, vax_policy]
for j in range(7):
plt.errorbar(x=[district_spacing * i + age_spacing * (j - 3)],
y=ylls[1, 6 - j] * USD /
(N_jk[f"N_{6-j}"][district] if N_jk else 1),
yerr=[[(ylls[1, 6 - j] - ylls[0, 6 - j]) * USD /
(N_jk[f"N_{6-j}"][district] if N_jk else 1)],
[(ylls[2, 6 - j] - ylls[1, 6 - j]) * USD /
(N_jk[f"N_{6-j}"][district] if N_jk else 1)]],
fmt=fmt,
color=age_group_colors[6 - j],
figure=fig,
label=None if i > 0 else age_bin_labels[6 - j],
ms=12,
elinewidth=5)
plt.xticks([1.5 * _ for _ in range(n)],
district_ordering,
rotation=rotation,
fontsize="20")
plt.yticks(fontsize="20")
plt.legend(title="age bin",
title_fontsize="20",
fontsize="20",
ncol=7,
loc="lower center",
bbox_to_anchor=(0.5, 1))
ymin, ymax = plt.ylim()
plt.vlines(x=[0.75 + 1.5 * _ for _ in range(n - 1)],
ymin=ymin,
ymax=ymax,
color="gray",
alpha=0.5,
linewidths=2)
plt.ylim(ymin, ymax)
plt.gca().grid(False, axis="x")
plt.PlotDevice().title(f"\n{vax_policy} demand curves").ylabel(
f"{ylabel}\n")
def plot_state_age_distribution(percentiles,
ylabel,
fmt,
district_spacing=1.5,
n=5,
age_spacing=0.1,
rotation=0,
ymin=0,
ymax=1000):
fig = plt.figure()
state_ordering = list(
sorted(percentiles.keys(),
key=lambda k: percentiles[k][0].max(),
reverse=True))
for (i, state) in enumerate(state_ordering[:n]):
ylls = percentiles[state]
for j in range(7):
plt.errorbar(x=[district_spacing * i + age_spacing * (j - 3)],
y=ylls[0, 6 - j],
yerr=[[(ylls[0, 6 - j] - ylls[1, 6 - j])],
[(ylls[2, 6 - j] - ylls[0, 6 - j])]],
fmt=fmt,
color=agebin_colors[6 - j],
figure=fig,
label=None if i > 0 else agebin_labels[6 - j],
ms=12,
elinewidth=5)
plt.xticks([1.5 * _ for _ in range(n)],
state_ordering,
rotation=rotation,
fontsize="20")
plt.yticks(fontsize="20")
# plt.legend(title = "age bin", title_fontsize = "20", fontsize = "20", ncol = 7,
plt.legend(fontsize="20",
ncol=7,
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.vlines(x=[0.75 + 1.5 * _ for _ in range(n - 1)],
ymin=ymin,
ymax=ymax,
color="gray",
alpha=0.5,
linewidths=4)
plt.ylim(ymin, ymax)
plt.gca().grid(False, axis="x")
plt.PlotDevice().ylabel(f"{ylabel}\n")
def plot_component_breakdowns(color,
white,
colorlabel,
whitelabel,
semilogy=False,
ylabel="WTP (USD)"):
fig, ax = plt.subplots()
ax.bar(range(7),
white * USD,
bottom=color * USD,
color="white",
edgecolor=age_group_colors,
linewidth=2,
figure=fig)
ax.bar(range(7),
color * USD,
color=age_group_colors,
edgecolor=age_group_colors,
linewidth=2,
figure=fig)
ax.bar(range(7), [0],
label=whitelabel,
color="white",
edgecolor="black",
linewidth=2)
ax.bar(range(7), [0],
label=colorlabel,
color="black",
edgecolor="black",
linewidth=2)
plt.xticks(range(7), age_bin_labels, fontsize="20")
plt.yticks(fontsize="20")
plt.legend(ncol=4,
fontsize="20",
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.PlotDevice().ylabel(f"{ylabel}\n")
if semilogy: plt.semilogy()
def outcomes_per_policy(percentiles,
metric_label,
fmt,
phis=[25, 50, 100, 200],
reference=(25, "no_vax"),
reference_color=no_vax_color,
vax_policies=["contact", "random", "mortality"],
policy_colors=[
contactrate_vax_color, random_vax_color,
mortality_vax_color
],
policy_labels=[
"contact rate priority", "random assignment",
"mortality priority"
],
spacing=0.2):
fig = plt.figure()
md, lo, hi = percentiles[reference]
*_, bars = plt.errorbar(x=[0],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt=fmt,
color=reference_color,
label="no vaccination",
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.hlines(md,
xmin=-1,
xmax=5,
linestyles="dotted",
colors=reference_color)
for (i, phi) in enumerate(phis, start=1):
for (j, (vax_policy, color, label)) in enumerate(
zip(vax_policies, policy_colors, policy_labels)):
md, lo, hi = death_percentiles[phi, vax_policy]
*_, bars = plt.errorbar(x=[i + spacing * (j - 1)],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt=fmt,
color=color,
label=label if i == 0 else None,
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.legend(ncol=4,
fontsize="20",
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.xticks(range(len(phis) + 1),
[f"$\phi = {phi}$%" for phi in ([0] + phis)],
fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel(f"{metric_label}\n")
plt.gca().grid(False, axis="x")
ymin, ymax = plt.ylim()
plt.vlines(x=[0.5 + _ for _ in range(len(phis))],
ymin=ymin,
ymax=ymax,
color="gray",
alpha=0.5,
linewidths=2)
plt.ylim(ymin, ymax)
plt.xlim(-0.5, len(phis) + 1.5)
dT_conf_scaled_TT[idx] / N_TT,
color=IN_color,
label="India (raw)",
figure=fig,
alpha=0.5,
marker="o",
s=10,
zorder=10)
plot_TT, = plt.plot(idx,
dT_conf_scaled_smooth_TT[idx] / N_TT,
color=IN_color,
label="India (smoothed)",
figure=fig,
zorder=10,
linewidth=2)
plt.xticks(fontsize="20", rotation=0)
plt.yticks(fontsize="20")
plt.legend([scatter_TN, plot_TN, scatter_TT, plot_TT], [
"Tamil Nadu (raw)", "Tamil Nadu (smoothed)", "India (raw)",
"India (smoothed)"
],
fontsize="20",
ncol=4,
framealpha=1,
handlelength=0.75,
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.gca().xaxis.set_major_formatter(plt.bY_FMT)
plt.gca().xaxis.set_minor_formatter(plt.bY_FMT)
plt.xlim(left=pd.Timestamp("March 1, 2020"),
right=pd.Timestamp("April 15, 2021"))
*_, bars = plt.errorbar(
x=[i],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt="o",
color=clr,
label=None if i > 0 else [
"contact rate prioritized", "random assignment",
"mortality prioritized"
][dx],
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.xticks(
range(len(metrics)),
[f"{phi}%" for phi in (100 * (10**np.linspace(-1, 1, 11))).round(0)],
fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("deaths\n").xlabel(
"\npercentage of population vaccinated annually")
# plt.ylim(200, 450)
plt.legend(fontsize="20")
plt.show()
# evaluated_YLL_percentiles = {k: np.percentile(v, [5, 50, 95]) for (k, v) in evaluated_YLLs.items() if "ve70" in k or "novaccination" in k}
# contact_percentiles = {k: v for (k, v) in evaluated_YLL_percentiles.items() if "contact" in k}
# random_percentiles = {k: v for (k, v) in evaluated_YLL_percentiles.items() if "random" in k}
# mortality_percentiles = {k: v for (k, v) in evaluated_YLL_percentiles.items() if "mortality" in k}
# novax_percentiles = {k: v for (k, v) in evaluated_YLL_percentiles.items() if "novacc" in k}
# fig = plt.figure()
N_natl = districts_to_run.N_tot.sum()
figure = plt.figure()
x_pop = list(
chain(*zip(
all_tev_50.loc[0]["num_vax"].shift(1).fillna(0) * 100 /
N_natl, all_tev_50.loc[0]["num_vax"] * 100 / N_natl)))
y_tev = list(
chain(*zip(all_tev_50.loc[0]["pc_tev_usd"], all_tev_50.loc[0]
["pc_tev_usd"])))
static, = plt.plot(x_pop,
y_tev,
figure=figure,
color="grey",
linewidth=2)
lines = [static]
plt.xticks(fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("TEV (USD)\n").xlabel(
"\npercentage of country vaccinated")
# plt.ylim(0, 1600)
# plt.xlim(left = 0, right = 100)
lines.append(plt.plot(0, 0, color="white")[0])
# plot dynamic curve
phis = [25, 50, 100, 200]
for (phi, all_tev) in zip(
phis, [all_tev_25, all_tev_50, all_tev_100, all_tev_200]):
daily_doses = phi * percent * annually * N_natl
distributed_doses = 0
x_pop = []
x=[i + 0.2 * (dx - 1)],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt="o",
color=clr,
label=None if i > 0 else [
"contact rate prioritized", "random assignment",
"mortality prioritized"
][dx],
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.xticks(list(range(-1, len(metrics))),
[f"$\phi = {phi}$%" for phi in [0, 25, 50, 75, 100, 200, 400]],
fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("\ndeaths")
plt.legend(fontsize="20", ncol=4, loc="lower center", bbox_to_anchor=(0.5, 1))
plt.show()
evaluated_YLL_percentiles = {
k: np.percentile(v, [5, 50, 95])
for (k, v) in evaluated_YLLs.items() if "ve70" in k or "novaccination" in k
}
contact_percentiles = {
k: v
for (k, v) in evaluated_YLL_percentiles.items() if "contact" in k
}
random_percentiles = {
PrD.plot()
plt.legend(title="Age category",
title_fontsize=18,
fontsize=16,
framealpha=1,
handlelength=1)
plt.xlim(right=pd.Timestamp("Jan 01, 2022"))
plt.PlotDevice()\
.xlabel("\nDate")\
.ylabel("Probability\n")
plt.subplots_adjust(left=0.12, bottom=0.12, right=0.94, top=0.96)
plt.gca().xaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.gca().xaxis.set_minor_formatter(mpl.ticker.NullFormatter())
plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
plt.xticks(fontsize="16")
plt.yticks(fontsize="16")
plt.gca().xaxis.grid(True, which="major")
plt.semilogy()
plt.ylim(bottom=1e-7)
plt.show()
PrD = pd.DataFrame(prob_death).set_index(
pd.date_range(start=simulation_start, freq="D", periods=len(prob_death)))
plt.plot(PrD, color=TN_color, linewidth=2, label="probability of death")
plt.xlim(left=pd.Timestamp("Jan 01, 2021"), right=pd.Timestamp("Jan 01, 2022"))
plt.PlotDevice().ylabel("log-probability\n")
# plt.subplots_adjust(left = 0.12, bottom = 0.12, right = 0.94, top = 0.96)
plt.gca().xaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.gca().xaxis.set_minor_formatter(mpl.ticker.NullFormatter())
plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())