diff options
| -rw-r--r-- | COVID-19.png | bin | 42113 -> 40198 bytes | |||
| -rw-r--r-- | SARS.png | bin | 0 -> 28850 bytes | |||
| -rw-r--r-- | out/COVID-19-data.csv | 6 | ||||
| -rw-r--r-- | out/COVID-19-prediction.csv | 154 | ||||
| -rw-r--r-- | out/SARS-data.csv | 3 | ||||
| -rw-r--r-- | out/SARS-prediction.csv | 10 | ||||
| -rw-r--r-- | solver2.py | 54 |
7 files changed, 120 insertions, 107 deletions
diff --git a/COVID-19.png b/COVID-19.png Binary files differindex 4e7413e..b57bf1a 100644 --- a/COVID-19.png +++ b/COVID-19.png diff --git a/SARS.png b/SARS.png Binary files differnew file mode 100644 index 0000000..c166acf --- /dev/null +++ b/SARS.png diff --git a/out/COVID-19-data.csv b/out/COVID-19-data.csv index 7cfc7a0..7809e69 100644 --- a/out/COVID-19-data.csv +++ b/out/COVID-19-data.csv @@ -1,3 +1,3 @@ -Beta: 0.10176825591408271 -Gamma: 0.003925680724581611 -R0: 25.9237220380241
\ No newline at end of file +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 index ff5fb0b..adec705 100644 --- a/out/COVID-19-prediction.csv +++ b/out/COVID-19-prediction.csv @@ -1,78 +1,78 @@ ,Actual,S,I,R -1/22/20,0.004129051987767584,13499.000074068588,0.9999259314124879,0.0 -1/23/20,0.004129051987767584,13498.89317704777,1.1026991091086147,0.004123843121000688 -1/24/20,0.008258103975535168,13498.775294623783,1.2160338603561553,0.008671515861079792 -1/25/20,0.008258103975535168,13498.64529772263,1.341015689947722,0.013686587421359685 -1/26/20,0.02064525993883792,13498.501773727428,1.479002645211681,0.019223627359204125 -1/27/20,0.02064525993883792,13498.343097865096,1.6315566734055231,0.02534546149822523 -1/28/20,0.02064525993883792,13498.168236409509,1.7996717065348078,0.032091883956462224 -1/29/20,0.02064525993883792,13497.975885292719,1.9846015727694382,0.039513134511468394 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+4/1/20,3262.4159021406726,46688.835373177644,3175.8731871739956,135.29143964835436 +4/2/20,3725.0152905198775,46309.086003779696,3539.553371809094,151.36062441120953 +4/3/20,4213.792048929664,45889.772400441274,3940.960819790049,169.26677976866858 +4/4/20,4722.400611620795,45427.94888900639,4382.85459886964,189.1965121239631 +4/5/20,5153.899082568807,44920.61512445932,4868.036249065472,211.3486264751976 +4/6/20,5606.422018348624,44364.71609092467,5399.349782659975,235.93412641534957 +4/7/20,6058.4250764526,43757.14210166731,5979.681684200406,263.1762141322693 diff --git a/out/SARS-data.csv b/out/SARS-data.csv new file mode 100644 index 0000000..f98e7f5 --- /dev/null +++ b/out/SARS-data.csv @@ -0,0 +1,3 @@ +Beta: 1e-08 +Gamma: 0.01700912239686379 +R0: 5.879198095396057e-07
\ No newline at end of file diff --git a/out/SARS-prediction.csv b/out/SARS-prediction.csv new file mode 100644 index 0000000..2849d47 --- /dev/null +++ b/out/SARS-prediction.csv @@ -0,0 +1,10 @@ +,Actual,S,I,R +4/10/03,0.21148953068592058,13499.000074068588,0.9999259314124879,0.0 +4/11/03,0.2244162454873646,13499.000074058673,0.98306190629524,0.016864035031213732 +4/12/03,0.23479999999999998,13499.000074048927,0.9664822974727793,0.033443653600438765 +4/14/03,0.2521768953068592,13499.000074039344,0.9501823087970815,0.049743651858518506 +4/15/03,0.2610772563176895,13499.000074029924,0.9341572239618826,0.06576874611448988 +4/16/03,0.2687061371841155,13499.000074020661,0.9184024060302004,0.08152357330806109 +4/17/03,0.27485162454873646,13499.000074011556,0.9029132970915866,0.09701269135235983 +4/18/03,0.2877783393501805,13499.000074002603,0.8876854169726223,0.11224058042343865 +4/19/03,0.2877783393501805,13499.000073993802,0.872714359634142,0.12721164656305334 @@ -8,7 +8,6 @@ from scipy.optimize import minimize import argparse import os -# TODO - Parse arguments for different model options 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']) @@ -20,16 +19,17 @@ parser.add_argument('--start', '-s', dest = 'start', default = '1/22/20', help = 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 = 13500/13501 -I_0 = 1/13501 -R_0 = 0 # Both are equal to 0/13501 -E_0 = 0 # Both are equal to 0/13501 +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 3.27 million population is quite hard, so here we've reduced the population to 13.5 thousand people, and modified -# the actual stats to match -correction_factor = 13502/3270000 if args.disease == 'COVID-19' else 1 +# 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): @@ -45,7 +45,7 @@ class Learner(object): 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) + 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()) @@ -155,7 +155,7 @@ 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] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') + 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}') @@ -170,7 +170,7 @@ 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] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') + 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}') @@ -185,7 +185,7 @@ 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] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') + 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)}') @@ -202,7 +202,7 @@ 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] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') + 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))}') @@ -226,13 +226,13 @@ 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] * 13500 + df_dict['S'] = prediction.y[0] * int(args.population) elif data == 'I': - df_dict['I'] = prediction.y[1] * 13500 + df_dict['I'] = prediction.y[1] * int(args.population) elif data == 'R': - df_dict['R'] = prediction.y[2] * 13500 + df_dict['R'] = prediction.y[2] * int(args.population) elif data == 'E': - df_dict['E'] = prediction.y[3] * 13500 + df_dict['E'] = prediction.y[3] * int(args.population) return pd.DataFrame(df_dict, index=index) @@ -247,8 +247,8 @@ 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/13500))**2)) - sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/13500))**2)) + 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): @@ -260,8 +260,8 @@ 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/13500))**2)) - sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/13500))**2)) + 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): @@ -273,8 +273,8 @@ 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/13500))**2)) - sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/13500))**2)) + 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): @@ -287,10 +287,10 @@ 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/13500))**2)) - sol_rec = np.sqrt(np.mean((solution.y[2] - (recovered.values * correction_factor/13500))**2)) - sol_exp = np.sqrt(np.mean((solution.y[3] - (exposed.values * correction_factor/13500))**2)) + 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('Hong_Kong') +my_learner = Learner(args.country) my_learner.train() |
