22 files changed, 195 insertions, 199 deletions

diff --git a/COVID-19-SIR.png b/COVID-19-SIR.png
new file mode 100644
index 0000000..4497cb1
--- /dev/null
+++ b/COVID-19-SIR.png
diff --git a/COVID-19.png b/COVID-19.png
deleted file mode 100644
index b57bf1a..0000000
--- a/COVID-19.png
+++ /dev/null
diff --git a/2020Unit08-CalcIII.LinearCompartmentModels.mw b/LinearCompartmentModels.mw
index 1b482d4..1b482d4 100644
--- a/2020Unit08-CalcIII.LinearCompartmentModels.mw
+++ b/LinearCompartmentModels.mw
diff --git a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel01.mw b/NLCModel01.mw
index b371730..b371730 100644
--- a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel01.mw
+++ b/NLCModel01.mw
diff --git a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel02.mw b/NLCModel02.mw
index ee5d005..ee5d005 100644
--- a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel02.mw
+++ b/NLCModel02.mw
diff --git a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel03.mw b/NLCModel03.mw
index d17ba93..d17ba93 100644
--- a/2020Unit08-CalcIII.InfectiousDiseaseNLCModel03.mw
+++ b/NLCModel03.mw
diff --git a/SARS-SIR.png b/SARS-SIR.png
new file mode 100644
index 0000000..d9332e0
--- /dev/null
+++ b/SARS-SIR.png
diff --git a/SARS.png b/SARS.png
deleted file mode 100644
index 53eb45c..0000000
--- a/SARS.png
+++ /dev/null
diff --git a/TEST.png b/TEST.png
deleted file mode 100644
index dab8c0b..0000000
--- a/TEST.png
+++ /dev/null
diff --git a/out/COVID-19-SIR-data.csv b/out/COVID-19-SIR-data.csv
new file mode 100644
index 0000000..68fc931
--- /dev/null
+++ b/out/COVID-19-SIR-data.csv
@@ -0,0 +1,3 @@
+Beta: 0.11506051868918243
+Gamma: 0.004484787817138545
+R0: 25.65573297570077
\ No newline at end of file
diff --git a/out/COVID-19-SIR-prediction.csv b/out/COVID-19-SIR-prediction.csv
new file mode 100644
index 0000000..dda0000
--- /dev/null
+++ b/out/COVID-19-SIR-prediction.csv
@@ -0,0 +1,78 @@
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diff --git a/out/COVID-19-data.csv b/out/COVID-19-data.csv
deleted file mode 100644
index 7809e69..0000000
--- a/out/COVID-19-data.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Beta: 0.12123601582761864
-Gamma: 0.004788532110372095
-R0: 25.317991616891955
\ No newline at end of file
diff --git a/out/COVID-19-prediction.csv b/out/COVID-19-prediction.csv
deleted file mode 100644
index adec705..0000000
--- a/out/COVID-19-prediction.csv
+++ /dev/null
@@ -1,78 +0,0 @@
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diff --git a/out/SARS-SIR-data.csv b/out/SARS-SIR-data.csv
new file mode 100644
index 0000000..b8e7f28
--- /dev/null
+++ b/out/SARS-SIR-data.csv
@@ -0,0 +1,3 @@
+Beta: 0.031662971068960946
+Gamma: 0.018519768809938723
+R0: 1.7096850070811285
\ No newline at end of file
diff --git a/out/SARS-SIR-prediction.csv b/out/SARS-SIR-prediction.csv
new file mode 100644
index 0000000..9a3a4c5
--- /dev/null
+++ b/out/SARS-SIR-prediction.csv
@@ -0,0 +1,77 @@
+,Actual,S,I,R
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+4/22/03,717.0,9342.024515976005,559.894186830479,98.08129719351507
+4/23/03,729.0,9325.38611794234,566.1060158201255,108.5078662375327
+4/24/03,744.0,9308.593695690377,572.3564692682052,119.04983504141725
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+4/28/03,778.5,9257.291082520527,591.3356116538148,151.37330582565713
+4/29/03,786.0,9239.88140918478,597.736342445242,162.38224836997682
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+6/26/03,877.5,8254.051255420887,923.6310079729482,822.3177366061623
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+6/30/03,877.5,8205.547573180975,937.6665140743783,856.7859127446457
+7/1/03,877.5,8181.130468913446,944.6552662718534,874.214264814699
+7/2/03,877.5,8156.605774951346,951.622547337333,891.7716777113201
+7/3/03,877.5,8131.9754820857925,958.5667310538335,909.4577868603722
+7/4/03,877.5,8107.241622298517,965.4861725686861,927.2722051327953
+7/7/03,877.5,8082.406268761857,972.3792083935365,945.2145228446052
+7/8/03,877.5,8057.47153583876,979.2441564043446,963.2843077568929
+7/9/03,877.5,8032.439579082788,986.0793158413854,981.4811050758253
+7/10/03,877.5,8007.312595238105,992.8829673092482,999.8044374526455
+7/11/03,877.5,7982.09282223949,999.6533727768367,1018.2538049836719
+8/7/03,877.5,7956.78253921233,1006.3887755773695,1036.8286852102988
diff --git a/out/SARS-data.csv b/out/SARS-data.csv
deleted file mode 100644
index 65b5f2f..0000000
--- a/out/SARS-data.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Beta: 1e-08
-Gamma: 0.0061248427719888315
-R0: 1.6326949723074842e-06
\ No newline at end of file
diff --git a/out/SARS-prediction.csv b/out/SARS-prediction.csv
deleted file mode 100644
index f677152..0000000
--- a/out/SARS-prediction.csv
+++ /dev/null
@@ -1,77 +0,0 @@
-,Actual,S,I,R
-4/10/03,0.1566473081148956,9999.0,1.0,0.0
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diff --git a/out/TEST-data.csv b/out/TEST-data.csv
deleted file mode 100644
index bc98828..0000000
--- a/out/TEST-data.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Beta: 0.4000000000000001
-Gamma: 1.0000000000049032e-08
-R0: 39999999.99980388
\ No newline at end of file
diff --git a/out/TEST-prediction.csv b/out/TEST-prediction.csv
deleted file mode 100644
index dd28231..0000000
--- a/out/TEST-prediction.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-,Actual,S,I,R
-1/22/20,1,13499.000074068588,0.9999259314124879,0.0
-1/23/20,2,13498.508297406446,1.4917025812581148,1.2295557754239627e-08
-1/24/20,3,13497.774897248144,2.225102721222464,3.06330734100344e-08
diff --git a/sir_model.cpp b/sir_model.cpp
index f054c36..9525347 100644
--- a/sir_model.cpp
+++ b/sir_model.cpp
@@ -6,12 +6,12 @@ using namespace std;
 const int DAYS = 200, ITER = 10000;
 
 // Population size
-const int POP = 2000;
+const int POP = 10000;
 
 // Model parameters
 // Beta = infection rate
 // Gamma = removal rate
-const double beta = 0.42, gamma = 0.25;
+const double beta = 0.3944, gamma = 0.02664;
 
 // Compartments
 double S[DAYS + 1], I[DAYS + 1], R[DAYS + 1];
diff --git a/sir_model.exe b/sir_model.exe
new file mode 100644
index 0000000..9acbc84
--- /dev/null
+++ b/sir_model.exe
diff --git a/solver2.py b/solver2.py
index 906673c..a90dfe1 100644
--- a/solver2.py
+++ b/solver2.py
@@ -1,3 +1,4 @@
+#!/usr/bin/python3
 from datetime import datetime, timedelta
 
 import matplotlib.pyplot as plt
@@ -20,17 +21,19 @@ parser.add_argument('--end', '-e', dest = 'end', default = None, help = 'the dat
 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)')
+parser.add_argument('--popcountry', '-pc', dest = 'popcountry', default = '3328200000', help = 'the population of the country (defaults to US population)')
+parser.add_argument('--popmodel', '-pm', dest = 'popmodel', default = '10000', help = 'the population of the model (defaults to 10000)')
+parser.add_argument('--initial', '-I', dest = 'initial', default = '1', help = 'initial infected people (defaults to 1')
 args = parser.parse_args()
 
-S_0 = (int(args.population) - 1) / int(args.population)
-I_0 = 1 / int(args.population)
+# 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.popmodel) / int(args.popcountry)
+
+S_0 = (int(args.popcountry) - int(args.initial)) / int(args.popcountry)
+I_0 = int(args.initial) / int(args.popcountry)
 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
@@ -155,9 +158,9 @@ class Learner(object):
 			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}')
+			print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, 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:
+			with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file:
 				file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}')
 		elif args.mode == 'SIR':
 			optimal = minimize(
@@ -170,9 +173,9 @@ class Learner(object):
 			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}')
+			print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, 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:
+			with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file:
 				file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}')
 		elif args.mode == 'ESIR':
 			optimal = minimize(
@@ -185,9 +188,9 @@ class Learner(object):
 			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}')
+			print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, 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:
+			with open(f'out/{args.disease}-{args.mode}-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)
@@ -202,15 +205,15 @@ class Learner(object):
 			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}')
+			print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, 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:
+			with open(f'out/{args.disease}-{args.mode}-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')
+		fig.savefig(f"{args.out if args.out != None else args.disease}-{args.mode}.png")
+		df.to_csv(f'out/{args.disease}-{args.mode}-prediction.csv')
 
 def filter_zeroes(arr):
 	out = np.array(arr)
@@ -226,13 +229,13 @@ def compose_df(prediction, actual, correction_factor, index):
 		if data == 'Actual':
 			df_dict['Actual'] = filter_zeroes(actual * correction_factor)
 		elif data == 'S':
-			df_dict['S'] = prediction.y[0] * int(args.population)
+			df_dict['S'] = prediction.y[0] * int(args.popmodel)
 		elif data == 'I':
-			df_dict['I'] = prediction.y[1] * int(args.population)
+			df_dict['I'] = prediction.y[1] * int(args.popmodel)
 		elif data == 'R':
-			df_dict['R'] = prediction.y[2] * int(args.population)
+			df_dict['R'] = prediction.y[2] * int(args.popmodel)
 		elif data == 'E':
-			df_dict['E'] = prediction.y[3] * int(args.population)
+			df_dict['E'] = prediction.y[3] * int(args.popmodel)
 
 	return pd.DataFrame(df_dict, index=index)
 
@@ -247,8 +250,8 @@ def loss_linear(point, confirmed, recovered):
 		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))
+	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.popmodel)))**2))
+	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.popmodel)))**2))
 	return sol_inf * 0.5 + sol_rec * 0.5
 
 def loss_sir(point, confirmed, recovered):
@@ -260,8 +263,8 @@ def loss_sir(point, confirmed, recovered):
 		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))
+	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.popmodel)))**2))
+	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.popmodel)))**2))
 	return sol_inf * 0.5 + sol_rec * 0.5
 
 def loss_esir(point, confirmed, recovered):
@@ -273,8 +276,8 @@ def loss_esir(point, confirmed, recovered):
 		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))
+	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.popmodel)))**2))
+	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.popmodel)))**2))
 	return sol_inf * 0.5 + sol_rec * 0.5
 
 def loss_seir(point, confirmed, recovered, exposed):
@@ -287,9 +290,9 @@ def loss_seir(point, confirmed, recovered, exposed):
 		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))
+	sol_inf = np.sqrt(np.mean((solution.y[1] - (confirmed.values.flatten() * correction_factor/int(args.popmodel)))**2))
+	sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/int(args.popmodel)))**2))
+	sol_exp = np.sqrt(np.mean((solution.y[3] - (exposed.values * correction_factor/int(args.popmodel)))**2))
 	return sol_inf/3 + sol_rec/3 + sol_exp/3
 
 my_learner = Learner(args.country)