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at_analysis.py
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at_analysis.py
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#%%
import altair as alt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from dose_redistributing_methods import redistribute_doses
from data_loading import *
from utility import write_img_to_file, write_to_file
from pathlib import Path
sns.set_theme()
PLOT_FOLDER = Path("Y:/GitRepos/oerpli.github.io/fdf")
ALTAIR_WIDTH = 500
plt.rcParams["figure.figsize"] = [10, 5]
deaths_at = get_deaths_by_age_at()
def save_fig(ax, name, alt_text=None, caption=None):
path = vlbg_img / f"{name}.png"
plt.savefig(path)
write_img_to_file(vlbg_report, name, path, alt_text=alt_text, caption=caption)
def save_table(df: pd.DataFrame, name: str):
df.columns = map(lambda x: " ".join(x), df.columns)
write_to_file(vlbg_report, name, df.to_markdown())
#%% Get Data Austria
df_full = get_vaccinations_at()
population = get_demographics_at()
pt = pd.DataFrame(population.unstack()).T
def get_avg_immunity(df, pop):
df = df.cumsum().copy()
df[D1] -= df[D2] # D1 contains now only 1st dose instead
imm_p = df[D1].copy() * 0
imm_p += (df[D1] * 0.89) / pop
imm_p += (df[D2] * 0.95) / pop
return imm_p
def create_altair_plot_immunity(imm, title, name=None):
chart_data = pd.melt(
imm.reset_index(),
id_vars="Date",
value_vars=imm.columns,
var_name="Age Group",
value_name="Immunity",
)
chart = (
alt.Chart(chart_data, width=ALTAIR_WIDTH)
.mark_line(clip=True)
.encode(
x="Date",
y=alt.Y("Immunity", scale=alt.Scale(domain=(0, 1))),
color="Age Group",
strokeDash="Age Group",
)
.properties(title=f"Estimated Immunity: {title}")
.interactive()
)
if name:
chart.save(str(PLOT_FOLDER / f"imm_{name}.json"))
return chart
#%%
def create_altair_plot_vacc(vacc, title, name=None):
chart_data = pd.melt(
vacc.reset_index(),
id_vars="Date",
value_vars=vacc.columns,
var_name="Age Group",
value_name="Vaccinations",
)
chart = (
alt.Chart(chart_data, width=ALTAIR_WIDTH)
.mark_line(clip=True)
.encode(
x="Date",
y="Vaccinations",
color="Age Group",
strokeDash="Age Group",
)
.properties(title=f"Number of vaccinations: {title}")
.interactive()
)
if name:
chart.save(str(PLOT_FOLDER / f"vacc_{name}.json"))
return chart
#%%
def create_altair_plot_weighted_immunity(imm_w, title, name=None):
chart_data = pd.melt(
imm_w.reset_index(),
id_vars="Date",
value_vars=imm_w.columns,
var_name="Weighted by",
value_name="Immunity",
)
chart = (
alt.Chart(chart_data, width=ALTAIR_WIDTH)
.mark_line(clip=True)
.encode(
x="Date",
y=alt.Y("Immunity", scale=alt.Scale(domain=(0, 1))),
color="Weighted by",
strokeDash="Weighted by",
)
.properties(title=f"Weighted average immunity: {title}")
.interactive()
)
if name:
chart.save(str(PLOT_FOLDER / f"imm_w{name}.json"))
return chart
#%%
def create_altair_plot_at_least_1d(df, title, name=None):
chart_data = pd.melt(
df.reset_index(),
id_vars=["Date"],
value_vars=df.columns,
var_name="Age group",
value_name="Fraction",
)
chart = (
alt.Chart(chart_data, width=ALTAIR_WIDTH)
.mark_line(clip=True)
.encode(
x="Date",
y=alt.Y("Fraction", scale=alt.Scale(domain=(0, 1))),
color="Age group",
strokeDash="Age group",
)
.properties(title=f"At least one dose: {title}")
.interactive()
)
if name:
chart.save(str(PLOT_FOLDER / f"_1d_{name}.json"))
return chart
#%%
def calc_weighted_immunity(imm, weights) -> pd.DataFrame:
dfs = []
for name, weight in weights.items():
imm_w = (imm * weight).sum(axis=1) / weight.sum()
df = pd.DataFrame({name: imm_w})
dfs.append(df)
a, *bs = dfs
for b in bs:
a = a.join(b)
return a
#%%
region_names = {
"Burgenland": "at-bg",
"Kärnten": "at-k",
"Niederösterreich": "at-noe",
"Oberösterreich": "at-ooe",
"Salzburg": "at-sbg",
"Steiermark": "at-stmk",
"Tirol": "at-t",
"Vorarlberg": "at-vlbg",
"Wien": "at-vienna",
"Österreich": "at",
}
def get_vaccination_rate(df, pop) -> pd.DataFrame:
vacc_rate = df.copy().cumsum()
vacc_rate[D1] /= pop
vacc_rate[D2] /= pop
return vacc_rate
regions = region_names # all regions
# regions = {"Österreich": "at"} # for debugging
for fd in [True, False]:
fds = "_fd" if fd else ""
for nice, short in regions.items():
df = df_full[nice] # get subset of region
pop = pt[nice].loc[0] # get population of region
dfv = df[[D1, D2]] # 1D,2D get vaccination data of region
# Choose one from the following two
rd = redistribute_doses(dfv, pop, distr_first=False, fractional_dosing=fd)
# This method basically ignores members of "critical groups" except old people
# Gives nicer but likely wrong numbers.
# rd = redistribute_doses(dfv, pop, distr_first=True) # also reassign first doses based on prio
imm_normal = get_avg_immunity(dfv, pop)
imm_fdf = get_avg_immunity(rd, pop)
print(f"FD {fd}: {rd.sum().sum()}")
print(f"FD {fd}: {dfv.sum().sum()}")
weightings = {
"Population": pop, # calc immunity on average
"Death distribution": deaths_at.loc["Deaths"], # weighted by deaths
}
imm_w_n = calc_weighted_immunity(imm_normal, weightings)
imm_w_fdf = calc_weighted_immunity(imm_fdf, weightings)
imm_w = imm_w_n.join(imm_w_fdf, rsuffix=" (FDF)", lsuffix=" (Normal)")
per_pop_c = imm_w.columns[0::2]
per_death_c = imm_w.columns[1::2]
total = dfv[D1] + dfv[D2]
# Avg immunity plot per age group, normal and FDF
create_altair_plot_immunity(imm_normal, f"{nice}", f"real_{short}{fds}")
create_altair_plot_immunity(imm_fdf, f"{nice} (FDF)", f"fdf_{short}{fds}")
# FDF + FD Comparison
vrr = get_vaccination_rate(dfv, pop)
vr_fdf = get_vaccination_rate(rd, pop)
# vr_fdf_fd = get_vaccination_rate(rdfd, pop)
create_altair_plot_at_least_1d(vrr[D1], f"{nice} (normal)")
create_altair_plot_at_least_1d(vr_fdf[D1], f"{nice} (FDF)")
# create_altair_plot_at_least_1d(vr_fdf_fd[D1], f"{nice} (FDF + FD)")
# Weighted average immunity for FDF, population and death distribution weighted
create_altair_plot_weighted_immunity(
imm_w[per_pop_c], f"{nice}", f"p_{short}{fds}"
)
create_altair_plot_weighted_immunity(
imm_w[per_death_c], f"{nice}", f"d_{short}{fds}"
)
# Vaccination progress, Total, Real, alternative FDF data
create_altair_plot_vacc(total.cumsum(), f"{nice} Total", f"real_t_{short}{fds}")
create_altair_plot_vacc(
dfv[D1].cumsum(), f"{nice} {D1}", f"real_d1_{short}{fds}"
)
create_altair_plot_vacc(
dfv[D2].cumsum(), f"{nice} {D2}", f"real_d2_{short}{fds}"
)
create_altair_plot_vacc(
rd[D1].cumsum(), f"{nice} {D1} (FDF)", f"fdf_d1_{short}{fds}"
)
create_altair_plot_vacc(
rd[D2].cumsum(), f"{nice} {D2} (FDF)", f"fdf_d2_{short}{fds}"
)
# plot immunity levels with matplotlib
ax = imm_normal.plot(title=f"Immunity {nice}")
ax.set_ylim(0, 1)
ax2 = imm_fdf.plot(title=f"FDF Immunity {nice}")
ax2.set_ylim(0, 1)
# %% Test stuff
create_altair_plot_vacc(rd[D2].cumsum(), f"{nice} {D2} (FDF)")
create_altair_plot_vacc(rd[D2].cumsum(), f"{nice} {D2} (FDF)")
create_altair_plot_vacc(rd[D2].cumsum(), f"{nice} {D2} (FDF)")
since_new_dosing = dfv.loc["2021-03-14":]
young, middle, old = ["25-34", "35-44", "45-54"], ["55-64", "65-74"], ["75-84", "85-99"]
yd1 = since_new_dosing[D1][young].sum().sum()
yd2 = since_new_dosing[D2][young].sum().sum()
md1 = since_new_dosing[D1][middle].sum().sum()
md2 = since_new_dosing[D2][middle].sum().sum()
od1 = since_new_dosing[D1][old].sum().sum()
od2 = since_new_dosing[D2][old].sum().sum()
print(yd1, yd2)
print(md1, md2)
print(od1, od2)
# %% Test germany
if False:
de = get_vaccinations_de()
guess_pop_de = pt["Österreich"].loc[0] * 10
rde = redistribute_doses(de, guess_pop_de, distr_first=True)
imm_de = get_avg_immunity(rde, guess_pop_de)
plt = create_altair_plot_immunity(imm_de, "Germany (FDF)", f"fdf_{'de'}")
display(plt)
ws = {"Population": guess_pop_de, "Deaths": deaths_at.loc["Deaths"]}
imm_w = calc_weighted_immunity(imm_de, ws)
plt = create_altair_plot_weighted_immunity(imm_w, "Germany (FDF)", f"fdf_{'de'}")
display(plt)
# %%
# %%
# create_altair_plot_weighted_immunity(imm_w[per_death_c], f"{nice}")
# %%