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nsw.py
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import sys
from datetime import datetime
from pytz import timezone
from pathlib import Path
import json
import pickle
from scipy.signal import convolve
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.units as munits
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import pandas as pd
from reff_plots_common import (
covidlive_new_cases,
covidlive_doses_per_100,
exponential,
determine_smoothed_cases_and_Reff,
exponential_with_vax,
exponential_with_infection_immunity,
get_SIR_projection,
get_exp_projection,
whiten,
th,
)
# Our uncertainty calculations are stochastic. Make them reproducible, at least:
np.random.seed(0)
converter = mdates.ConciseDateConverter()
munits.registry[np.datetime64] = converter
munits.registry[datetime.date] = converter
munits.registry[datetime] = converter
POP_OF_NSW = 8.166e6
VAX = 'vax' in sys.argv
OTHERS = 'others' in sys.argv
CONCERN = 'concern' in sys.argv
SYDNEY = 'sydney' in sys.argv
NOT_SYDNEY = 'notsydney' in sys.argv
HUNTER = 'hunter' in sys.argv
ILLAWARRA = 'illawarra' in sys.argv
WESTERN_NSW = 'wnsw' in sys.argv
LGA_IX = None
LGA = None
OLD = 'old' in sys.argv
if (
not (VAX or OTHERS or CONCERN or SYDNEY or NOT_SYDNEY or HUNTER or ILLAWARRA or WESTERN_NSW)
and sys.argv[1:]
):
if (VAX and OTHERS) or (VAX and CONCERN):
pass # That's fine and allowed
if len(sys.argv) == 2:
LGA_IX = int(sys.argv[1])
elif OLD and len(sys.argv) == 3:
OLD_END_IX = int(sys.argv[2])
else:
raise ValueError(sys.argv[1:])
if OLD:
VAX = True
# Data from NSW Health by LGA and test notification date
def lga_data(start_date=np.datetime64('2021-06-10')):
url = (
"https://data.nsw.gov.au/data/dataset/"
"aefcde60-3b0c-4bc0-9af1-6fe652944ec2/"
"resource/21304414-1ff1-4243-a5d2-f52778048b29/"
"download/confirmed_cases_table1_location.csv"
)
df = pd.read_csv(url)
df = df.dropna()
LGAs = set(df['lga_name19'])
cases_by_lga = {}
for lga in LGAs:
if not isinstance(lga, str):
continue
cases_by_date = {
d: 0
for d in np.arange(
np.datetime64(df['notification_date'].min()),
np.datetime64(df['notification_date'].max()) + 1,
)
}
for _, row in df[df['lga_name19'] == lga].iterrows():
cases_by_date[np.datetime64(row['notification_date'])] += 1
dates = np.array(list(cases_by_date.keys()))
new = np.array(list(cases_by_date.values()))
new = new[dates >= start_date]
dates = dates[dates >= start_date]
cases_by_lga[lga.split(' (')[0]] = new
# Last day is incomplete data, ignore it:
dates = dates[:-1]
cases_by_lga = {lga: cases[:-1] for lga, cases in cases_by_lga.items()}
return dates, cases_by_lga
def projected_vaccine_immune_population(t, historical_doses_per_100):
"""compute projected future susceptible population, given an array
historical_doses_per_100 for cumulative doses doses per 100 population prior to and
including today (length doesn't matter, so long as it goes back longer than
VAX_ONSET_MU plus 3 * VAX_ONSET_SIGMA), and assuming a certain vaccine efficacy and
rollout schedule"""
# We assume vaccine effectiveness after each dose ramps up the integral of a
# Gaussian with the following mean and stddev in days:
VAX_ONSET_MU = 10.5
VAX_ONSET_SIGMA = 3.5
AUG = np.datetime64('2021-08-01').astype(int) - dates[-1].astype(int)
SEP = np.datetime64('2021-09-01').astype(int) - dates[-1].astype(int)
OCT = np.datetime64('2021-10-01').astype(int) - dates[-1].astype(int)
NOV = np.datetime64('2021-11-01').astype(int) - dates[-1].astype(int)
# History of previously projected rates, so I can remake old projections:
if dates[-1] >= np.datetime64('2021-11-21'):
JUL_RATE = None
AUG_RATE = None
SEP_RATE = None
OCT_RATE = None
NOV_RATE = 0.10
elif dates[-1] >= np.datetime64('2021-10-30'):
JUL_RATE = None
AUG_RATE = None
SEP_RATE = None
OCT_RATE = 0.33
NOV_RATE = 0.33
elif dates[-1] >= np.datetime64('2021-10-22'):
JUL_RATE = None
AUG_RATE = None
SEP_RATE = None
OCT_RATE = 0.7
NOV_RATE = 0.7
elif dates[-1] >= np.datetime64('2021-08-28'):
JUL_RATE = None
AUG_RATE = 1.4
SEP_RATE = 1.6
OCT_RATE = 1.8
NOV_RATE = 1.8
elif dates[-1] >= np.datetime64('2021-08-16'):
JUL_RATE = None
AUG_RATE = 1.2
SEP_RATE = 1.4
OCT_RATE = 1.6
NOV_RATE = 1.6
elif dates[-1] >= np.datetime64('2021-08-08'):
JUL_RATE = None
AUG_RATE = 1.01
SEP_RATE = 0.92
OCT_RATE = 1.26
NOV_RATE = 1.26
else:
JUL_RATE = 0.63
AUG_RATE = 0.76
SEP_RATE = 0.85
OCT_RATE = 1.06
NOV_RATE = 1.29
doses_per_100 = np.zeros_like(t)
doses_per_100[0] = historical_doses_per_100[-1]
for i in range(1, len(doses_per_100)):
if i < AUG:
doses_per_100[i] = doses_per_100[i - 1] + JUL_RATE
if i < SEP:
doses_per_100[i] = doses_per_100[i - 1] + AUG_RATE
elif i < OCT:
doses_per_100[i] = doses_per_100[i - 1] + SEP_RATE
elif i < NOV:
doses_per_100[i] = doses_per_100[i - 1] + OCT_RATE
else:
doses_per_100[i] = doses_per_100[i - 1] + NOV_RATE
if dates[-1] >= np.datetime64('2021-11-21'):
MAX_DOSES_PER_100 = 2 * 80.0
else:
MAX_DOSES_PER_100 = 2 * 85.0
doses_per_100 = np.clip(doses_per_100, 0, MAX_DOSES_PER_100)
all_doses_per_100 = np.concatenate([historical_doses_per_100, doses_per_100])
# The "prepend=0" makes it as if all the doses in the initial day were just
# administered all at once, but as long as historical_doses_per_100 is long enough
# for it to have taken full effect, it doesn't matter.
daily = np.diff(all_doses_per_100, prepend=0)
# convolve daily doses with a transfer function for delayed effectiveness of vaccnes
pts = int(VAX_ONSET_MU + 3 * VAX_ONSET_SIGMA)
x = np.arange(-pts, pts + 1, 1)
kernel = np.exp(-((x - VAX_ONSET_MU) ** 2) / (2 * VAX_ONSET_SIGMA ** 2))
kernel /= kernel.sum()
convolved = convolve(daily, kernel, mode='same')
effective_doses_per_100 = convolved.cumsum()
immune = 0.4 * effective_doses_per_100[len(historical_doses_per_100):] / 100
return immune
LGAs_OF_CONCERN = [
'Blacktown',
'Campbelltown',
'Canterbury-Bankstown',
'Cumberland',
'Fairfield',
'Georges River',
'Liverpool',
'Parramatta',
'Penrith',
'Bayside',
'Strathfield',
'Burwood',
]
HUNTER_LGAS = [
"Cessnock",
"Lake Macquarie",
"Dungog",
"Maitland",
"Mid-Coast",
"Muswellbrook",
"Newcastle",
"Port Stephens",
"Singleton",
"Upper Hunter Shire"
]
ILLAWARRA_LGAS = ["Wollongong", "Shoalhaven", "Shellharbour", "Kiama", "Wingecarribee"]
WESTERN_NSW_LGAS = [
# Central West
"Bathurst Regional",
"Blayney",
"Cabonne",
"Cowra",
"Forbes",
"Lachlan",
"Lithgow",
"Mid-Western Regional",
"Oberon",
"Orange",
"Parkes",
"Weddin",
# North Western
"Bogan",
"Bourke",
"Brewarrina",
"Cobar",
"Coonamble",
"Dubbo Regional",
"Gilgandra",
"Narromine",
"Walgett",
"Warren",
"Warrumbungle Shire",
# Far West
"Broken Hill",
"Central Darling",
# "Unincorporated Far West",
]
# Source: https://lpinsw.maps.arcgis.com/apps/webappviewer/index.html?id=2a8d27c8959c407396be0a3433eb4a58
GREATER_SYDNEY_LGAS = {
"Hawkesbury",
"Central Coast",
"The Hills Shire",
"Hornsby",
"Northern Beaches",
"Blue Mountains",
"Penrith",
"Blacktown",
"Parramatta",
"Ryde",
"Willoughby",
"Lane Cove",
"Hunters Hill",
"North Sydney",
"Mosman",
"Wollondilly",
"Liverpool",
"Fairfield",
"Cumberland",
"Strathfield",
"Burwood",
"Canada Bay",
"Inner West",
"Sydney",
"Woollahra",
"Waverley",
"Camden",
"Campbelltown",
"Canterbury-Bankstown",
"Georges River",
"Bayside",
"Randwick",
"Sutherland Shire",
"Wollongong",
"Shellharbour"
}
if LGA_IX is not None or OTHERS or CONCERN or SYDNEY or NOT_SYDNEY or HUNTER or ILLAWARRA or WESTERN_NSW:
today = np.datetime64(datetime.now(), 'D')
LGA_DATA_CACHE = Path(f'NSW_LGA_DATA_{today}.temp.pickle')
if not LGA_DATA_CACHE.exists():
dates, cases_by_lga = lga_data()
LGA_DATA_CACHE.write_bytes(pickle.dumps((dates, cases_by_lga)))
dates, cases_by_lga = pickle.loads(LGA_DATA_CACHE.read_bytes())
# Sort LGAs in reverse order by last 14d cases
sorted_lgas_of_concern = sorted(
LGAs_OF_CONCERN, key=lambda k: -cases_by_lga[k][-14:].sum()
)
# print(sorted_lgas_of_concern)
# for lga in sorted_lgas:
# print(lga, cases_by_lga[lga][-14:].sum())
# Quick-and-dirty check - these LGAs are exluded from the Western NSW list above to
# ensure we don't crash because they haven't had any COVID cases - but once they do
# have cases we want to include them. But I can't be sure in advance how they will
# be named in the dataset - e.g. two may or may not have "Shire" in the name.
for lga in cases_by_lga:
if "Far West" in lga:
WESTERN_NSW_LGAS.append(lga)
print(f"There are cases in {lga} now, add it to the list!")
if LGA_IX is not None:
LGA = sorted_lgas_of_concern[LGA_IX]
new = cases_by_lga[LGA]
elif OTHERS:
# Sum over all LGAs *not* of concern
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga not in LGAs_OF_CONCERN)
elif CONCERN:
# Sum over all LGAs of concern
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga in LGAs_OF_CONCERN)
elif SYDNEY:
# Sum over all LGAs in Greater Sydney
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga in GREATER_SYDNEY_LGAS)
elif NOT_SYDNEY:
# Sum over all LGAs *not* in Greater Sydney
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga not in GREATER_SYDNEY_LGAS)
elif HUNTER:
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga in HUNTER_LGAS)
elif ILLAWARRA:
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga in ILLAWARRA_LGAS)
elif WESTERN_NSW:
new = sum(cases_by_lga[lga] for lga in cases_by_lga if lga in WESTERN_NSW_LGAS)
else:
dates, new = covidlive_new_cases('NSW', start_date=np.datetime64('2021-06-10'))
if dates[-1] >= np.datetime64('2022-01-16'):
# Data corrections as of Jan 16th:
new[dates==np.datetime64('2022-01-01')] += 5_539
new[dates==np.datetime64('2022-01-02')] += 5_540
new[dates==np.datetime64('2022-01-03')] += 5_542
new[dates==np.datetime64('2022-01-04')] += 5_542
new[dates==np.datetime64('2022-01-05')] += 5_542
new[dates==np.datetime64('2022-01-06')] += 10_658
new[dates==np.datetime64('2022-01-07')] += 11_864
new[dates==np.datetime64('2022-01-08')] += 13_307
new[dates==np.datetime64('2022-01-09')] += 14_597
new[dates==np.datetime64('2022-01-10')] += 14_598
new[dates==np.datetime64('2022-01-11')] += 14_598
new[dates==np.datetime64('2022-01-12')] += 14_599
new[dates==np.datetime64('2022-01-13')] += 7_352
new[dates==np.datetime64('2022-01-14')] += 3_876
new[dates==np.datetime64('2022-01-15')] += 1_601
new[dates==np.datetime64('2022-01-12')] -= 61_387
new[dates==np.datetime64('2022-01-13')] -= 37_938
new[dates==np.datetime64('2022-01-14')] -= 21_748
new[dates==np.datetime64('2022-01-15')] -= 13_682
elif dates[-1] == np.datetime64('2022-01-15'):
# Data corrections as of Jan 15th:
new[dates==np.datetime64('2022-01-01')] += 5_230
new[dates==np.datetime64('2022-01-02')] += 5_231
new[dates==np.datetime64('2022-01-03')] += 5_232
new[dates==np.datetime64('2022-01-04')] += 5_232
new[dates==np.datetime64('2022-01-05')] += 5_232
new[dates==np.datetime64('2022-01-06')] += 10_348
new[dates==np.datetime64('2022-01-07')] += 11_554
new[dates==np.datetime64('2022-01-08')] += 12_997
new[dates==np.datetime64('2022-01-09')] += 12_997
new[dates==np.datetime64('2022-01-10')] += 12_998
new[dates==np.datetime64('2022-01-11')] += 12_998
new[dates==np.datetime64('2022-01-12')] += 12_998
new[dates==np.datetime64('2022-01-13')] += 5_751
new[dates==np.datetime64('2022-01-14')] += 2_275
new[dates==np.datetime64('2022-01-12')] -= 61_387
new[dates==np.datetime64('2022-01-13')] -= 37_938
new[dates==np.datetime64('2022-01-14')] -= 21_748
elif dates[-1] == np.datetime64('2022-01-14'):
# Data corrections as of Jan 14th:
new[dates==np.datetime64('2022-01-01')] += 4_399
new[dates==np.datetime64('2022-01-02')] += 4_399
new[dates==np.datetime64('2022-01-03')] += 4_400
new[dates==np.datetime64('2022-01-04')] += 4_400
new[dates==np.datetime64('2022-01-05')] += 4_400
new[dates==np.datetime64('2022-01-06')] += 9_516
new[dates==np.datetime64('2022-01-07')] += 10_722
new[dates==np.datetime64('2022-01-08')] += 10_722
new[dates==np.datetime64('2022-01-09')] += 10_722
new[dates==np.datetime64('2022-01-10')] += 10_723
new[dates==np.datetime64('2022-01-11')] += 10_723
new[dates==np.datetime64('2022-01-12')] += 10_723
new[dates==np.datetime64('2022-01-13')] += 3_476
new[dates==np.datetime64('2022-01-12')] -= 61_387
new[dates==np.datetime64('2022-01-13')] -= 37_938
elif dates[-1] == np.datetime64('2022-01-13'):
# Data corrections as of Jan 13th:
new[dates==np.datetime64('2022-01-01')] += 2_132
new[dates==np.datetime64('2022-01-02')] += 2_132
new[dates==np.datetime64('2022-01-03')] += 2_132
new[dates==np.datetime64('2022-01-04')] += 2_132
new[dates==np.datetime64('2022-01-05')] += 2_132
new[dates==np.datetime64('2022-01-06')] += 7_247
new[dates==np.datetime64('2022-01-07')] += 7_247
new[dates==np.datetime64('2022-01-08')] += 7_247
new[dates==np.datetime64('2022-01-09')] += 7_247
new[dates==np.datetime64('2022-01-10')] += 7_247
new[dates==np.datetime64('2022-01-11')] += 7_247
new[dates==np.datetime64('2022-01-12')] += 7_247
new[dates==np.datetime64('2022-01-12')] -= 61_387
if dates[-1] >= np.datetime64('2022-03-25'):
TEST_DETECTION_RATE = 0.35
elif dates[-1] >= np.datetime64('2022-01-09'):
TEST_DETECTION_RATE = 0.27
else:
TEST_DETECTION_RATE = 0.2
START_VAX_PROJECTIONS = 42 # July 22nd, when I started making vaccine projections
all_dates = dates
all_new = new
doses_per_100 = covidlive_doses_per_100(
n=len(dates),
state='NSW',
population=POP_OF_NSW,
)
if OLD:
dates = dates[:START_VAX_PROJECTIONS + OLD_END_IX]
new = new[:START_VAX_PROJECTIONS + OLD_END_IX]
doses_per_100 = doses_per_100[:START_VAX_PROJECTIONS + OLD_END_IX]
START_PLOT = np.datetime64('2021-06-13')
END_PLOT = np.datetime64('2022-07-01') if VAX else dates[-1] + 28
tau = 5 # reproductive time of the virus in days
R_clip = 50
immune = projected_vaccine_immune_population(np.arange(100), doses_per_100)
s = 1 - immune
dk_dt = 1 / tau * (s[1] / s[0] - 1)
# Keep the old methodology for old plots:
if dates[-1] >= np.datetime64('2022-06-16'):
padding_model = exponential
elif dates[-1] >= np.datetime64('2022-01-04'):
padding_model = lambda x, A, k: exponential_with_infection_immunity(
x,
A,
k,
cumulative_cases=new.sum(),
tau=tau,
effective_population=TEST_DETECTION_RATE * POP_OF_NSW,
)
elif dates[-1] >= np.datetime64('2021-10-27'):
padding_model = lambda x, A, k: exponential_with_vax(x, A, k, dk_dt)
else:
padding_model = exponential
# Whether or not to do a 5dma of data prior to the fit. Change of methodology as of
# 2021-11-19, so keep old methodology for remaking plots prior to then. Changed
# methodology back on 2021-12-11.
if dates[-1] > np.datetime64('2021-12-10'):
PRE_FIT_SMOOTHING = None
elif dates[-1] > np.datetime64('2021-11-18'):
PRE_FIT_SMOOTHING = 5
else:
PRE_FIT_SMOOTHING = None
# Where the magic happens, estimate everything:
(
new_smoothed,
u_new_smoothed,
R,
u_R,
cov,
shot_noise_factor,
) = determine_smoothed_cases_and_Reff(
new,
fit_pts=min(20, len(dates[dates >= START_PLOT])),
pre_fit_smoothing=PRE_FIT_SMOOTHING,
padding_model=padding_model,
R_clip=R_clip,
tau=tau,
)
# Fudge what would happen with a different R_eff:
# cov_R_new_smoothed[-1] *= 0.05 / np.sqrt(variance_R[-1])
# R[-1] = 0.75
# variance_R[-1] = 0.05**2
R = R.clip(0, None)
R_upper = (R + u_R).clip(0, R_clip)
R_lower = (R - u_R).clip(0, R_clip)
new_smoothed = new_smoothed.clip(0, None)
new_smoothed_upper = (new_smoothed + u_new_smoothed).clip(0, None)
new_smoothed_lower = (new_smoothed - u_new_smoothed).clip(0, None)
# Projection of daily case numbers:
days_projection = (END_PLOT - dates[-1]).astype(int)
t_projection = np.linspace(0, days_projection, days_projection + 1)
if VAX:
# Fancy stochastic SIR model
(
new_projection,
new_projection_lower,
new_projection_upper,
R_eff_projection,
R_eff_projection_lower,
R_eff_projection_upper,
total_cases,
total_cases_lower,
total_cases_upper,
) = get_SIR_projection(
current_caseload=new_smoothed[-1],
cumulative_cases=new.sum(),
R_eff=R[-1],
tau=tau,
population=POP_OF_NSW,
test_detection_rate=TEST_DETECTION_RATE,
vaccine_immunity=projected_vaccine_immune_population(
t_projection, doses_per_100
),
n_days=days_projection + 1,
n_trials=1000 if OLD else 10000, # just save some time if we're animating
cov=cov,
)
else:
# Simple model, no vaccines or community immunity
new_projection, new_projection_lower, new_projection_upper = get_exp_projection(
t_projection=t_projection,
current_caseload=new_smoothed[-1],
R_eff=R[-1],
cov=cov,
tau=tau,
)
MASKS = np.datetime64('2021-06-23')
LGA_LOCKDOWN = np.datetime64('2021-06-26')
LOCKDOWN = np.datetime64('2021-06-27')
TIGHTER_LOCKDOWN = np.datetime64('2021-07-10')
NONCRITICAL_RETAIL_CLOSED = np.datetime64('2021-07-18')
STATEWIDE = np.datetime64('2021-08-15')
CURFEW = np.datetime64('2021-08-23')
END_CURFEW = np.datetime64('2021-09-16')
END_LOCKDOWN = np.datetime64('2021-10-11')
EASING_80 = np.datetime64('2021-10-18')
END_MASKS = np.datetime64('2021-12-15')
MASKS_AGAIN = np.datetime64('2021-12-24')
DENSITY_LIMITS = np.datetime64('2021-12-27')
END_DENSITY_LIMITS = np.datetime64('2022-02-18')
END_MASKS_AGAIN = np.datetime64('2022-02-25')
fig1 = plt.figure(figsize=(10, 6))
ax1 = plt.axes()
ax1.fill_betweenx(
[-10, 10],
[MASKS, MASKS],
[LGA_LOCKDOWN, LGA_LOCKDOWN],
color=whiten("yellow", 0.5),
linewidth=0,
label="Density limits/70% easing",
)
ax1.fill_betweenx(
[-10, 10],
[LGA_LOCKDOWN, LGA_LOCKDOWN],
[LOCKDOWN, LOCKDOWN],
color=whiten("yellow", 0.5),
edgecolor=whiten("orange", 0.5),
linewidth=0,
hatch="//////",
label="East Sydney LGA lockdown",
)
ax1.fill_betweenx(
[-10, 10],
[LOCKDOWN, LOCKDOWN],
[TIGHTER_LOCKDOWN, TIGHTER_LOCKDOWN],
color=whiten("orange", 0.5),
linewidth=0,
label="Greater Sydney lockdown",
)
ax1.fill_betweenx(
[-10, 10],
[TIGHTER_LOCKDOWN, TIGHTER_LOCKDOWN],
[NONCRITICAL_RETAIL_CLOSED, NONCRITICAL_RETAIL_CLOSED],
color=whiten("orange", 0.5),
edgecolor=whiten("red", 0.35),
linewidth=0,
hatch="//////",
label="Lockdown tightened",
)
ax1.fill_betweenx(
[-10, 10],
[NONCRITICAL_RETAIL_CLOSED, NONCRITICAL_RETAIL_CLOSED],
[STATEWIDE, STATEWIDE],
color=whiten("red", 0.35),
linewidth=0,
label="Noncritical retail closed",
)
ax1.fill_betweenx(
[-10, 10],
[STATEWIDE, STATEWIDE],
[CURFEW, CURFEW],
color=whiten("red", 0.35),
edgecolor=whiten("red", 0.45),
hatch="//////",
linewidth=0,
label="Regional lockdowns",
)
ax1.fill_betweenx(
[-10, 10],
[CURFEW, CURFEW],
[END_CURFEW, END_CURFEW],
color="red",
alpha=0.45,
linewidth=0,
label="LGA curfew",
)
ax1.fill_betweenx(
[-10, 10],
[END_CURFEW, END_CURFEW],
[END_LOCKDOWN, END_LOCKDOWN],
color=whiten("red", 0.35),
edgecolor=whiten("red", 0.45),
hatch="//////",
linewidth=0,
)
ax1.fill_betweenx(
[-10, 10],
[END_LOCKDOWN, END_LOCKDOWN],
[EASING_80, EASING_80],
color=whiten("yellow", 0.5),
linewidth=0,
)
ax1.fill_betweenx(
[-10, 10],
[EASING_80, EASING_80],
[END_MASKS, END_MASKS],
color=whiten("green", 0.5),
linewidth=0,
label="80% easing/mask mandate",
)
ax1.fill_betweenx(
[-10, 10],
[END_MASKS, END_MASKS],
[MASKS_AGAIN, MASKS_AGAIN],
color=whiten("green", 0.25),
linewidth=0,
label="End mandatory masks",
)
ax1.fill_betweenx(
[-10, 10],
[MASKS_AGAIN, MASKS_AGAIN],
[DENSITY_LIMITS, DENSITY_LIMITS],
color=whiten("green", 0.5),
linewidth=0,
)
ax1.fill_betweenx(
[-10, 10],
[DENSITY_LIMITS, DENSITY_LIMITS],
[END_DENSITY_LIMITS, END_DENSITY_LIMITS],
color=whiten("yellow", 0.5),
linewidth=0,
)
ax1.fill_betweenx(
[-10, 10],
[END_DENSITY_LIMITS, END_DENSITY_LIMITS],
[END_MASKS_AGAIN, END_MASKS_AGAIN],
color=whiten("green", 0.5),
linewidth=0,
)
ax1.fill_betweenx(
[-10, 10],
[END_MASKS_AGAIN, END_MASKS_AGAIN],
[END_PLOT, END_PLOT],
color=whiten("green", 0.25),
linewidth=0,
)
ax1.fill_between(
dates[1:] + 1,
R,
label=R"$R_\mathrm{eff}$",
step='pre',
color='C0',
)
if VAX:
ax1.fill_between(
np.concatenate([dates[1:].astype(int), dates[-1].astype(int) + t_projection]) + 1,
np.concatenate([R_lower, R_eff_projection_lower]),
np.concatenate([R_upper, R_eff_projection_upper]),
label=R"$R_\mathrm{eff}$/projection uncertainty",
color='cyan',
edgecolor='blue',
alpha=0.2,
step='pre',
zorder=2,
hatch="////",
)
ax1.fill_between(
dates[-1].astype(int) + t_projection + 1,
R_eff_projection,
label=R"$R_\mathrm{eff}$ (projection)",
step='pre',
color='C0',
linewidth=0,
alpha=0.75
)
else:
ax1.fill_between(
dates[1:] + 1,
R_lower,
R_upper,
label=R"$R_\mathrm{eff}$ uncertainty",
color='cyan',
edgecolor='blue',
alpha=0.2,
step='pre',
zorder=2,
hatch="////",
)
ax1.axhline(1.0, color='k', linewidth=1)
ax1.axis(xmin=START_PLOT, xmax=END_PLOT, ymin=0, ymax=5)
ax1.grid(True, linestyle=":", color='k', alpha=0.5)
ax1.set_ylabel(R"$R_\mathrm{eff}$")
u_R_latest = (R_upper[-1] - R_lower[-1]) / 2
R_eff_string = fR"$R_\mathrm{{eff}}={R[-1]:.02f} \pm {u_R_latest:.02f}$"
latest_update_day = datetime.fromisoformat(str(dates[-1] + 1))
latest_update_day = f'{latest_update_day.strftime("%B")} {th(latest_update_day.day)}'
if VAX:
if OTHERS:
region = "New South Wales (excluding LGAs of concern)"
elif CONCERN:
region = "New South Wales LGAs of concern"
elif SYDNEY:
region = "Greater Sydney"
elif NOT_SYDNEY:
region = "New South Wales (excluding Greater Sydney)"
elif HUNTER:
region = "the Hunter region"
elif ILLAWARRA:
region = "the Illawarra region"
elif WESTERN_NSW:
region = "Western New South Wales"
else:
region = "New South Wales"
title_lines = [
f"SIR model of {region} as of {latest_update_day}",
f"Starting from currently estimated {R_eff_string}",
]
else:
if LGA:
region = LGA
elif OTHERS:
region = "New South Wales (excluding LGAs of concern)"
elif CONCERN:
region = "New South Wales LGAs of concern"
elif SYDNEY:
region = "Greater Sydney"
elif NOT_SYDNEY:
region = "New South Wales (excluding Greater Sydney)"
elif HUNTER:
region = "the Hunter region"
elif ILLAWARRA:
region = "the Illawarra region"
elif WESTERN_NSW:
region = "Western New South Wales"
else:
region = "New South Wales"
title_lines = [
f"$R_\\mathrm{{eff}}$ in {region} as of {latest_update_day}, with restriction levels and daily cases",
f"Latest estimate: {R_eff_string}",
]
ax1.set_title('\n'.join(title_lines))
ax1.yaxis.set_major_locator(mticker.MultipleLocator(0.25))
ax2 = ax1.twinx()
if OLD:
ax2.step(all_dates + 1, all_new + 0.02, color='purple', alpha=0.5)
ax2.step(dates + 1, new + 0.02, color='purple', label='Daily cases')
ax2.plot(
dates.astype(int) + 0.5,
new_smoothed,
color='magenta',
label='Daily cases (smoothed)',
)
ax2.fill_between(
dates.astype(int) + 0.5,
new_smoothed_lower,
new_smoothed_upper,
color='magenta',
alpha=0.3,
linewidth=0,
zorder=10,
label=f'Smoothing/{"projection" if VAX else "trend"} uncertainty',
)
ax2.plot(
dates[-1].astype(int) + 0.5 + t_projection,
new_projection.clip(0, 1e6), # seen SVG rendering issues when this is big
color='magenta',
linestyle='--',
label=f'Daily cases ({"SIR projection" if VAX else "exponential trend"})',
)
ax2.fill_between(
dates[-1].astype(int) + 0.5 + t_projection,
new_projection_lower.clip(0, 1e6), # seen SVG rendering issues when this is big
new_projection_upper.clip(0, 1e6),
color='magenta',
alpha=0.3,
linewidth=0,
)
ax2.set_ylabel("Daily cases (log scale)")
ax2.set_yscale('log')
ax2.axis(ymin=1, ymax=100_000)
fig1.tight_layout(pad=1.8)
handles, labels = ax1.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
handles += handles2
labels += labels2
if VAX:
order = [9, 11, 10, 12, 13, 15, 14, 8, 7, 0, 1, 2, 3, 4, 5, 6]
else:
order = [9, 10, 11, 12, 14, 13, 8, 7, 0, 1, 2, 3, 4, 5, 6]
ax2.legend(
# handles,
# labels,
[handles[idx] for idx in order],
[labels[idx] for idx in order],
loc='upper left',
ncol=2 if VAX else 1,
prop={'size': 8}
)
ax2.yaxis.set_major_formatter(mticker.EngFormatter())
ax2.yaxis.set_minor_formatter(mticker.EngFormatter())
ax2.tick_params(axis='y', which='minor', labelsize='x-small')
plt.setp(ax2.get_yminorticklabels()[1::2], visible=False)
locator = mdates.DayLocator([1, 15])
ax1.xaxis.set_major_locator(locator)
formatter = mdates.ConciseDateFormatter(locator, show_offset=False)
ax1.xaxis.set_major_formatter(formatter)
ax2.tick_params(axis='y', colors='purple', which='both')
ax1.spines['right'].set_color('purple')
ax2.spines['right'].set_color('purple')
ax2.yaxis.label.set_color('purple')
ax1.tick_params(axis='y', colors='C0', which='both')
ax1.spines['left'].set_color('C0')
ax2.spines['left'].set_color('C0')
ax1.yaxis.label.set_color('C0')
axpos = ax1.get_position()
text = fig1.text(
0.99,
0.02,
"@chrisbilbo | chrisbillington.net/COVID_NSW",
size=8,
alpha=0.5,
color=(0, 0, 0.25),
fontfamily="monospace",
horizontalalignment="right"
)
text.set_bbox(dict(facecolor='white', alpha=0.8, linewidth=0))
if VAX:
total_cases_range = f"{total_cases_lower/1000:.0f}k—{total_cases_upper/1000:.0f}k"
text = fig1.text(
0.63,
0.83,
"\n".join(
[
f"Projected total cases in outbreak: {total_cases/1000:.0f}k",
f" 68% range: {total_cases_range}",
]
),
fontsize='small',
)
text.set_bbox(dict(facecolor='white', alpha=0.8, linewidth=0))
if OTHERS:
suffix = "_others_vax"
elif CONCERN:
suffix = "_concern_vax"
elif SYDNEY:
suffix = "_sydney_vax"
elif NOT_SYDNEY:
suffix = "_not_sydney_vax"
elif HUNTER:
suffix = "_hunter_vax"
elif ILLAWARRA:
suffix = "_illawarra_vax"
elif WESTERN_NSW:
suffix = "_wnsw_vax"
else:
suffix = '_vax'
elif LGA:
suffix=f'_LGA_{LGA_IX}'
elif OTHERS:
suffix='_LGA_others'
elif CONCERN:
suffix = '_LGA_concern'
elif SYDNEY:
suffix = '_sydney'
elif NOT_SYDNEY:
suffix = '_not_sydney'
elif HUNTER:
suffix = '_hunter'
elif ILLAWARRA:
suffix = "_illawarra"
elif WESTERN_NSW:
suffix = '_wnsw'
else:
suffix = ''
if OLD:
fig1.savefig(f'nsw_animated/{OLD_END_IX:04d}.png', dpi=133)
else:
fig1.savefig(f'COVID_NSW{suffix}.svg')
fig1.savefig(f'COVID_NSW{suffix}.png', dpi=133)
if VAX or not (LGA or OTHERS or CONCERN or SYDNEY or NOT_SYDNEY or HUNTER or ILLAWARRA or WESTERN_NSW):
ax2.set_yscale('linear')
if OLD and dates[-1] < np.datetime64('2021-12-10'):
ymax = 2_500
elif OLD and dates[-1] < np.datetime64('2022-01-15'):
ymax = 60_000
elif VAX:
ymax = 60_000
else:
ymax = 60_000
ax2.axis(ymin=0, ymax=ymax)
ax2.yaxis.set_major_locator(mticker.MultipleLocator(ymax / 10))
ax2.yaxis.set_major_formatter(mticker.EngFormatter())
ax2.set_ylabel("Daily confirmed cases (linear scale)")