From 52147720caa2abf82014f7fda404c9499a850a16 Mon Sep 17 00:00:00 2001 From: Ta180m Date: Thu, 30 Apr 2020 12:44:50 -0500 Subject: Logged more data --- COVID-19-SEIR.png | Bin 39490 -> 40300 bytes README.md | 20 ++++-- SARS-SEIR.png | Bin 35105 -> 41265 bytes SARS-SIR.png | Bin 41624 -> 41666 bytes out/COVID-19-ESIR-data.csv | 3 +- out/COVID-19-Linear-data.csv | 3 +- out/COVID-19-SEIR-data.csv | 11 +-- out/COVID-19-SEIR-prediction.csv | 152 +++++++++++++++++++-------------------- out/COVID-19-SIR-data.csv | 3 +- out/SARS-ESIR-data.csv | 3 +- out/SARS-Linear-data.csv | 3 +- out/SARS-SEIR-data.csv | 11 +-- out/SARS-SEIR-prediction.csv | 150 +++++++++++++++++++------------------- out/SARS-SIR-data.csv | 7 +- out/SARS-SIR-prediction.csv | 150 +++++++++++++++++++------------------- solver2.py | 46 +++++------- 16 files changed, 284 insertions(+), 278 deletions(-) diff --git a/COVID-19-SEIR.png b/COVID-19-SEIR.png index d44ed9d..765ea68 100644 Binary files a/COVID-19-SEIR.png and b/COVID-19-SEIR.png differ diff --git a/README.md b/README.md index 2ba58f1..a22aa08 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,19 @@ # Infectious-Disease-Modeling -Original code by [JasonXu314](https://github.com/JasonXu314/covid-19-project/) and [Lewuathe](https://github.com/Lewuathe/COVID19-SIR) +A project to modeling infectious diseases with the SIR model and variations. + +![](https://raw.githubusercontent.com/Ta180m/Infectious-Disease-Modeling/master/SARS-ESIR.png) + +![](https://raw.githubusercontent.com/Ta180m/Infectious-Disease-Modeling/master/COVID-19-ESIR.png) + +## Usage For SARS in Hong Kong use -`./solver2.py --country=Hong_Kong --popcountry=20000 --initial=1000 --disease=SARS --start=4/10/03` +`./solver2.py --country=Hong_Kong --popcountry=20000 --initial=1000 --disease=SARS --start=4/10/03 --mode={SIR,Linear,ESIR,SEIR}` For COVID-19 in the US use -`./solver2.py --popcountry=3000000 --initial=100` - +`./solver2.py --popcountry=3000000 --initial=100 --mode={SIR,Linear,ESIR,SEIR}` -## Usage ``` usage: solver2.py [-h] [--mode {SIR,Linear,ESIR,SEIR}] [--data [{Actual,S,I,R,E} [{Actual,S,I,R,E} ...]]] @@ -56,4 +60,8 @@ optional arguments: the population of the model (defaults to 10000) --initial INITIAL, -I INITIAL initial infected people (defaults to 1) -``` \ No newline at end of file +``` + +## Credits + +Original code by [JasonXu314](https://github.com/JasonXu314/covid-19-project/) and [Lewuathe](https://github.com/Lewuathe/COVID19-SIR) \ No newline at end of file diff --git a/SARS-SEIR.png b/SARS-SEIR.png index b0187aa..9e5b74b 100644 Binary files a/SARS-SEIR.png and b/SARS-SEIR.png differ diff --git a/SARS-SIR.png b/SARS-SIR.png index d9332e0..1321854 100644 Binary files a/SARS-SIR.png and b/SARS-SIR.png differ diff --git a/out/COVID-19-ESIR-data.csv b/out/COVID-19-ESIR-data.csv index e16f0d7..3c6bb37 100644 --- a/out/COVID-19-ESIR-data.csv +++ b/out/COVID-19-ESIR-data.csv @@ -1,4 +1,5 @@ Beta: 0.11506079905613333 Gamma: 0.004485002592721715 Mu: 1e-08 -R0: 25.654509698111923 \ No newline at end of file +R0: 25.654509698111923 +Predicted I: 1266.8145664521824, Actual I: 1320.7366666666667 \ No newline at end of file diff --git a/out/COVID-19-Linear-data.csv b/out/COVID-19-Linear-data.csv index b204797..d0469e1 100644 --- a/out/COVID-19-Linear-data.csv +++ b/out/COVID-19-Linear-data.csv @@ -1,3 +1,4 @@ Beta: 0.0005306298181865554 Gamma: 0.00121221978960238 -R0: 0.4377340006638624 \ No newline at end of file +R0: 0.4377340006638624 +Predicted I: 377.76904311879434, Actual I: 1320.7366666666667 \ No newline at end of file diff --git a/out/COVID-19-SEIR-data.csv b/out/COVID-19-SEIR-data.csv index 98e5f69..4b2d494 100644 --- a/out/COVID-19-SEIR-data.csv +++ b/out/COVID-19-SEIR-data.csv @@ -1,5 +1,6 @@ -Beta: 0.11054937398441604 -Gamma: 1e-08 -Mu: 1e-08 -Sigma: 0.0009697697322082875 -R0: 5527411.7020644825 \ No newline at end of file +Beta: 0.11054352661813334 +Gamma: 0.001 +Mu: 0.0009999999999999992 +Sigma: 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\ No newline at end of file diff --git a/out/SARS-ESIR-data.csv b/out/SARS-ESIR-data.csv index 3ec411f..21d4469 100644 --- a/out/SARS-ESIR-data.csv +++ b/out/SARS-ESIR-data.csv @@ -1,4 +1,5 @@ Beta: 0.2367418297398915 Gamma: 0.08679182290949222 Mu: 0.10967338538708958 -R0: 1.205006381498899 \ No newline at end of file +R0: 1.205006381498899 +Predicted I: 934.6265172952101, Actual I: 877.5 \ No newline at end of file diff --git a/out/SARS-Linear-data.csv b/out/SARS-Linear-data.csv index 47d9faa..a322350 100644 --- a/out/SARS-Linear-data.csv +++ b/out/SARS-Linear-data.csv @@ -1,3 +1,4 @@ Beta: 0.00225573366659839 Gamma: 0.01741524200420521 -R0: 0.12952640371312119 \ No newline at end of file +R0: 0.12952640371312119 +Predicted I: 946.0965063014658, Actual I: 877.5 \ No newline at end of file diff --git a/out/SARS-SEIR-data.csv b/out/SARS-SEIR-data.csv index face094..36fe951 100644 --- a/out/SARS-SEIR-data.csv +++ b/out/SARS-SEIR-data.csv @@ -1,5 +1,6 @@ -Beta: 0.01082548864886504 -Gamma: 1e-08 -Mu: 1e-08 -Sigma: 0.0010246938312194157 -R0: 541269.1501908005 \ No newline at end of file +Beta: 0.025728454417841363 +Gamma: 0.001 +Mu: 0.001 +Sigma: 0.5156784257190153 +R0: 12.83932927130887 +Predicted I: 1009.5304659757634, Actual I: 877.5 \ No newline at end of file diff --git a/out/SARS-SEIR-prediction.csv b/out/SARS-SEIR-prediction.csv index fb009fd..b5ab71d 100644 --- a/out/SARS-SEIR-prediction.csv +++ b/out/SARS-SEIR-prediction.csv @@ -1,77 +1,77 @@ ,Actual,S,I,R 4/10/03,499.0,9500.0,500.0,0.0 -4/11/03,529.5,9494.832776379075,505.16722864414726,-1.0049017893654477e-05 -4/12/03,554.0,9489.615021231453,510.38498886174114,-2.0196708490327354e-05 -4/14/03,595.0,9484.346299922172,515.6537152881966,-3.044403329478051e-05 -4/15/03,616.0,9479.026175635721,520.9738447394818,-4.07919681313532e-05 -4/16/03,634.0,9473.654208751632,526.3458168365376,-5.124149936138442e-05 -4/17/03,648.5,9468.229956265563,531.7700745841634,-6.179360882997584e-05 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1006.3748700651734, Actual I: 877.5 \ No newline at end of file diff --git a/out/SARS-SIR-prediction.csv b/out/SARS-SIR-prediction.csv index 9a3a4c5..ba1c85f 100644 --- a/out/SARS-SIR-prediction.csv +++ b/out/SARS-SIR-prediction.csv @@ -1,77 +1,77 @@ ,Actual,S,I,R 4/10/03,499.0,9500.0,500.0,0.0 -4/11/03,529.5,9484.884979595565,505.80148109959003,9.313539304844733 -4/12/03,554.0,9469.619417390539,511.64566678773895,18.73491582172085 -4/14/03,595.0,9454.202845099702,517.5322376669819,28.26491723331429 -4/15/03,616.0,9438.634815392412,523.4608571171718,37.90432749041551 -4/16/03,634.0,9422.91490886804,529.4311682856298,47.65392284632816 -4/17/03,648.5,9407.04273428322,535.4427939878531,57.51447172892478 -4/18/03,679.0,9391.017928551839,541.4953367075137,67.48673474064691 -4/19/03,679.0,9374.840156745033,547.5883785964601,77.57146465850494 -4/21/03,701.0,9358.509112091206,553.7214814747153,87.76940643407822 -4/22/03,717.0,9342.024515976005,559.894186830479,98.08129719351507 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4074988..4f010ae 100644 --- a/solver2.py +++ b/solver2.py @@ -18,7 +18,7 @@ parser.add_argument('--disease', '-D', dest = 'disease', default = 'COVID-19', h 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('--incubation', '-i', dest = 'incubation_period', default = None, help = 'the incubation period of the disease (only applicable if using SEIR 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('--popcountry', '-pc', dest = 'popcountry', default = '3328200000', help = 'the population of the country (defaults to US population)') @@ -72,20 +72,6 @@ class Learner(object): 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 @@ -131,7 +117,7 @@ class Learner(object): 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)) + result = solve_ivp(model, [0, size], [S_0,I_0,R_0,E_0], t_eval=np.arange(0, size, 1), vectorized=True) else: result = solve_ivp(model, [0, size], [S_0,I_0,R_0], t_eval=np.arange(0, size, 1), vectorized=True) @@ -150,10 +136,10 @@ class Learner(object): if args.mode == 'Linear': optimal = minimize( loss_linear, - [0.001, 0.001], + [0.01, 0.01], args=(confirmed_data, recovered_data), method='L-BFGS-B', - bounds=[(0.00000001, 0.4), (0.00000001, 0.4)] + bounds=[(0.001, 1.0), (0.001, 1.0)] ) beta, gamma = optimal.x print(f'Beta: {beta}, Gamma: {gamma}, R0: {beta/gamma}') @@ -161,14 +147,15 @@ class Learner(object): 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}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}') + file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}\n') + file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') elif args.mode == 'SIR': optimal = minimize( loss_sir, - [0.001, 0.001], + [0.01, 0.01], args=(confirmed_data, recovered_data), method='L-BFGS-B', - bounds=[(0.00000001, 0.4), (0.00000001, 0.4)] + bounds=[(0.001, 1.0), (0.001, 1.0)] ) beta, gamma = optimal.x print(f'Beta: {beta}, Gamma: {gamma}, R0: {beta/gamma}') @@ -176,14 +163,15 @@ class Learner(object): 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}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}') + file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}\n') + file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') elif args.mode == 'ESIR': optimal = minimize( loss_esir, - [0.001, 0.001, 0.001], + [0.01, 0.01, 0.01], args=(confirmed_data, recovered_data), method='L-BFGS-B', - bounds=[(0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4)] + bounds=[(0.001, 1.0), (0.001, 1.0), (0.001, 1.0)] ) beta, gamma, mu = optimal.x print(f'Beta: {beta}, Gamma: {gamma}, Mu: {mu} R0: {beta/(gamma + mu)}') @@ -191,16 +179,17 @@ class Learner(object): 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}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nR0: {beta/(gamma + mu)}') + file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nR0: {beta/(gamma + mu)}\n') + file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') elif args.mode == 'SEIR': # exposed_data = self.load_exposed(self.country) optimal = minimize( loss_seir, - [0.001, 0.001, 0.001, 0.001], + [0.01, 0.01, 0.01, 0.01], args=(confirmed_data, recovered_data), method='L-BFGS-B', - bounds=[(0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4), (0.00000001, 0.4)] + bounds=[(0.001, 1.0), (0.001, 1.0), (0.001, 1.0), (0.001, 1.0)] ) beta, gamma, mu, sigma = optimal.x print(f'Beta: {beta}, Gamma: {gamma}, Mu: {mu}, Sigma: {sigma} R0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}') @@ -208,7 +197,8 @@ class Learner(object): 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}-{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))}') + file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nSigma: {sigma}\nR0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}\n') + file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') fig, ax = plt.subplots(figsize=(15, 10)) ax.set_title(f'{args.disease} cases over time ({args.mode} Model)') df.plot(ax=ax) -- cgit v1.2.3-70-g09d2