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()
summed_wtp_soc = np.median(evaluated_WTP_pc[50, "random"] -
evaluated_WTP_p[50, "random"],
axis=0)
# plot_component_breakdowns(summed_wtp_soc, summed_wtp_priv, "social", "private", semilogy = False)
# plt.show()
# # dist x age
per_district_WTP_percentiles = {
(district, *parse_tag(tag)): np.percentile(wtp[0, :, :], [50, 5, 95],
axis=0)
for ((district, tag), wtp) in district_WTP.items()
}
per_district_YLL_percentiles = {
(district, *parse_tag(tag)): np.percentile(yll, [50, 5, 95], axis=0)
for ((district, tag), yll) in district_YLL.items()
}
# plot_district_age_distribution(per_district_WTP_percentiles, "per capita WTP (USD)", "D", N_jk = N_jk_dicts)
# plt.show()
# plot_district_age_distribution(per_district_YLL_percentiles, "YLL" , "o")
# plt.show()
# demand curves
ranking_provider = get_wtp_ranking(district_WTP)
demand_curves(ranking_provider, "mortality")
plt.show()
demand_curves(ranking_provider, "random")
plt.show()
demand_curves(ranking_provider, "contact")
plt.show()