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covid_plot_deaths.py
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covid_plot_deaths.py
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import os
import numpy as np
import pandas as pd
from pandas.tseries.offsets import MonthEnd
import utils_thai
from covid_data_api import get_ifr
from covid_data_api import ihme_dataset
from covid_plot_utils import plot_area
from covid_plot_utils import source
from utils_pandas import cum2daily
from utils_pandas import cut_ages
from utils_pandas import cut_ages_labels
from utils_pandas import get_cycle
from utils_pandas import import_csv
from utils_pandas import increasing
from utils_pandas import normalise_to_total
from utils_pandas import perc_format
from utils_pandas import rearrange
from utils_pandas import topprov
from utils_pandas import weekly2daily
from utils_scraping import logger
from utils_scraping import remove_prefix
from utils_thai import area_crosstab
from utils_thai import AREA_LEGEND
from utils_thai import DISTRICT_RANGE
from utils_thai import DISTRICT_RANGE_SIMPLE
from utils_thai import FIRST_AREAS
from utils_thai import join_provinces
from utils_thai import trend_table
AGE_BINS = [10, 20, 50, 60, 70]
DEATH_COLS = ['0-9', '10-19', '20-49', '50-59', '60-69', '70+']
DEATH_COLS = [f"Deaths Age {age}" for age in DEATH_COLS]
def get_by_prov():
cases = import_csv("cases_by_province")
# fill in missing provinces
cases_pivot = cases.fillna(0).pivot_table(index="Date", columns="Province", values="Cases")
# fill in missing days
all_days = pd.date_range(cases_pivot.index.min(), cases_pivot.index.max(), name="Date")
cases_pivot = cases_pivot.reindex(all_days).fillna(0) # put in missing days with NaN
cases = cases.set_index(["Date", "Province"]).combine_first(cases_pivot.unstack().to_frame("Cases"))
cases = join_provinces(cases, "Province", ["Health District Number", "region"]) # to fill in missing health districts
# cases = cases.fillna(0) # all the other values
ifr = get_ifr()
cases = cases.join(ifr[['ifr', 'Population', 'total_pop']], on="Province")
return cases
def save_deaths_plots(df: pd.DataFrame) -> None:
#######################
# Cases by provinces
#######################
cases = get_by_prov()
ihme = ihme_dataset(check=False)
cases_region = cases.reset_index()
pop_region = pd.crosstab(cases_region['Date'], cases_region['region'], values=cases_region["Population"], aggfunc="sum")
cases_region = pd.crosstab(cases_region['Date'], cases_region['region'], values=cases_region["Cases"], aggfunc="sum")
def cases_per_capita(col, per=100000):
def func(adf):
return adf[col] / adf['Population'] * per
return func
####################
# Deaths
####################
# TODO: predict median age of death based on population demographics
# I think they are the same really. or pretty close
deaths_reason = df['Deaths Risk Under 60 Comorbidity None'].combine_first(
df['Deaths Comorbidity None']).to_frame('Deaths Under 60yo without Underlying Diseases')
deaths_reason["Deaths"] = df["Deaths"]
deaths_reason['Deaths Risk Family'] = df['Deaths Risk Family']
if not ihme.empty:
deaths_reason["Estimated Total Deaths (IHME)"] = ihme['seir_daily_mean']
# deaths_reason["Estimated Deaths Max (IHME)"] = ihme['seir_daily_upper']
# deaths_reason["Estimated Deaths Min (IHME)"] = ihme['seir_daily_lower']
# cols = [
# 'Deaths',
# 'Deaths Risk Family',
# 'Deaths Under 60yo without Underlying Diseases',
# # 'Deaths Risk Unvaccinated',
# ]
# legends = [
# 'Deaths',
# 'Deaths Infected from Family',
# 'Deaths Under 60yo without Underlying Diseases',
# # 'Deaths with no history of vaccination',
# ]
plot_area(df=deaths_reason,
title='Covid Deaths - Thailand',
png_prefix='deaths_reason', cols_subset=list(deaths_reason.columns),
actuals=['Deaths'],
# box_cols=[c for c in deaths_reason.columns if "IHME" in str(c)],
ma_days=7,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
footnote_left=f'{source}Data Source: CCSA Daily Briefing, IHME')
if 'Deaths Age 0-4' in df.columns:
df = df.combine_first((df['Deaths Age 0-4'] + df['Deaths Age 5-9']).to_frame('Deaths Age 0-9'))
df = df.combine_first((df['Deaths Age 20-29'] + df['Deaths Age 30-39'] +
df['Deaths Age 40-49']).to_frame('Deaths Age 20-49'))
df = df.combine_first((df['Cases Age 20-29'] + df['Cases Age 30-39'] + df['Cases Age 40-49']).to_frame('Cases Age 20-49'))
df['Deaths Comorbidity Aged 70+'] = df['Deaths Age 70+']
df['Deaths Comorbidity Aged 60+'] = df['Deaths Age 70+'] + df['Deaths Age 60-69']
df['Deaths Comorbidity Under 60 without Comorbidity'] = df['Deaths Risk Under 60 Comorbidity None'].combine_first(
df["Deaths Comorbidity None"])
df['Deaths Comorbidity Under 60 with Comorbidity'] = df['Deaths'] - \
df['Deaths Comorbidity Aged 60+'] - df['Deaths Comorbidity Under 60 without Comorbidity']
# df['Deaths Comorbidity Under 60 with Comorbidity'] = df["Deaths Risk Under 60 Comorbidity "]
cols = [c for c in df.columns if "Deaths Comorbidity" in str(c) and "None" not in str(c)]
# Just get ones that are still used. and sort by top
cols = list(df.iloc[-50:][cols].mean(axis=0).dropna().sort_values(ascending=False).index)
legends = [col.replace("Deaths Comorbidity ", "").replace(
"Hypertension", "High Blood Pressure (Hypertension)").replace(
"Hyperlipidemia", "High Cholesterol (Hyperlipidemia)").replace(
"Cerebrovascular", "Stroke (Cerebrovascular)") for col in cols]
deaths = df["Deaths"].replace(0, np.nan)
plot_area(df=df[cols].div(deaths, axis=0) * 100,
title='% of Covid Deaths - Comorbidities - Thailand',
legends=legends,
png_prefix='deaths_comorbidities', cols_subset=cols,
# actuals=['Deaths'],
ma_days=14,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
y_formatter=perc_format,
footnote_left=f'{source}Data Source: CCSA Daily Briefing',
footnote="A Comorbidity is not the cause of death but a condition that\n"
"may increase an individuals risk of death from Covid")
cols = [c for c in df.columns if "Deaths Risk" in str(c) and "60" not in str(c) and "MA" not in c]
# Just get ones that are still used. and sort by top
cols = list(df.iloc[-80:][cols].mean(axis=0).dropna().sort_values(ascending=False).index)
legends = [col.replace("Deaths Risk ", "").replace(
"Others", "Other People").replace(
"Location", "Live/go to an epidemic area") for col in cols]
plot_area(df=df[cols].div(deaths, axis=0) * 100,
title='% of Covid Deaths - Risks - Thailand',
legends=legends,
png_prefix='deaths_risk', cols_subset=cols,
# actuals=['Deaths'],
ma_days=21,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
y_formatter=perc_format,
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
cols = [
'Deaths',
'Deaths Risk Unvaccinated',
]
legends = [
'Deaths',
'Deaths with no history of vaccination',
]
plot_area(df=df,
title='Covid Deaths Unvaccinated - Thailand',
legends=legends,
png_prefix='deaths_unvaccinated', cols_subset=cols,
actuals=['Deaths'],
ma_days=7,
kind='area', stacked=False, percent_fig=True,
cmap='tab10',
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
df['Deaths Age Median (MA)'] = df['Deaths Age Median'].rolling('7d').median().shift(-6)
df['Deaths Age Min (MA)'] = df['Deaths Age Min'].rolling('7d').min().shift(-6)
df['Deaths Age Max (MA)'] = df['Deaths Age Max'].rolling('7d').max().shift(-6)
cols = [
'Deaths Age Median (MA)',
'Deaths Age Max (MA)',
'Deaths Age Min (MA)',
]
legends = [
'Median Age of Deaths',
'Maximum Age of Deaths',
'Minimum Age of Deaths',
]
plot_area(df=df,
title='Covid Deaths Age Range - Thailand',
legends=legends,
highlight=['Deaths Age Median (MA)'],
between=['Deaths Age Max', 'Deaths Age Min'],
png_prefix='deaths_age', cols_subset=cols,
ma_days=None,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
# cols = rearrange([f'Deaths Area {area}' for area in DISTRICT_RANGE], *FIRST_AREAS)
# plot_area(df=df,
# title='Covid Deaths by Health District - Thailand',
# legends=AREA_LEGEND,
# png_prefix='deaths_by_area', cols_subset=cols,
# ma_days=7,
# kind='area', stacked=True, percent_fig=True,
# cmap='tab20',
# footnote_left=f'{source}Data Source: CCSA Daily Briefing')
by_region = cases.reset_index()
by_region = pd.crosstab(by_region['Date'], by_region['region'], values=by_region['Deaths'], aggfunc="sum")
plot_area(df=by_region / pop_region * 10**6,
title='Covid Deaths/1M - by Region - Thailand',
png_prefix='deaths_region', cols_subset=utils_thai.REG_COLS, legends=utils_thai.REG_LEG,
ma_days=7,
kind='line', stacked=False, percent_fig=False, mini_map=True,
cmap=utils_thai.REG_COLOURS,
periods_to_plot=['3', '4'],
table=trend_table(cases['Deaths'], sensitivity=25, style="green_down", ma_days=7),
footnote='Table of latest Deaths and regional 7 day trend per 1M',
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
by_region['Deaths'] = df['Deaths']
plot_area(df=by_region,
title='Covid Deaths - by Region - Thailand',
png_prefix='deaths_region_stacked', cols_subset=utils_thai.REG_COLS, legends=utils_thai.REG_LEG,
ma_days=7,
kind='area', stacked=True, percent_fig=True, mini_map=True,
periods_to_plot=['3', '4'],
# unknown_name="Imported/Prisons", unknown_total="Deaths", # I don't think deaths get seperated
cmap=utils_thai.REG_COLOURS,
footnote_left=f'{source}Data Source: MOPH Covid-19 Dashboard')
top5 = cases.pipe(topprov,
cases_per_capita("Deaths", 10**6),
name="Province Cases",
other_name="Other Provinces",
num=7)
plot_area(df=top5,
title='Covid Deaths/1M - Top Provinces - Thailand',
png_prefix='deaths_prov_top', cols_subset=top5.columns.to_list(),
ma_days=14,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
periods_to_plot=['3', '4'],
footnote_left=f'{source}Data Sources: CCSA Daily Briefing\n API: Daily Reports of COVID-19 Infections')
top5 = cases.pipe(topprov,
increasing(cases_per_capita("Deaths", 10**6), 14),
name="Province Cases",
other_name="Other Provinces",
num=7)
plot_area(df=top5,
title='Covid Deaths/1M - Trending Up Provinces - Thailand',
png_prefix='deaths_prov_increasing', cols_subset=top5.columns.to_list(),
ma_days=14,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
periods_to_plot=['3', '4'],
footnote_left=f'{source}Data Sources: CCSA Daily Briefing\n API: Daily Reports of COVID-19 Infections')
# Work out Death ages from CFR from situation reports
age_ranges = ["15-39", "40-59", "60-"]
cols = [f'W3 CFR {age}' for age in age_ranges]
plot_area(df=df,
title='Covid CFR since 2021-04-01 - Thailand',
png_prefix='deaths_w3cfr', cols_subset=cols,
ma_days=None,
kind='line', stacked=False, percent_fig=False,
cmap='tab10',
footnote="CFR: Case Fatality Rate is dependent on testing so doesn't show risk of death",
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
#ages = ["Age 0-14", "Age 15-39", "Age 40-59", "Age 60-"]
ages = cut_ages_labels([15, 40, 60], "Cases Age")
# Put unknowns into ages based on current ratios. But might not be valid for prison unknowns?
# w3_cases = df[ages + ['Cases', 'Deaths']].pipe(normalise_to_total, ages, "Cases")
w3_cases = df[ages + ['Cases', 'Deaths']]
cols = ages
plot_area(df=w3_cases,
title='Covid Cases by Age - Thailand',
png_prefix='cases_ages2', cols_subset=cols,
unknown_name='Cases Unknown Age', unknown_total='Cases', unknown_percent=False,
ma_days=7,
kind='area', stacked=True, percent_fig=True,
cmap=get_cycle('summer_r', len(cols), extras=["gainsboro"]),
footnote_left=f'{source}Data Source: API: Daily Reports of COVID-19 Infections')
case_ages_cum = w3_cases["2021-04-01":].cumsum()
# work out ages of deaths from cfr
# CFR * cases = deaths
for ages_range in age_ranges:
case_ages_cum[f"Deaths Age {ages_range} Cum"] = df[f"W3 CFR {ages_range}"].rolling(
21, min_periods=13,
center=True).mean() / 100 * case_ages_cum[f"Cases Age {ages_range.replace('60-', '60+')}"].rolling(
7, min_periods=3, center=True).mean()
# Not used and gets an error
# deaths_by_age = cum2daily(case_ages_cum)
# death_cols = [f'Deaths Age {age}' for age in age_ranges]
# deaths_by_age['Deaths'] = df['Deaths']
# deaths_by_age['Deaths Ages Median'] = deaths_by_age['Deaths'].rolling(7, min_periods=3, center=True).mean()
# deaths_by_age['Deaths Ages Sum'] = deaths_by_age[death_cols].sum(axis=1)
# deaths_by_age = deaths_by_age.pipe(normalise_to_total, death_cols, 'Deaths Ages Median')
# cols = death_cols + ['Deaths Ages Median', 'Deaths Ages Sum']
# plot_area(df=deaths_by_age,
# title='Covid Deaths Age Range - Thailand',
# png_prefix='deaths_age_bins', cols_subset=cols,
# ma_days=None,
# kind='line', stacked=False, percent_fig=False,
# cmap='tab10',
# footnote_left=f'{source}Data Source: CCSA Daily Briefing')
# don't use this chart anymore since we can get this data from the dashboard
# plot_area(df=deaths_by_age,
# png_prefix='deaths_age_est',
# cols_subset=death_cols,
# title='Thailand Covid Death Age Distribution\nEstimation from smoothed CFR from daily situation reports',
# kind='area',
# stacked=True,
# percent_fig=True,
# ma_days=None,
# cmap=get_cycle('summer_r', len(cols) + 1))
# Plot death ages from dashboard data
plot_area(df=df,
title='Covid Deaths Age Distribution - Thailand',
png_prefix='deaths_age_dash', cols_subset=DEATH_COLS,
unknown_name='Deaths Unknown Age', unknown_total='Deaths', unknown_percent=False,
ma_days=7,
kind='area', stacked=True, percent_fig=True, clean_end=True,
cmap=get_cycle('summer_r', len(DEATH_COLS), extras=["gainsboro"]),
footnote_left=f'{source}Data Source: MOPH Covid-19 Dashboard')
# Do CFR for all regions. show vaccine effectiveness
ttd = 17 # TODO: should change over time
cfr_warning = "CFR is a poor estimate of IFR (risk of death if infected) due to low detection rates\n" + \
f"Deaths shifted by {ttd} days, est detection to death dur."
# TODO: use actual med time to death from briefing. It changes slightly over time.
def cfr_est(df, cases_col="Cases"): return df['Deaths'].rolling(
90).mean() / df[cases_col].shift(ttd).rolling(90).mean() * 100
by_region = cases[['Cases', 'Deaths', "region"]].groupby(["Date", "region"]).sum()
cfr_region = by_region.groupby("region", group_keys=False).apply(cfr_est).to_frame("CFR Est").reset_index()
cfr_region = pd.crosstab(cfr_region['Date'], cfr_region['region'], values=cfr_region["CFR Est"], aggfunc="sum")
cfr_region = cfr_region.replace(float("inf"), np.nan)
plot_area(df=cfr_region,
title='Case Fatality Rate (CFR) - Last 90 days - by Region - Thailand',
png_prefix='cfr_region', cols_subset=utils_thai.REG_COLS, legends=utils_thai.REG_LEG,
ma_days=0,
kind='line', stacked=False, percent_fig=False, mini_map=True,
cmap=utils_thai.REG_COLOURS,
y_formatter=perc_format,
# table=trend_table(cases['Cases'], sensitivity=25, style="green_down"),
footnote=cfr_warning,
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
case_ages = df[cut_ages_labels(AGE_BINS, "Cases Age")]
death_ages = df[cut_ages_labels(AGE_BINS, "Deaths Age")]
death_ages.columns = cut_ages_labels(AGE_BINS, "Age")
case_ages.columns = cut_ages_labels(AGE_BINS, "Age")
cfr_age = death_ages.combine(case_ages, lambda d, c: d.rolling(90).mean() / c.shift(ttd).rolling(90).mean() * 100)
plot_area(df=cfr_age,
title='Case Fatality Rate (CFR) - Last 90 days - by Age - Thailand',
png_prefix='cfr_age', cols_subset=list(reversed(cfr_age.columns)),
ma_days=0,
kind='line', stacked=False, percent_fig=False,
# cmap=utils_thai.REG_COLOURS,
y_formatter=perc_format,
# table=trend_table(cases['Cases'], sensitivity=25, style="green_down"),
footnote=cfr_warning,
footnote_left=f'{source}Data Source: MOPH Covid-19 Dashboard')
cfr_ifr = cfr_est(df).to_frame("Estimated CFR (90 days avg)")
cfr_ifr["Estimated CFR inc ATK (90d)"] = df['Deaths'].rolling(
90).mean() / (df['Cases'] + df['ATK']).shift(ttd).rolling(90).mean() * 100
cfr_ifr['Estimated IFR (IHME)'] = (ihme['infection_fatality'] * 100)
plot_area(df=cfr_ifr,
title='Case Fatality Rate (CFR) vs Infection Fatality Rate (IFR) - Estimated - Thailand',
png_prefix='cfr_ifr', cols_subset=list(cfr_ifr.columns),
ma_days=0,
kind='line', stacked=False, percent_fig=False,
cmap="tab10",
y_formatter=perc_format,
footnote=cfr_warning,
footnote_left=f'{source}Data Source: CCSA Daily Briefing, IHME')
def save_excess_death_plots(df):
cases = get_by_prov()
# Excess Deaths
# TODO: look at causes of death
# - https://data.humdata.org/dataset/who-data-for-thailand
# - https://en.wikipedia.org/wiki/Health_in_Thailand
# - pm2.5?
# - change in reporting?
# Just normal ageing population
# Take avg(2015-2019)/(2021) = p num. (can also correct for population changes?)
def calc_pscore(adf):
months = adf.groupby(["Year", "Month"], as_index=False).sum(
numeric_only=True).pivot(columns="Year", values="Deaths", index="Month")
death3_avg = months[years3].mean(axis=1)
death3_min = months[years3].min(axis=1)
death3_max = months[years3].max(axis=1)
death5_avg = months[years5].mean(axis=1)
death5_min = months[years5].min(axis=1)
death5_max = months[years5].max(axis=1)
result = pd.DataFrame()
for year in range(2020, months.columns.max() + 1):
res = pd.DataFrame()
res['Excess Deaths'] = (months[year] - death5_avg)
res['P-Score'] = res['Excess Deaths'] / death5_avg * 100
res['Pre Avg'], res['Pre Min'], res['Pre Max'] = death3_avg, death3_min, death3_max
res['Pre 5 Avg'], res['Pre 5 Min'], res['Pre 5 Max'] = death5_avg, death5_min, death5_max
res['Deaths All Month'] = months[year]
for y in range(2012, months.columns.max() + 1):
res[f'Deaths {y}'] = months[y]
res['Date'] = pd.to_datetime(f'{year}-' + res.index.astype(int).astype(str),
format='%Y-%m') + MonthEnd(0)
result = result.combine_first(res.reset_index().set_index("Date"))
result = result.dropna(subset=['P-Score'])
return result.drop(columns=["Month"])
excess = import_csv("deaths_all", dir="inputs/json", date_cols=[])
excess = join_provinces(excess, 'Province', ['region', 'Health District Number'])
years5 = list(range(2015, 2020))
years3 = [2015, 2016, 2017, 2018]
all = calc_pscore(excess)
all['Deaths Covid'] = df['Deaths'].groupby(pd.Grouper(freq='ME')).sum()
all['Deaths (ex. Known Covid)'] = all['Deaths All Month'] - all['Deaths Covid']
all['Deaths 2021 (ex. Known Covid)'] = all['Deaths 2021'] - all['Deaths Covid']
all['Expected Deaths'] = all['Pre 5 Avg'] + all['Deaths Covid']
all['Deviation from expected Deaths'] = (all['Excess Deaths'] - all['Deaths Covid']) / all['Pre Avg'] * 100
legends = [
'Non-Covid Deaths deviation from Normal Deaths',
'All Deaths deviation from mean Normal Deaths',
]
plot_area(df=all, png_prefix='deaths_pscore',
title='Mortaliy (all causes) compared to Previous Years - Thailand',
legends=legends,
cols_subset=['Deviation from expected Deaths', 'P-Score'],
ma_days=None,
kind='line', stacked=False, percent_fig=False, limit_to_zero=False,
cmap='tab10',
periods_to_plot=['all'],
y_formatter=perc_format,
footnote='All cause mortality compared to average for same period in 2015-2019 inc known Covid deaths.',
footnote_left=f'{source}Data Source: MOPH Covid-19 Dashboard')
cols = [f'Deaths {y}' for y in range(2012, 2021, 1)]
by_month = pd.DataFrame(all)
by_month['Month'] = by_month.index.strftime('%B')
years2020 = by_month["2020-01-01":"2021-01-01"][cols + ['Month']].reset_index().set_index("Month")
cols2021 = ['Deaths 2021', 'Deaths 2021 (ex. Known Covid)']
years2021 = by_month["2021-01-01":"2022-01-01"][cols2021 + ['Month']].reset_index().set_index("Month")
cols2022 = ['Deaths 2022']
years2022 = by_month["2022-01-01":"2023-01-01"][cols2022 + ['Month']].reset_index().set_index("Month")
cols2023 = ['Deaths 2023']
years2023 = by_month["2023-01-01":"2024-01-01"][cols2023 + ['Month']].reset_index().set_index("Month")
by_month = years2020.combine_first(years2021).combine_first(years2022).combine_first(years2023).sort_values("Date")
cols = cols + cols2021 + cols2022 + cols2023
plot_area(df=by_month,
title='Excess Deaths - Thailand',
legend_pos="lower center", legend_cols=3,
png_prefix='deaths_excess_years', cols_subset=cols,
ma_days=None,
kind='bar', stacked=False, percent_fig=False, limit_to_zero=False,
periods_to_plot=['all'],
cmap='tab10',
footnote='\n\n\n\nNote: Number of deaths from all causes compared to previous years.',
footnote_left=f'\n\n\n\n{source}Data Source: MOPH Covid-19 Dashboard')
# Test to get box plots working
# https://stackoverflow.com/questions/57466631/matplotlib-boxplot-and-bar-chart-shifted-when-overlaid-using-twinx
# fig, ax = plt.subplots(figsize=(20, 10))
# ax2 = ax.twinx()
# boxes = pan_months[[f'Deaths {y}' for y in range(2015,2020)]].transpose()
# boxes.plot.box(ax=ax2, grid=False)
# pan_months[['Deaths (ex. Known Covid)', 'Deaths Covid', ]].plot.bar(ax=ax, stacked=True, align='center', alpha=0.3)
# ax2.set_ylim(0)
# ax.set_ylim(ax2.get_ylim())
# plt.savefig("test.png")
# TODO: Why the spikes in 2018 and 2019? Is there a way to correct? Change in reporting method?
# death rates increase smoothly and so do total deaths here - https://knoema.com/atlas/Thailand/topics/Demographics/Mortality/Number-of-deaths
# why different totals?
# deaths by region 2019 - https://www.statista.com/statistics/1107886/thailand-number-of-male-deaths-by-region/
# Age Deaths
# Year
# 2002 514080 25896.0
# 2003 514080 25968.0
# 2004 514080 29951.0
# 2005 514080 34385.0
# 2006 514080 34098.0
# 2007 514080 33573.0
# 2008 514080 33743.0
# 2009 514080 33718.0
# 2010 514080 35049.0
# 2011 10024560 322167.0
# 2012 13194720 422776.0
# 2013 12323640 413373.0
# 2014 13194720 448601.0
# 2015 9519048 456391.0
# 2016 9519048 480434.0
# 2017 9519048 468911.0
# 2018 9519048 473541.0
# 2019 9519048 506211.0
# 2020 9519048 501438.0
# 2021 4759524 263005.0
# deaths causes over multiple years - https://data.worldbank.org/indicator/SH.DTH.NCOM.ZS?locations=TH
# - has suicides and road accidents also - as rate
# pneumonia? - https://nucleuswealth.com/articles/is-thailand-hiding-covid-19-cases/
# UN causes of death 2016 - https://www.who.int/nmh/countries/tha_en.pdf. total deaths - 539,000??
# road deaths? http://rvpreport.rvpeservice.com/viewrsc.aspx?report=0486&session=16
def group_deaths(excess, by, daily_covid):
cols5y = [f'Deaths {y}' for y in years5]
dfby = excess.groupby(by, observed=False).apply(calc_pscore)
covid_by = daily_covid.groupby([by, pd.Grouper(level=0, freq='ME')])['Deaths'].sum()
dfby['Deaths ex Covid'] = dfby['Deaths All Month'] - covid_by
dfby['Covid Deaths'] = covid_by
dfby = dfby.reset_index().pivot(values=["Deaths All Month", 'Deaths ex Covid', 'Covid Deaths'] + cols5y,
index="Date",
columns=by)
dfby.columns = [' '.join(c) for c in dfby.columns]
# Bar chart is not aligned right otherwise
dfby = dfby.set_index(dfby.index - pd.offsets.MonthBegin(1))
labels = list(excess[by].unique())
# Need to adjust each prev year so stacked in the right place
for i in range(1, len(labels)):
prev_bars = dfby[[f'Deaths All Month {label}' for label in labels[:i]]].sum(axis=1)
covid = dfby[f'Covid Deaths {labels[i]}']
for year in cols5y:
dfby[f'{year} {labels[i]}'] += prev_bars.add(covid, fill_value=0)
return dfby, labels
# Do comparison bar charts to historical distribution of years
pan_months = pd.DataFrame(all)
pan_months = pan_months.set_index(pan_months.index - pd.offsets.MonthBegin(1))
# pan_months['Month'] = pan_months['Date'].dt.to_period('M')
by_region, regions = group_deaths(excess, "region", cases)
# # Get covid deaths by region
# covid_by_region = cases.groupby([pd.Grouper(level=0, freq='ME'), "region"])['Deaths'].sum()
# # fix up dates to start on 1st (for bar graph)
# covid_by_region = covid_by_region.reset_index("region")
# covid_by_region = covid_by_region.set_index(covid_by_region.index - pd.offsets.MonthBegin(1))
# by_region = by_region.combine_first(covid_by_region.pivot(values="Deaths", columns="region").add_prefix("Covid Deaths "))
by_age = excess.pipe(cut_ages, [10, 20, 30, 40, 50, 60, 70])
# by_age = excess.pipe(cut_ages, [15, 65, 75, 85])
new_cols = dict({a: remove_prefix(a, "Deaths Age ") for a in DEATH_COLS}, **{"Deaths Age 60-": "60+"})
# Get the deaths ages and unstack so can be matched with excess deaths
covid_age = df[DEATH_COLS].rename(
columns=new_cols).unstack().to_frame("Deaths").rename_axis(["Age Group", "Date"]).reset_index("Age Group")
by_age, ages = group_deaths(by_age, "Age Group", covid_age)
footnote = """
Expected Deaths = Min/Mean/Max of years before the pandemic ({year_span}) + Known Covid Deaths.
Note: Excess deaths can be changed by many factors other than Covid.
""".strip()
footnote3 = f"""{footnote}
2015-2018 was used to compare for the most stable death rates. For other comparisons see
https://djay.github.io/covidthailand/#excess-deaths
""".strip()
footnote5 = f"""{footnote}
For a comparison excluding 2019 (which had higher than expected deaths)
see https://djay.github.io/covidthailand/#excess-deaths
""".strip()
for years in [years5, years3]:
year_span = f"{min(years)}-{max(years)}"
cols_y = [f'Deaths {y}' for y in years]
note = (footnote5 if len(years) > 4 else footnote3).format(year_span=year_span)
suffix = "_5y" if len(years) > 4 else ""
legends = [
'Deaths excl. Covid Deaths',
'Confirmed Covid Deaths',
]
plot_area(df=pan_months,
title=f'Deaths from All Causes {year_span} - Thailand',
legends=legends,
legend_cols=2, legend_pos="lower center",
png_prefix=f'deaths_excess_covid{suffix}',
cols_subset=['Deaths (ex. Known Covid)', 'Deaths Covid'],
box_cols=cols_y,
periods_to_plot=['all'],
kind='bar', stacked=True,
cmap='tab10',
footnote=note,
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
plot_area(df=by_region,
title=f'Deaths from All Causes vs. Expected Deaths by Region ({year_span}) - Thailand',
legends=[f'{reg}' for reg in regions],
legend_cols=4, legend_pos="lower center",
png_prefix=f'deaths_excess_region{suffix}',
cols_subset=[f'Deaths All Month {reg}' for reg in regions],
periods_to_plot=['all'],
box_cols=[[f"{y} {reg}" for y in cols_y] for reg in regions],
kind='bar', stacked=True,
cmap='tab10',
footnote=note,
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
plot_area(df=by_age,
title=f'Deaths from All Causes by Age vs. Expected Deaths ({year_span}) - Thailand',
legends=[f'{age}' for age in ages],
legend_cols=2, legend_pos="center left",
png_prefix=f'deaths_excess_age_bar{suffix}',
cols_subset=[f'Deaths All Month {age}' for age in ages],
box_cols=[[f"{y} {age}" for y in cols_y] for age in ages],
periods_to_plot=['all'],
kind='bar', stacked=True,
cmap='tab10',
footnote=note,
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
by_province = excess.groupby(["Province"]).apply(calc_pscore)
by_province['Deaths Covid'] = cases.groupby(["Province", pd.Grouper(level=0, freq='ME')])['Deaths'].sum()
top5 = by_province.pipe(topprov, lambda adf: (adf["Excess Deaths"] - adf['Deaths Covid']) / adf['Pre 5 Avg'] * 100, num=5)
cols = top5.columns.to_list()
plot_area(df=top5,
title='Deviation from Expected Monthly Deaths - Thailand',
png_prefix='deaths_expected_prov', cols_subset=cols,
periods_to_plot=['all'],
ma_days=None,
kind='line', stacked=False, percent_fig=False, limit_to_zero=False,
cmap='tab10',
footnote='Note: Average 2015-19 plus known Covid deaths.\n' + footnote5,
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
by_region = excess.groupby(["region"]).apply(calc_pscore).reset_index()
by_region = pd.crosstab(by_region['Date'], by_region['region'], values=by_region['P-Score'], aggfunc="sum")
plot_area(df=by_region,
title='Mortaliy (all causes) compared to Previous Years - By Region - Thailand',
png_prefix='deaths_pscore_region', cols_subset=utils_thai.REG_COLS, legends=utils_thai.REG_LEG,
# ma_days=21,
kind='line', stacked=False, percent_fig=False, mini_map=True, limit_to_zero=False,
cmap=utils_thai.REG_COLOURS,
periods_to_plot=['all'],
y_formatter=perc_format,
table=trend_table(by_province['P-Score'], sensitivity=0.04, style="abs", ma_days=1),
footnote='All cause mortality compared to average for same period in 2015-2019 inc known Covid deaths.',
footnote_left=f'{source}Data Source: CCSA Daily Briefing')
top5 = by_province.pipe(topprov, lambda adf: adf["Excess Deaths"], num=7)
cols = top5.columns.to_list()
plot_area(df=top5,
title='Excess Deaths - Highest Provinces - Thailand',
png_prefix='deaths_excess_prov', cols_subset=cols,
periods_to_plot=['all'],
ma_days=None,
kind='line', stacked=False, percent_fig=False, limit_to_zero=False,
cmap='tab10',
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
by_district = excess.groupby("Health District Number").apply(calc_pscore)
by_district['Deaths Covid'] = cases.groupby(["Health District Number", pd.Grouper(level=0, freq='ME')])['Deaths'].sum()
by_district['Deviation from expected Deaths'] = (
by_district['Excess Deaths'] - by_district['Deaths Covid']) / by_district['Pre 5 Avg'] * 100
top5 = area_crosstab(by_district, "Deviation from expected Deaths", "")
cols = rearrange([f'Deviation from expected Deaths Area {area}' for area in DISTRICT_RANGE_SIMPLE], *FIRST_AREAS)
plot_area(df=top5,
title='Deviation from Expected Monthly Deaths - Thailand',
legends=AREA_LEGEND,
png_prefix='deaths_expected_area', cols_subset=cols,
periods_to_plot=['all'],
ma_days=None,
kind='line', stacked=False, percent_fig=False, limit_to_zero=False,
cmap='tab20',
footnote='Note: Average 2015-2019 plus known Covid deaths.',
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
by_age = excess.pipe(cut_ages, AGE_BINS).groupby(["Age Group"]).apply(calc_pscore)
by_age = by_age.reset_index().pivot(values=["P-Score"], index="Date", columns="Age Group")
by_age.columns = [' '.join(c) for c in by_age.columns]
plot_area(df=by_age,
title='Mortaliy (all causes) compared to Previous Years - By Age - Thailand',
png_prefix='deaths_pscore_age',
cols_subset=list(by_age.columns),
kind='line', stacked=False, limit_to_zero=False,
y_formatter=perc_format,
periods_to_plot=['all'],
cmap='tab10',
footnote='All cause mortality compared to average for same period in 2015-2019 inc known Covid deaths.',
footnote_left=f'{source}Data Sources: Office of Registration Administration\n Department of Provincial Administration')
if __name__ == "__main__":
df = import_csv("combined", index=["Date"])
briefings = import_csv("cases_briefings", ["Date"], False)
dash = import_csv("moph_dashboard", ["Date"], False, dir="inputs/json") # so we cache it
dash_weekly = import_csv("moph_dash_weekly", ["Date"], False, dir="inputs/json") # so we cache it
vaccols = [f"Vac Given {d} Cum" for d in range(1, 5)]
hospcols = [c for c in df.columns if 'Hospitalized' in c]
daily = cum2daily(dash_weekly, exclude=vaccols + hospcols)
daily_deaths = weekly2daily(dash_weekly[(c for c in dash_weekly.columns if "Deaths " in c)])
daily = daily.combine_first(daily_deaths)
df = briefings.combine_first(dash).combine_first(daily).combine_first(df)
# df = import_csv("combined", index=["Date"])
os.environ["MAX_DAYS"] = '0'
os.environ['USE_CACHE_DATA'] = 'True'
save_deaths_plots(df)
save_excess_death_plots(df)