aboutsummaryrefslogtreecommitdiff
path: root/solver2.py
blob: ae2403a493277c7135d301c54c12e5568cfbcf72 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
from datetime import datetime, timedelta

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
import argparse
import os

parser = argparse.ArgumentParser()

parser.add_argument('--mode', '-m', dest = 'mode', help = 'change the mode of the model (SIR, Linear, ESIR, SEIR); default: SIR', default = 'SIR', choices = ['SIR', 'Linear', 'ESIR', 'SEIR'])
parser.add_argument('--data', '-d', dest = 'include_data', help = 'change the type of data to present in the graph (Actual, S, I, R, E); default: Actual S I R', nargs = '*', default = ['Actual', 'S', 'I', 'R'], choices = ['Actual', 'S', 'I', 'R', 'E'])
parser.add_argument('--folder', '-f', dest = 'folder', default = 'data', help = 'the folder in which to find the data files; defaults to looking in the data folder')
parser.add_argument('--disease', '-D', dest = 'disease', default = 'COVID-19', help = 'the disease to model; defaults to COVID-19')
parser.add_argument('--out', '-o', dest = 'out', default = None, help = 'the name of the graph and csv files; defaults to the name of the disease')
parser.add_argument('--start', '-s', dest = 'start', default = '1/22/20', help = 'the date where the data starts (defaults to the start date of COVID-19 (1/22/20))')
parser.add_argument('--end', '-e', dest = 'end', default = None, help = 'the date where the data stops (defaults to whereever the input data ends)')
parser.add_argument('--incubation', '-i', dest = 'incubation_period', default = None, help = 'the incubation period of the disease (only applicable if using SIRE model; ignored otherwise); none by default')
parser.add_argument('--predict', '-p', dest = 'prediction_range', default = None, help = 'the number of days to predict the course of the disease (defaults to None, meaning the model will not predict beyond the given data)')
parser.add_argument('--country', '-c', dest = 'country', default = 'US', help = 'the country that is being modeled (defaults to US)')
parser.add_argument('--population', '-P', dest = 'population', default = '10000', help = 'the population of the model (defaults to 10000)')
args = parser.parse_args()

S_0 = (int(args.population) - 1) / int(args.population)
I_0 = 1 / int(args.population)
R_0 = 0
E_0 = 0

# Running a model for a million population is quite hard, so here we've reduced the population and modified the actual stats to match
correction_factor = int(args.population) / 3270000 if args.country == 'US' else int(args.population) / 63710000 if args.country == 'Hong_Kong' else 1

class Learner(object):
	def __init__(self, country):
		self.country = country

	def load_confirmed(self, country):
		"""
			Load confirmed cases
		"""
		df = pd.read_csv(f'{args.folder}/{args.disease}-Confirmed.csv')
		country_df = df[df['Country/Region'] == country]

		if args.end != None:
			confirmed_sums = np.sum([reg.loc[args.start:args.end].values for reg in country_df.iloc], axis = 0)
		else:
			confirmed_sums = np.sum([reg.loc[[args.start:]].values for reg in country_df.iloc], axis = 0)
		
		if args.end != None:
			new_data = pd.DataFrame(confirmed_sums, country_df.iloc[0].loc[args.start:args.end].index.tolist())
		else:
			new_data = pd.DataFrame(confirmed_sums, country_df.iloc[0].loc[args.start:].index.tolist())
		
		return new_data


	def load_recovered(self, country):
		"""
			Load recovered cases
		"""
		df = pd.read_csv(f'{args.folder}/{args.disease}-Recovered.csv')
		country_df = df[df['Country/Region'] == country]

		if args.end != None:
			out = country_df.iloc[0].loc[args.start:args.end]
		else:
			out = country_df.iloc[0].loc[args.start:]
		
		return out

	def load_exposed(self, country):
		"""
			Load data for exposed persons 
		"""
		df = pd.read_csv(f'{args.folder}/{args.disease}-Exposed.csv')
		country_df = df[df['Country/Region'] == country]

		if args.end != None:
			out = country_df.iloc[0].loc[args.start:args.end]
		else:
			out = country_df.iloc[0].loc[args.start:]
		
		return out


	def extend_index(self, index, new_size):
		values = index.values
		current = datetime.strptime(index[-1], '%m/%d/%y')
		while len(values) < new_size:
			current = current + timedelta(days=1)
			values = np.append(values, datetime.strftime(current, '%m/%d/%y'))
		return values

	def predict(self, data, beta = None, gamma = None, mu = None, sigma = None):
		"""
			Predict how the number of people in each compartment can be changed through time toward the future.
			The model is formulated with the given beta and gamma (or others).
			Returns the "solved" system of initial value problems, to be "graded" by the loss function
		"""
		new_index = self.extend_index(data.index, args.prediction_range if args.prediction_range != None else len(data.index))
		size = len(new_index)
		def model(t, y):
			S = y[0]
			I = y[1]
			R = y[2]

			if args.mode == 'SEIR':
				E = y[3]

			if args.mode == 'Linear':
				return [-beta * S, beta * S - gamma * I, gamma * I]
			elif args.mode == 'SIR':
				return [-beta * S * I, beta * S * I - gamma * I, gamma * I]
			elif args.mode == 'ESIR':
				if mu != None:
					return [mu - beta * S * I - mu * S, beta * S * I - gamma * I - mu * I, gamma * I - mu * R]
				else:
					raise Exception('Expected mu for ESIR model')
			elif args.mode == 'SEIR':
				if mu != None and sigma != None:
					return [mu - beta * S * I - mu * S, beta * S * I - sigma * E - mu * E, sigma * E - gamma * I - mu * I, gamma * I - mu * R]
				elif mu == None:
					raise Exception('Expected mu for SEIR model')
				elif sigma == None:
					raise Exception('Expected sigma for SEIR model')
		
		extended_actual = np.concatenate((data.values.flatten(), [0] * (size - len(data.values))))

		if args.mode == 'SEIR':
			result = solve_ivp(model, [0, size], [S_0,I_0,R_0, E_0], t_eval=np.arange(0, size, 1))
		else:
			result = solve_ivp(model, [0, size], [S_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True)

		return new_index, extended_actual, result

	def train(self):
		"""
			Run the optimization to estimate the beta and gamma fitting the given confirmed cases.
		"""
		confirmed_data = self.load_confirmed(self.country)
		recovered_data = self.load_recovered(self.country)

		if not os.path.isdir('out'):
			os.mkdir('out')

		if args.mode == 'Linear':
			optimal = minimize(
				loss_linear,
				[0.001, 0.001],
				args=(confirmed_data, recovered_data),
				method='L-BFGS-B',
				bounds=[(0.00000001, 0.4), (0.00000001, 0.4)]
			)
			beta, gamma = optimal.x
			print(f'Beta: {beta}, Gamma: {gamma}, R0: {beta/gamma}')
			new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma)
			print(f'Predicted I: {prediction.y[1][-1] * int(args.population)}, Actual I: {extended_actual[-1] * correction_factor}')
			df = compose_df(prediction, extended_actual, correction_factor, new_index)
			with open(f'out/{args.disease}-data.csv', 'w+') as file:
				file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}')
		elif args.mode == 'SIR':
			optimal = minimize(
				loss_sir,
				[0.001, 0.001],
				args=(confirmed_data, recovered_data),
				method='L-BFGS-B',
				bounds=[(0.00000001, 0.4), (0.00000001, 0.4)]
			)
			beta, gamma = optimal.x
			print(f'Beta: {beta}, Gamma: {gamma}, R0: {beta/gamma}')
			new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma)
			print(f'Predicted I: {prediction.y[1][-1] * int(args.population)}, Actual I: {extended_actual[-1] * correction_factor}')
			df = compose_df(prediction, extended_actual, correction_factor, new_index)
			with open(f'out/{args.disease}-data.csv', 'w+') as file:
				file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}')
		elif args.mode == 'ESIR':
			optimal = minimize(
				loss_esir,
				[0.001, 0.001, 0.001],
				args=(confirmed_data, recovered_data),
				method='L-BFGS-B',
				bounds=[(0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4)]
			)
			beta, gamma, mu = optimal.x
			print(f'Beta: {beta}, Gamma: {gamma}, Mu: {mu} R0: {beta/(gamma + mu)}')
			new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma, mu = mu)
			print(f'Predicted I: {prediction.y[1][-1] * int(args.population)}, Actual I: {extended_actual[-1] * correction_factor}')
			df = compose_df(prediction, extended_actual, correction_factor, new_index)
			with open(f'out/{args.disease}-data.csv', 'w+') as file:
				file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nR0: {beta/(gamma + mu)}')
		elif args.mode == 'SEIR':
			exposed_data = self.load_exposed(self.country)

			optimal = minimize(
				loss_seir,
				[0.001, 0.001],
				args=(confirmed_data, recovered_data, exposed_data),
				method='L-BFGS-B',
				bounds=[(0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4)]
			)
			beta, gamma, mu, sigma = optimal.x
			print(f'Beta: {beta}, Gamma: {gamma}, Mu: {mu}, Sigma: {sigma} R0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}')
			new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma, mu = mu)
			print(f'Predicted I: {prediction.y[1][-1] * int(args.population)}, Actual I: {extended_actual[-1] * correction_factor}')
			df = compose_df(prediction, extended_actual, correction_factor, new_index)
			with open(f'out/{args.disease}-data.csv', 'w+') as file:
				file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nSigma: {sigma}\nR0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}')
		fig, ax = plt.subplots(figsize=(15, 10))
		ax.set_title(f'{args.disease} cases over time ({args.mode} Model)')
		df.plot(ax=ax)
		fig.savefig(f"{args.out if args.out != None else args.disease}.png")
		df.to_csv(f'out/{args.disease}-prediction.csv')

def filter_zeroes(arr):
	out = np.array(arr)
	for index in range(len(out)):
		if out[index] == 0:
			out[index] = None
	return out

def compose_df(prediction, actual, correction_factor, index):
	df_dict = {}

	for data in args.include_data:
		if data == 'Actual':
			df_dict['Actual'] = filter_zeroes(actual * correction_factor)
		elif data == 'S':
			df_dict['S'] = prediction.y[0] * int(args.population)
		elif data == 'I':
			df_dict['I'] = prediction.y[1] * int(args.population)
		elif data == 'R':
			df_dict['R'] = prediction.y[2] * int(args.population)
		elif data == 'E':
			df_dict['E'] = prediction.y[3] * int(args.population)

	return pd.DataFrame(df_dict, index=index)

# Loss Functions - used to "train" the model

def loss_linear(point, confirmed, recovered):
	size = len(confirmed)
	beta, gamma = point
	def model(t, y):
		S = y[0]
		I = y[1]
		R = y[2]
		return [-beta * S, beta * S - gamma * I, gamma * I]
	solution = solve_ivp(model, [0, size], [S_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True)
	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.population)))**2))
	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.population)))**2))
	return sol_inf * 0.5 + sol_rec * 0.5

def loss_sir(point, confirmed, recovered):
	size = len(confirmed)
	beta, gamma = point
	def model(t, y):
		S = y[0]
		I = y[1]
		R = y[2]
		return [-beta * S * I, beta * S * I - gamma * I, gamma * I]
	solution = solve_ivp(model, [0, size], [S_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True)
	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.population)))**2))
	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.population)))**2))
	return sol_inf * 0.5 + sol_rec * 0.5

def loss_esir(point, confirmed, recovered):
	size = len(confirmed)
	beta, gamma, mu = point
	def model(t, y):
		S = y[0]
		I = y[1]
		R = y[2]
		return [mu - beta * S * I - mu * S, beta * S * I - gamma * I - mu * I, gamma * I - mu * R]
	solution = solve_ivp(model, [0, size], [S_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True)
	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.population)))**2))
	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.population)))**2))
	return sol_inf * 0.5 + sol_rec * 0.5

def loss_seir(point, confirmed, recovered, exposed):
	size = len(confirmed)
	beta, gamma, mu, sigma = point
	def model(t, y):
		S = y[0]
		I = y[1]
		R = y[2]
		E = y[3]
		return [mu - beta * S * I - mu * S, beta * S * I - sigma * E - mu * E, sigma * E * I - gamma * I - mu * I, gamma * I - mu * R]
	solution = solve_ivp(model, [0, size], [S_0,E_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True)
	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.population)))**2))
	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.population)))**2))
	sol_exp = np.sqrt(np.mean((solution.y[3] - (exposed.values * correction_factor/int(args.population)))**2))
	return sol_inf/3 + sol_rec/3 + sol_exp/3

my_learner = Learner(args.country)
my_learner.train()