IFR = infectionFatalityRateA if U[i] <= intensiveUnits else infectionFatalityRateB
D[i] = DPrev + IFR * (R[i] - RPrev)
RPrev = R[i]
DPrev = D[i]
D = shared.delay(D, - timeInfected + timePresymptomatic +symptomToHospitalLag + timeInHospital + communicationLag) # deaths from R
# Plot
fig = plt.figure(dpi=75, figsize=(20,16))
ax = fig.add_subplot(111)
ax.fmt_xdata = matplotlib.dates.DateFormatter('%Y-%m-%d') # higher date precision for cursor display
if logPlot:
ax.set_yscale("log", nonposy='clip')
# actual country data
XCDR_data = np.array(world_data.get_country_xcdr(COUNTRY, PROVINCE,
excludeCountries=EXCLUDECOUNTRIES, returnDates=True))
dataOffset = shared.get_offset_X(XCDR_data, D, dataOffset) # match model day to real data day for deaths curve todo: percentage wise?
ax.plot(XCDR_data[:,0], XCDR_data[:,1], 'o', color='orange', alpha=0.5, lw=1, label='cases actually detected in tests')
ax.plot(XCDR_data[:,0], XCDR_data[:,2], 'x', color='black', alpha=0.5, lw=1, label='actually deceased')
# set model time to real world time
X = shared.model_to_world_time(X - dataOffset, XCDR_data)
# model data
#ax.plot(X, S, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(X, E, 'y', alpha=0.5, lw=2, label='Exposed (realtime)')
ax.plot(X, I, 'r--', alpha=0.5, lw=1, label='Infected (realtime)')
ax.plot(X, FC, color='orange', alpha=0.5, lw=1, label='Found cumulated: "cases" (lagtime)')
ax.plot(X, U, 'r', alpha=0.5, lw=2, label='ICU (realtime)')
#ax.plot(X, R, 'g', alpha=0.5, lw=1, label='Recovered with immunity')
ax.plot(X, I, 'r--', alpha=0.5, lw=1, label='Infected')
ax.plot(X, F, color='orange', alpha=0.5, lw=1, label='Found')
ax.plot(X, H, 'r', alpha=0.5, lw=2, label='ICU')
#ax.plot(X, R, 'g', alpha=0.5, lw=1, label='Recovered with immunity')
#ax.plot(X, P, 'c', alpha=0.5, lw=1, label='Probability of infection')
ax.plot(X, D, 'k', alpha=0.5, lw=1, label='Deaths')
ax.plot([min(X), max(X)], [intensiveUnits, intensiveUnits],
'b-.',
alpha=0.5,
lw=1,
label='Number of ICU available')
# actual country data
XCDR_data = np.array(
world_data.get_country_xcdr(COUNTRY, PROVINCE, dateOffset=dataOffset))
ax.plot(XCDR_data[:, 0],
XCDR_data[:, 1],
'o',
color='orange',
alpha=0.5,
lw=1,
label='cases actually detected in tests')
ax.plot(XCDR_data[:, 0],
XCDR_data[:, 2],
'x',
color='black',
alpha=0.5,
lw=1,
label='actually deceased')
# todo: single loop, cleanup
countryDeaths = []
for country in countries:
try:
if countryPopulation[country] < 1000000:
continue
province = 'all'
country2 = country
if country == 'Hubei':
country2 = 'China'
province = 'Hubei'
XCDR_data = np.array(
world_data.get_country_xcdr(country2,
province=province,
returnDates=True))
cases = int(XCDR_data[-1, 1]) # last row, third column
deaths = int(XCDR_data[-1, 2]) # last row, third column
if deaths < 10:
continue
recovered = int(XCDR_data[-1, 3]) # last row, third column
date = XCDR_data[-1, 0]
countryDeaths.append((country, cases, deaths, recovered, date))
except Exception as e:
print("fail: ", country, sys.exc_info()[0], e)
countryDeathsPC = []
for ccdrd in countryDeaths:
country, cases, deaths, recovered, date = ccdrd
import numpy as np
import matplotlib.pyplot as plt
import population
import world_data
countries, provinces = world_data.get_countries_provinces()
countryPopulation = population.get_all_population_data()
countryDeaths = []
for country in countries:
try:
if countryPopulation[country] < 1000000:
continue
XCDR_data = np.array(world_data.get_country_xcdr(country))
cases = int(XCDR_data[-1, 1]) # last row, third column
deaths = int(XCDR_data[-1, 2]) # last row, third column
if deaths < 10:
continue
recovered = int(XCDR_data[-1, 3]) # last row, third column
countryDeaths.append((country, cases, deaths, recovered))
except:
print("fail: ", country)
countryDeathsPC = []
for ccdr in countryDeaths:
country, cases, deaths, recovered = ccdr
try:
pop = population.get_population(country)
countryDeathsPC.append((country, deaths * 1.0e6 / pop, deaths, pop))