Script to fit COVID19 cases to logistic sigmoid functions.

gistfile1.txt

from datetime import datetime
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np

# Data acquired from:
# https://covid19info.live/
d = {
    'Europe': '''
2020-01-23 0
2020-01-24 2
2020-01-25 3
2020-01-26 3
2020-01-27 4
2020-01-28 8
2020-01-29 10
2020-01-30 10
2020-01-31 20
2020-02-01 25
2020-02-02 27
2020-02-03 29
2020-02-04 30
2020-02-05 30
2020-02-06 30
2020-02-07 34
2020-02-08 39
2020-02-09 41
2020-02-10 51
2020-02-11 53
2020-02-12 55
2020-02-13 55
2020-02-14 55
2020-02-15 56
2020-02-16 56
2020-02-17 56
2020-02-18 56
2020-02-19 56
2020-02-20 56
2020-02-21 73
2020-02-22 115
2020-02-23 208
2020-02-24 290
2020-02-25 394
2020-02-26 554
2020-02-27 820
2020-02-28 1114
2020-02-29 1489
2020-03-01 2242
2020-03-02 2773
2020-03-03 3415
2020-03-04 4437
2020-03-05 5872
2020-03-06 7700
2020-03-07 9881
2020-03-08 12505
2020-03-09 15335
2020-03-10 18812
2020-03-11 23993
''',
'Korea': '''
2020-01-23 1
2020-01-24 2
2020-01-25 2
2020-01-26 3
2020-01-27 4
2020-01-28 4
2020-01-29 4
2020-01-30 4
2020-01-31 11
2020-02-01 12
2020-02-02 15
2020-02-03 15
2020-02-04 16
2020-02-05 19
2020-02-06 23
2020-02-07 24
2020-02-08 24
2020-02-09 25
2020-02-10 27
2020-02-11 28
2020-02-12 28
2020-02-13 28
2020-02-14 28
2020-02-15 28
2020-02-16 29
2020-02-17 30
2020-02-18 31
2020-02-19 31
2020-02-20 104
2020-02-21 204
2020-02-22 433
2020-02-23 602
2020-02-24 833
2020-02-25 977
2020-02-26 1261
2020-02-27 1766
2020-02-28 2337
2020-02-29 3150
2020-03-01 3736
2020-03-02 4335
2020-03-03 5186
2020-03-04 5621
2020-03-05 6088
2020-03-06 6593
2020-03-07 7041
2020-03-08 7314
2020-03-09 7478
2020-03-10 7513
2020-03-11 7755
''',
}

region = 'Europe'
s = d[region]

x, y = zip(*(l.rstrip().split(' ') for l in s.split('\n')[1:-1]))
dates = x
# x = [datetime.strptime(s, '%Y-%m-%d').date() for s in x]
x = list(range(len(x)))
y = [int(_) for _ in y]
for i in range(len(x)):
    print(x[i], y[i])
print(x)
print(y)

def sigmoid(x, a, b):
     y = 1 / (1 + np.exp(-b*(x-a)))
     return y

def logistic(x, a, b, c):
    y = a / (1 + np.exp(-b * (x - c)))
    return y    

# Seed values for regression.
p0=[200000, 0.5, 70]
popt, pcov = curve_fit(logistic, x, y, p0=p0)
print(popt)

plt.scatter(x, y, c='r', label='Cases')
plt.xlabel('Days')
plt.ylabel('Present and past confirmed cases')
x2 = list(range(150))
plt.plot(x2, logistic(x2, *popt), 'b', label='Fitted sigmoid logistic function')
title = '{} {}'.format(region, dates[-1])
title += '\nMaximum={:d}, Midpoint={:d}, Steepness={:.2f}'.format(
    int(popt[0]), int(popt[2]), popt[1])
title += '\nCurrent day={}'.format(len(x))
title += ', Current present and past cases={}'.format(y[-1])
plt.title(title, fontsize='small')
plt.legend()
# plt.yscale('log')
plt.savefig('COVID19_sigmoid_{}_{}.png'.format(region, dates[-1]))