diff options
| -rw-r--r-- | COVID-19-SEIR.png | bin | 39490 -> 40300 bytes | |||
| -rw-r--r-- | README.md | 20 | ||||
| -rw-r--r-- | SARS-SEIR.png | bin | 35105 -> 41265 bytes | |||
| -rw-r--r-- | SARS-SIR.png | bin | 41624 -> 41666 bytes | |||
| -rw-r--r-- | out/COVID-19-ESIR-data.csv | 3 | ||||
| -rw-r--r-- | out/COVID-19-Linear-data.csv | 3 | ||||
| -rw-r--r-- | out/COVID-19-SEIR-data.csv | 11 | ||||
| -rw-r--r-- | out/COVID-19-SEIR-prediction.csv | 152 | ||||
| -rw-r--r-- | out/COVID-19-SIR-data.csv | 3 | ||||
| -rw-r--r-- | out/SARS-ESIR-data.csv | 3 | ||||
| -rw-r--r-- | out/SARS-Linear-data.csv | 3 | ||||
| -rw-r--r-- | out/SARS-SEIR-data.csv | 11 | ||||
| -rw-r--r-- | out/SARS-SEIR-prediction.csv | 150 | ||||
| -rw-r--r-- | out/SARS-SIR-data.csv | 7 | ||||
| -rw-r--r-- | out/SARS-SIR-prediction.csv | 150 | ||||
| -rw-r--r-- | solver2.py | 46 |
16 files changed, 284 insertions, 278 deletions
diff --git a/COVID-19-SEIR.png b/COVID-19-SEIR.png Binary files differindex d44ed9d..765ea68 100644 --- a/COVID-19-SEIR.png +++ b/COVID-19-SEIR.png @@ -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. + + + + + +## 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 Binary files differindex b0187aa..9e5b74b 100644 --- a/SARS-SEIR.png +++ b/SARS-SEIR.png diff --git a/SARS-SIR.png b/SARS-SIR.png Binary files differindex d9332e0..1321854 100644 --- a/SARS-SIR.png +++ b/SARS-SIR.png 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: 0.005265008086429555 +R0: 46.4494661074415 +Predicted I: 1267.3615656181303, Actual I: 1320.7366666666667
\ No newline at end of file diff --git a/out/COVID-19-SEIR-prediction.csv b/out/COVID-19-SEIR-prediction.csv index 216cf0d..5a6f3a1 100644 --- a/out/COVID-19-SEIR-prediction.csv +++ b/out/COVID-19-SEIR-prediction.csv @@ -1,78 +1,78 @@ ,Actual,S,I,R 1/22/20,0.0033333333333333335,9999.666666666666,0.33333333333333337,0.0 -1/23/20,0.0033333333333333335,9999.627703932636,0.3722960708871821,-7.047500503241619e-09 -1/24/20,0.006666666666666667,9999.584187413606,0.41581259384888014,-1.49152950354593e-08 -1/25/20,0.006666666666666667,9999.535585180327,0.464414831515325,-2.3699210398033534e-08 -1/26/20,0.016666666666666666,9999.481297402239,0.5187026144994259,-3.350731144359509e-08 -1/27/20,0.016666666666666666,9999.420271502591,0.5797285196504275,-4.452958089143598e-08 -1/28/20,0.016666666666666666,9999.35189874109,0.648101287311564,-5.6875709818881055e-08 -1/29/20,0.016666666666666666,9999.275787997143,0.7242120381115329,-7.061590659430941e-08 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+4/1/20,711.2066666666667,9310.650698204987,695.4303515061353,-12.71637374733341 +4/2/20,812.0533333333334,9236.321441869342,770.4416733410799,-14.144215936599569 +4/3/20,918.6066666666667,9154.67862991096,852.8395994098861,-15.725284954048453 +4/4/20,1029.4833333333333,9065.170950913795,943.1824939167649,-17.47445257061634 +4/5/20,1123.5500000000002,8967.254451847157,1042.021850099939,-19.407643870288563 +4/6/20,1222.2,8860.39253806574,1149.9022902316447,-21.54183725009952 +4/7/20,1320.7366666666667,8744.0559733096,1267.3615656181303,-23.895064420132684 diff --git a/out/COVID-19-SIR-data.csv b/out/COVID-19-SIR-data.csv index 68fc931..a0805e1 100644 --- a/out/COVID-19-SIR-data.csv +++ b/out/COVID-19-SIR-data.csv @@ -1,3 +1,4 @@ Beta: 0.11506051868918243 Gamma: 0.004484787817138545 -R0: 25.65573297570077
\ No newline at end of file +R0: 25.65573297570077 +Predicted I: 1266.8105911330124, Actual I: 1320.7366666666667
\ 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 -4/18/03,679.0,9462.752972916978,537.247063243363,-7.244928711449601e-05 -4/19/03,679.0,9457.222811208285,542.7772303122033,-8.320953379882134e-05 -4/21/03,701.0,9451.639021404832,548.3610255258131,-9.407535747333604e-05 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+7/7/03,877.5,8232.369026859264,1017.5976333074824,706.2897995000255 +7/8/03,877.5,8212.623636674289,1016.4241606257575,726.9079011410773 +7/9/03,877.5,8192.976623841936,1015.0230170706917,747.6772421120531 +7/10/03,877.5,8173.432704147797,1013.401680335792,768.5860294222934 +7/11/03,877.5,8153.996486489892,1011.5680536896141,789.6221407582674 +8/7/03,877.5,8134.672472878669,1009.5304659757634,810.7731244835715 diff --git a/out/SARS-SIR-data.csv b/out/SARS-SIR-data.csv index b8e7f28..d6e9dfa 100644 --- a/out/SARS-SIR-data.csv +++ b/out/SARS-SIR-data.csv @@ -1,3 +1,4 @@ -Beta: 0.031662971068960946 -Gamma: 0.018519768809938723 -R0: 1.7096850070811285
\ No newline at end of file +Beta: 0.031663066064741584 +Gamma: 0.018520041050816886 +R0: 1.7096650044058612 +Predicted I: 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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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) |
