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BlogVariantsDelta.py
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BlogVariantsDelta.py
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# -*- coding: utf-8 -*-
"""
Blog work on variants.
Created on Mon Feb 15 10:19:50 2021
Updated for Delta beginning Jun 30 2021
@author: 212367548
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import covid
import urllib
from scipy import signal
import datetime
import os
#%% Alternative data sources to GISAID
# covariants.org
# nice because they compile the GISAID data into a file on github
# disadvantage is that it's already aggregated to 2-week blocks
covariants = pd.read_json('https://raw.githubusercontent.com/hodcroftlab/covariants/master/cluster_tables/USAClusters_data.json')
covariants_wi = pd.DataFrame(covariants.loc['Wisconsin', 'countries'])
covariants_wi.plot(x='week', y=['total_sequences', '20I (Alpha, V1)', '21A (Delta)'])
# I believe week is "two-week period beginning..."
#%%
wi = covariants_wi.copy()
col_rename = {'total_sequences': 'Total',
'20I (Alpha, V1)': 'Alpha',
'21A (Delta)': 'Delta'}
wi['Week'] = pd.to_datetime(wi.week)
wi = wi.set_index('Week')
wi = wi[col_rename.keys()]
wi = wi.rename(columns=col_rename)
wi['Alpha (B.1.1.7)'] = wi['Alpha'] / wi['Total']
wi['Delta (B.1.617.2)'] = wi['Delta'] / wi['Total']
wi['Other variants'] = 1 - wi['Alpha (B.1.1.7)'] - wi['Delta (B.1.617.2)']
# wi.plot(y=['Alpha (B.1.1.7)', 'Delta (B.1.617.2)', 'Other variants'])
#%% plotly version
plotdata = wi #wi[['Alpha (B.1.1.7) fraction', 'Delta (B.1.617.2) fraction', 'Other variants']]
plotdata.index.name='Date'
plotdata = plotdata.reset_index()
plotdata = plotdata[plotdata.Date >= pd.to_datetime('2021-01-15')]
fig = px.area(
plotdata,
x='Date',
y=['Delta (B.1.617.2)', 'Alpha (B.1.1.7)', 'Other variants'],
color_discrete_sequence=['darkblue', 'tomato', 'gray'],
labels={'value':'Variant share', 'variable':'Variant'},
title='Coronavirus variant share in WI')
savefile = '.\\docs\\assets\\plotly\\Variant-Fraction.html'
fig.write_html(
file=savefile,
default_height=400,
include_plotlyjs='cdn',
)
os.startfile(savefile)
save_png = '.\\docs\\assets\\Variant-Fraction.png'
fig.write_image(
save_png,
width=700,
height=400,
engine='kaleido',
)
os.startfile(save_png)
#%% Get case data by test date
start_date = pd.to_datetime('2021-01-15')
end_date = pd.to_datetime('2021-07-27')
plotdata = pd.DataFrame(index=pd.date_range(start=start_date, end=end_date))
# plotdata['Cases'] = widata.set_index('Date')['POS_NEW']
# plotdata['Cases 7-day'] = widata.set_index('Date')['POS_NEW'].rolling(7).mean()
pos_df = covid.scrape_widash_postest()
plotdata['Cases'] = pos_df.set_index('Date')['Positive']
plotdata['Cases 7-day'] = plotdata.Cases.rolling(7).mean()
#%% Plot cases by proportion of variants
variants_temp = wi.copy()
# advanced dates one week, so they're plotted in the middle of the sum range
variants_temp.index = variants_temp.index + datetime.timedelta(days=7)
plotdata['Alpha fraction'] = variants_temp['Alpha (B.1.1.7)']
plotdata['Delta fraction'] = variants_temp['Delta (B.1.617.2)']
plotdata['Other fraction'] = variants_temp['Other variants']
plotdata[['Alpha fraction', 'Delta fraction', 'Other fraction']] = plotdata[['Alpha fraction', 'Delta fraction', 'Other fraction']].interpolate()
plotdata['Alpha (B.1.1.7)'] = plotdata['Alpha fraction'] * plotdata['Cases 7-day']
plotdata['Delta (B.1.617.2)'] = plotdata['Delta fraction'] * plotdata['Cases 7-day']
plotdata['Other variants'] = plotdata['Other fraction'] * plotdata['Cases 7-day']
plotdata.index.name = 'Date'
plotdata = plotdata[~np.isnan(plotdata['Other variants'])]
fig = px.area(
plotdata.reset_index(),
x='Date',
y=['Delta (B.1.617.2)', 'Alpha (B.1.1.7)', 'Other variants'],
# color_discrete_sequence=['darkgreen', 'rgb(209, 52, 52)', 'gray'],
color_discrete_sequence=['darkblue', 'tomato', 'gray'],
labels={'value':'Cases/day', 'variable':'Variant'},
title='Estimated cases by variant in WI')
savefile = '.\\docs\\assets\\plotly\\Variant-Cases.html'
fig.write_html(
file=savefile,
default_height=400,
include_plotlyjs='cdn',
)
os.startfile(savefile)
save_png = '.\\docs\\assets\\Variant-Cases.png'
fig.write_image(
save_png,
width=700,
height=400,
engine='kaleido',
)
os.startfile(save_png)
#%% Estimates
exit
# model_start = pd.to_datetime('2021-05-09')
# DeltaR = 1.5 # factor that Delta's R exceeds the current mix of strains
# R1 = 0.75
# start = 560
model_start = pd.to_datetime('2021-05-17')
DeltaR = 1.77 # factor that Delta's R exceeds the current mix of strains
R1 = 0.7
start = 430
N = (end_date - model_start).days + 1
R2 = R1*DeltaR
s = 5 # serial interval, 5 days
d = np.arange(0,N)
# 4% at May 24 from covariants data, apply the R factor and time delay to start date
delay = (model_start - pd.to_datetime('2021-05-24')).days
frac2 = 0.04 * DeltaR**(delay/s)
v1 = start * np.exp((R1-1)*d/s)
v2 = frac2 * start * np.exp((R2-1)*d/s)
plotdata.loc[model_start:,'Prior trend'] = v1
plotdata.loc[model_start:,'Delta trend'] = v2
plotdata.loc[model_start:,'Model total'] = v1 + v2
plotdata.index.name = 'Date'
#%% Use covariants estimates
plotdata2 = wi.copy()
# advanced dates one week, so they're plotted in the middle of the sum range
plotdata2.index = plotdata2.index + datetime.timedelta(days=7)
# limit dates
plotdata2 = plotdata2.loc[(plotdata2.index >= start_date) & (plotdata2.index <= end_date)].copy()
plotdata2['Delta estimate'] = plotdata['Cases 7-day'] * plotdata2['Delta (B.1.617.2)']
plotdata2['Alpha estimate'] = plotdata['Cases 7-day'] * plotdata2['Alpha (B.1.1.7)']
#%% Plot
# fig = px.line(plotdata, y=['Cases 7-day', 'Classic', 'B.1.1.7', 'Model total'])
fig = px.area(
plotdata,
y=['Delta trend', 'Prior trend'],
color_discrete_sequence=['tomato', 'lightsteelblue'],
title='<i><b>Possible</i></b> Delta variant trend in WI',
labels={'index':'Date', 'value': 'Cases / day'}
)
fig.add_trace(
go.Scatter(
x=plotdata.index,
y=plotdata['Cases 7-day'],
name='Cases (7-day avg)',
marker_color='steelblue',
)
)
fig.add_trace(
go.Scatter(
x=plotdata2.index,
y=plotdata2['Alpha estimate'],
name='Alpha estimate',
marker_color='saddlebrown',
line_dash='dot'
)
)
fig.add_trace(
go.Scatter(
x=plotdata2.index,
y=plotdata2['Delta estimate'],
name='Delta estimate',
marker_color='darkred',
line_dash='dot'
)
)
fig.update_layout(legend_traceorder='reversed', legend_title='')
# fig.add_trace(
# go.Bar(
# x=data1.index,
# y=data1.iloc[:,gg],
# name=data1_label,
# marker_color=plotcolors[2],
# hovertemplate='%{y:.0f}',
# showlegend=showlegend,
# ),
# row=sub_row[gg],
# col=sub_col[gg],
# )
# save as html, with plotly JS library loaded from CDN
htmlfile='docs\\assets\\plotly\\Variant-Estimate.html'
fig.write_html(
file=htmlfile,
default_height=500,
include_plotlyjs='cdn',
)
pngfile = 'docs\\assets\\Variant-Estimate.png'
fig.write_image(
pngfile,
width=700,
height=500,
engine='kaleido',
)
os.startfile(htmlfile)
os.startfile(pngfile)