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Hopefully fixed everything

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Anthony Wang 2020-04-29 19:09:32 -05:00
parent 7aab8f8c52
commit 9a82dd97da
22 changed files with 195 additions and 199 deletions
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2020Unit08-CalcIII.LinearCompartmentModels.mw → LinearCompartmentModels.mw
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out/COVID-19-SIR-data.csv Normal file
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Beta: 0.11506051868918243
Gamma: 0.004484787817138545
R0: 25.65573297570077
1 Beta: 0.11506051868918243
2 Gamma: 0.004484787817138545
3 R0: 25.65573297570077

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out/COVID-19-SIR-prediction.csv Normal file
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,Actual,S,I,R
1/22/20,0.0033333333333333335,9999.666666666666,0.33333333333333337,0.0
1/23/20,0.0033333333333333335,9999.626113483311,0.3723057932824626,0.0015807234055033998
1/24/20,0.006666666666666667,9999.580819471055,0.4158342816934694,0.003346247251000438
1/25/20,0.006666666666666667,9999.530230573782,0.46445125535619813,0.00531817086165759
1/26/20,0.016666666666666666,9999.473709826556,0.5187688441820978,0.007521329263086074
1/27/20,0.016666666666666666,9999.410196574465,0.5798062772339272,0.009997148300881093
1/28/20,0.016666666666666666,9999.339138930427,0.6480939379280791,0.012767131645821241
1/29/20,0.016666666666666666,9999.260060733477,0.7240894731817856,0.01584979334159779
1/30/20,0.016666666666666666,9999.172254217212,0.8084730926218743,0.01927269016575604
1/31/20,0.023333333333333334,9999.074780009785,0.9021475685847693,0.023072421629695316
2/1/20,0.02666666666666667,9998.966467133905,1.0062382361164908,0.02729462997866896
2/2/20,0.02666666666666667,9998.845913006835,1.1220929929726546,0.03199400019178435
2/3/20,0.03666666666666667,9998.711483440398,1.2512822996184734,0.037234259982002894
2/4/20,0.03666666666666667,9998.561312640975,1.3955991792287554,0.043088179796139985
2/5/20,0.03666666666666667,9998.393303209497,1.5570592176879055,0.0496375728148651
2/6/20,0.03666666666666667,9998.205126141458,1.7379005635899234,0.056973294952701675
2/7/20,0.03666666666666667,9997.994220826904,1.9405839282384063,0.06519524485802725
2/8/20,0.03666666666666667,9997.75779505044,2.1677925856465476,0.07441236391307328
2/9/20,0.03666666666666667,9997.492824991228,2.422432372537136,0.0847426362339254
2/10/20,0.03666666666666667,9997.196055222987,2.7076316883425555,0.0963130886705231
2/11/20,0.04,9996.863998713989,3.0267414952047873,0.10925979080666
2/12/20,0.04,9996.492936827064,3.38333531797541,0.12372785495998372
2/13/20,0.043333333333333335,9996.078919319601,3.7812092442155962,0.13987143618199585
2/14/20,0.043333333333333335,9995.617764343546,4.2243819241961145,0.15785373225805216
2/15/20,0.043333333333333335,9995.1003045259,4.721655585099373,0.17803988899952877
2/16/20,0.043333333333333335,9994.516837015459,5.282346835600999,0.2008161489413117
2/17/20,0.043333333333333335,9993.866263436728,5.907518666955111,0.22621789631618436
2/18/20,0.043333333333333335,9993.14533367619,6.600300535008138,0.2543657888027925
2/19/20,0.043333333333333335,9992.348486073488,7.366041685509018,0.2854722410034983
2/20/20,0.043333333333333335,9991.467847421447,8.212311154109193,0.31984142444438063
2/21/20,0.05,9990.493232966062,9.148897766362614,0.3578692675752351
2/22/20,0.05,9989.412146406505,10.187810137725744,0.4000434557695737
2/23/20,0.05,9988.209779895118,11.343276673557556,0.4469434313246251
2/24/20,0.05,9986.86901403742,12.631745569119527,0.4992403934613348
2/25/20,0.05,9985.3704178921,14.07188480957564,0.5576972983243645
2/26/20,0.05,9983.692248971025,15.684582169992398,0.6231688589820928
2/27/20,0.05333333333333334,9981.810453239235,17.492945215338796,0.6966015454266151
2/28/20,0.05333333333333334,9979.69866511494,19.522301300486347,0.7790335845737427
2/29/20,0.08,9977.328207469527,21.800197570209086,0.8715949602630044
3/1/20,0.1,9974.668091627558,24.356400959183517,0.9755074132576449
3/2/20,0.17666666666666667,9971.685017366766,27.222898191988705,1.092084441244626
3/3/20,0.24333333333333335,9968.34337291806,30.43389578310618,1.2227312988346257
3/4/20,0.3466666666666667,9964.605234965518,34.025820036919995,1.3689449975620385
3/5/20,0.5733333333333334,9960.430368646397,38.037317047716726,1.5323143058849769
3/6/20,0.7233333333333334,9955.77622755113,42.50925269968544,1.7145197491852677
3/7/20,1.12,9950.597953723314,47.4847126669177,1.9173336097684561
3/8/20,1.5,9944.8448045356,53.01237292907167,2.142822535329084
3/9/20,1.7133333333333334,9938.42938086884,59.17565842355453,2.3949607076053994
3/10/20,2.3600000000000003,9931.284416242248,66.03936148704915,2.6762222707021737
3/11/20,3.6833333333333336,9923.337837581861,73.67299461476044,2.9891678033797313
3/12/20,5.19,9914.501858677362,82.16105475639304,3.3370865662455413
3/13/20,7.156666666666667,9904.672980182095,91.60302331615122,3.7239965017542147
3/14/20,9.523333333333333,9893.731989613054,102.11336615273899,4.1546442342075025
3/15/20,9.726666666666667,9881.543961350886,113.82153357936006,4.634505069754301
3/16/20,14.356666666666667,9867.958256639891,126.87196036371769,5.169782996390647
3/17/20,20.32,9852.808523588024,141.42406572801508,5.767410683959723
3/18/20,29.576666666666668,9835.912697166894,157.65225334895476,6.435049484151843
3/19/20,46.980000000000004,9817.072999211756,175.7459113577394,7.181089430504479
3/20/20,64.67666666666668,9796.075938421527,195.90941234007096,8.014649238402235
3/21/20,85.75,9772.692310358772,218.3621133361513,8.94557630507686
3/22/20,112.11333333333334,9746.677197449711,243.33835584068188,9.984446709607242
3/23/20,145.54333333333335,9717.769968984217,271.0874658028638,11.14256521291942
3/24/20,179.12,9685.694281115815,301.87375362639807,12.431965257786567
3/25/20,219.26000000000002,9650.158076861686,335.9765141694853,13.865408968829001
3/26/20,279.4533333333334,9610.856608434478,373.6868457962786,15.456545769241968
3/27/20,338.8566666666667,9567.478576225396,415.2978095557358,17.223614218867255
3/28/20,404.9266666666667,9519.55452671549,461.25900332866496,19.186469955843926
3/29/20,469.6966666666667,9466.575218355096,512.0586800277034,21.366101617200858
3/30/20,539.4566666666667,9408.030818560183,568.1836003605466,23.78558107927107
3/31/20,627.24,9343.41090371236,630.1190328299489,26.470063457691705
4/1/20,711.2066666666667,9272.204459158873,698.3487537337221,29.44678710740408
4/2/20,812.0533333333334,9193.899879212608,773.3550471647377,32.745073622653635
4/3/20,918.6066666666667,9107.984967152086,855.6187050109245,36.39632783698995
4/4/20,1029.4833333333333,9013.946935221464,945.6190269552703,40.434037823266756
4/5/20,1123.5500000000002,8911.272404630537,1043.8338204758206,44.89377489364194
4/6/20,1222.2,8799.447405554742,1150.7394008456806,49.8131935995775
4/7/20,1320.7366666666667,8677.957377135148,1266.8105911330124,55.23203173183961
1 Actual S I R
2 1/22/20 0.0033333333333333335 9999.666666666666 0.33333333333333337 0.0
3 1/23/20 0.0033333333333333335 9999.626113483311 0.3723057932824626 0.0015807234055033998
4 1/24/20 0.006666666666666667 9999.580819471055 0.4158342816934694 0.003346247251000438
5 1/25/20 0.006666666666666667 9999.530230573782 0.46445125535619813 0.00531817086165759
6 1/26/20 0.016666666666666666 9999.473709826556 0.5187688441820978 0.007521329263086074
7 1/27/20 0.016666666666666666 9999.410196574465 0.5798062772339272 0.009997148300881093
8 1/28/20 0.016666666666666666 9999.339138930427 0.6480939379280791 0.012767131645821241
9 1/29/20 0.016666666666666666 9999.260060733477 0.7240894731817856 0.01584979334159779
10 1/30/20 0.016666666666666666 9999.172254217212 0.8084730926218743 0.01927269016575604
11 1/31/20 0.023333333333333334 9999.074780009785 0.9021475685847693 0.023072421629695316
12 2/1/20 0.02666666666666667 9998.966467133905 1.0062382361164908 0.02729462997866896
13 2/2/20 0.02666666666666667 9998.845913006835 1.1220929929726546 0.03199400019178435
14 2/3/20 0.03666666666666667 9998.711483440398 1.2512822996184734 0.037234259982002894
15 2/4/20 0.03666666666666667 9998.561312640975 1.3955991792287554 0.043088179796139985
16 2/5/20 0.03666666666666667 9998.393303209497 1.5570592176879055 0.0496375728148651
17 2/6/20 0.03666666666666667 9998.205126141458 1.7379005635899234 0.056973294952701675
18 2/7/20 0.03666666666666667 9997.994220826904 1.9405839282384063 0.06519524485802725
19 2/8/20 0.03666666666666667 9997.75779505044 2.1677925856465476 0.07441236391307328
20 2/9/20 0.03666666666666667 9997.492824991228 2.422432372537136 0.0847426362339254
21 2/10/20 0.03666666666666667 9997.196055222987 2.7076316883425555 0.0963130886705231
22 2/11/20 0.04 9996.863998713989 3.0267414952047873 0.10925979080666
23 2/12/20 0.04 9996.492936827064 3.38333531797541 0.12372785495998372
24 2/13/20 0.043333333333333335 9996.078919319601 3.7812092442155962 0.13987143618199585
25 2/14/20 0.043333333333333335 9995.617764343546 4.2243819241961145 0.15785373225805216
26 2/15/20 0.043333333333333335 9995.1003045259 4.721655585099373 0.17803988899952877
27 2/16/20 0.043333333333333335 9994.516837015459 5.282346835600999 0.2008161489413117
28 2/17/20 0.043333333333333335 9993.866263436728 5.907518666955111 0.22621789631618436
29 2/18/20 0.043333333333333335 9993.14533367619 6.600300535008138 0.2543657888027925
30 2/19/20 0.043333333333333335 9992.348486073488 7.366041685509018 0.2854722410034983
31 2/20/20 0.043333333333333335 9991.467847421447 8.212311154109193 0.31984142444438063
32 2/21/20 0.05 9990.493232966062 9.148897766362614 0.3578692675752351
33 2/22/20 0.05 9989.412146406505 10.187810137725744 0.4000434557695737
34 2/23/20 0.05 9988.209779895118 11.343276673557556 0.4469434313246251
35 2/24/20 0.05 9986.86901403742 12.631745569119527 0.4992403934613348
36 2/25/20 0.05 9985.3704178921 14.07188480957564 0.5576972983243645
37 2/26/20 0.05 9983.692248971025 15.684582169992398 0.6231688589820928
38 2/27/20 0.05333333333333334 9981.810453239235 17.492945215338796 0.6966015454266151
39 2/28/20 0.05333333333333334 9979.69866511494 19.522301300486347 0.7790335845737427
40 2/29/20 0.08 9977.328207469527 21.800197570209086 0.8715949602630044
41 3/1/20 0.1 9974.668091627558 24.356400959183517 0.9755074132576449
42 3/2/20 0.17666666666666667 9971.685017366766 27.222898191988705 1.092084441244626
43 3/3/20 0.24333333333333335 9968.34337291806 30.43389578310618 1.2227312988346257
44 3/4/20 0.3466666666666667 9964.605234965518 34.025820036919995 1.3689449975620385
45 3/5/20 0.5733333333333334 9960.430368646397 38.037317047716726 1.5323143058849769
46 3/6/20 0.7233333333333334 9955.77622755113 42.50925269968544 1.7145197491852677
47 3/7/20 1.12 9950.597953723314 47.4847126669177 1.9173336097684561
48 3/8/20 1.5 9944.8448045356 53.01237292907167 2.142822535329084
49 3/9/20 1.7133333333333334 9938.42938086884 59.17565842355453 2.3949607076053994
50 3/10/20 2.3600000000000003 9931.284416242248 66.03936148704915 2.6762222707021737
51 3/11/20 3.6833333333333336 9923.337837581861 73.67299461476044 2.9891678033797313
52 3/12/20 5.19 9914.501858677362 82.16105475639304 3.3370865662455413
53 3/13/20 7.156666666666667 9904.672980182095 91.60302331615122 3.7239965017542147
54 3/14/20 9.523333333333333 9893.731989613054 102.11336615273899 4.1546442342075025
55 3/15/20 9.726666666666667 9881.543961350886 113.82153357936006 4.634505069754301
56 3/16/20 14.356666666666667 9867.958256639891 126.87196036371769 5.169782996390647
57 3/17/20 20.32 9852.808523588024 141.42406572801508 5.767410683959723
58 3/18/20 29.576666666666668 9835.912697166894 157.65225334895476 6.435049484151843
59 3/19/20 46.980000000000004 9817.072999211756 175.7459113577394 7.181089430504479
60 3/20/20 64.67666666666668 9796.075938421527 195.90941234007096 8.014649238402235
61 3/21/20 85.75 9772.692310358772 218.3621133361513 8.94557630507686
62 3/22/20 112.11333333333334 9746.677197449711 243.33835584068188 9.984446709607242
63 3/23/20 145.54333333333335 9717.769968984217 271.0874658028638 11.14256521291942
64 3/24/20 179.12 9685.694281115815 301.87375362639807 12.431965257786567
65 3/25/20 219.26000000000002 9650.158076861686 335.9765141694853 13.865408968829001
66 3/26/20 279.4533333333334 9610.856608434478 373.6868457962786 15.456545769241968
67 3/27/20 338.8566666666667 9567.478576225396 415.2978095557358 17.223614218867255
68 3/28/20 404.9266666666667 9519.55452671549 461.25900332866496 19.186469955843926
69 3/29/20 469.6966666666667 9466.575218355096 512.0586800277034 21.366101617200858
70 3/30/20 539.4566666666667 9408.030818560183 568.1836003605466 23.78558107927107
71 3/31/20 627.24 9343.41090371236 630.1190328299489 26.470063457691705
72 4/1/20 711.2066666666667 9272.204459158873 698.3487537337221 29.44678710740408
73 4/2/20 812.0533333333334 9193.899879212608 773.3550471647377 32.745073622653635
74 4/3/20 918.6066666666667 9107.984967152086 855.6187050109245 36.39632783698995
75 4/4/20 1029.4833333333333 9013.946935221464 945.6190269552703 40.434037823266756
76 4/5/20 1123.5500000000002 8911.272404630537 1043.8338204758206 44.89377489364194
77 4/6/20 1222.2 8799.447405554742 1150.7394008456806 49.8131935995775
78 4/7/20 1320.7366666666667 8677.957377135148 1266.8105911330124 55.23203173183961

3
out/COVID-19-data.csv
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@ -1,3 +0,0 @@
Beta: 0.12123601582761864
Gamma: 0.004788532110372095
R0: 25.317991616891955
1 Beta: 0.12123601582761864
2 Gamma: 0.004788532110372095
3 R0: 25.317991616891955

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out/COVID-19-prediction.csv
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@ -1,78 +0,0 @@
,Actual,S,I,R
1/22/20,0.01529051987767584,49999.0,1.0,0.0
1/23/20,0.01529051987767584,49998.87142424206,1.1234972154720178,0.005078542461735381
1/24/20,0.03058103975535168,49998.726971267446,1.2622445067142218,0.010784225844925973
1/25/20,0.03058103975535168,49998.564682877775,1.418122720321673,0.017194401905434777
1/26/20,0.0764525993883792,49998.38234997699,1.5932536885634114,0.024396334439772325
1/27/20,0.0764525993883792,49998.1761616234,1.7912976312291053,0.032540745365268506
1/28/20,0.0764525993883792,49997.94267086507,2.0155651686230516,0.04176396630732312
1/29/20,0.0764525993883792,49997.68103501622,2.2668658997885043,0.052099083983799914
1/30/20,0.0764525993883792,49997.389420767366,2.5469608746381214,0.06361835799887736
1/31/20,0.10703363914373089,49997.064956030794,2.858608840222778,0.07643512898078467
2/1/20,0.12232415902140673,49996.70372994068,3.2055662407315637,0.09070381858180188
2/2/20,0.12232415902140673,49996.30079285303,3.592587217491785,0.10661992947825968
2/3/20,0.16819571865443425,49995.85015634566,4.025423608968964,0.12442004537053958
2/4/20,0.16819571865443425,49995.344793218246,4.510824950766836,0.14438183098307383
2/5/20,0.16819571865443425,49994.77663749231,5.05653847562736,0.16682403206434543
2/6/20,0.16819571865443425,49994.13658441118,5.671309113430702,0.1921064753868881
2/7/20,0.16819571865443425,49993.41449044005,6.36487949119525,0.2206300687472865
2/8/20,0.16819571865443425,49992.59917326595,7.1479899330776036,0.2528368009661757
2/9/20,0.16819571865443425,49991.67841179774,8.032378460372584,0.2892097418882419
2/10/20,0.16819571865443425,49990.6389461661,9.03078079151322,0.3302730423822217
2/11/20,0.1834862385321101,49989.46647772359,10.156930342070767,0.3765919343409029
2/12/20,0.1834862385321101,49988.145669044556,11.425558224754685,0.4287727306811233
2/13/20,0.19877675840978593,49986.66014392524,12.85239324941266,0.4874628253437725
2/14/20,0.19877675840978593,49984.99248738367,14.454161923030584,0.5533506932937899
2/15/20,0.19877675840978593,49983.12424565975,16.248588449732576,0.6271658905201664
2/16/20,0.19877675840978593,49981.02889523054,18.261139779188554,0.70996499027104
2/17/20,0.19877675840978593,49978.651306317464,20.54472852896415,0.8039651535683541
2/18/20,0.19877675840978593,49975.97895646455,23.11139937053732,0.909644164911142
2/19/20,0.19877675840978593,49972.9967703992,25.97565285443824,1.027576746355153
2/20/20,0.19877675840978593,49969.678659363395,29.162561937280955,1.1587786993212803
2/21/20,0.2293577981651376,49965.98752111364,32.70777198176339,1.3047069045955584
2/22/20,0.2293577981651376,49961.875239921,36.65750075666734,1.4672593223291648
2/23/20,0.2293577981651376,49957.2826865711,41.068538436858454,1.6487749920384194
2/24/20,0.2293577981651376,49952.13971836411,46.0082476032863,1.8520340326047855
2/25/20,0.2293577981651376,49946.365179114735,51.55456324298426,2.0802576422748684
2/26/20,0.2293577981651376,49939.86689915227,57.795992749069626,2.337108098660416
2/27/20,0.24464831804281345,49932.54169532051,64.83161592074356,2.626688758738319
2/28/20,0.24464831804281345,49924.275370977855,72.7710849632911,2.95354405885061
2/29/20,0.3669724770642202,49914.94271599721,81.73462448808112,3.322659514704465
3/1/20,0.4587155963302752,49904.40750676606,91.85303151256642,3.739461721372202
3/2/20,0.8103975535168195,49892.522506186426,103.26767546028361,4.209818353291284
3/3/20,1.1162079510703364,49879.12946367488,116.13049816085325,4.740038164264311
3/4/20,1.5902140672782874,49864.05911516256,130.6040138499797,5.336870987459033
3/5/20,2.6299694189602447,49847.13118309514,146.86130916945123,6.007507735408336
3/6/20,3.318042813455657,49828.154376432845,165.08604316714002,6.759580400010251
3/7/20,5.137614678899083,49806.92639065047,185.472447297002,7.601162052527955
3/8/20,6.8807339449541285,49783.15387362768,208.30131583295304,8.544810539364274
3/9/20,7.859327217125382,49756.46216012896,233.93144509123292,9.606394779802377
3/10/20,10.825688073394495,49726.5796666417,262.62464902953786,10.79568432875744
3/11/20,16.896024464831804,49693.16121368366,294.7130775011809,12.125708815156308
3/12/20,23.807339449541285,49655.78361959187,330.60339369236084,13.612986715763734
3/13/20,32.82874617737003,49613.94570052266,370.7767741221621,15.277525355182393
3/14/20,43.68501529051988,49567.068270451586,415.7889086425552,17.14282090585288
3/15/20,44.6177370030581,49514.49414117355,466.2700004383959,19.2358583880537
3/16/20,65.85626911314985,49455.48812230267,522.9247660274261,21.587111669901272
3/17/20,93.21100917431193,49389.237021272376,586.5324352602737,24.23054346734994
3/18/20,135.67278287461772,49314.84964333535,657.9467513204519,27.20360534419196
3/19/20,215.5045871559633,49231.35679156358,738.0959707243596,30.5472377120575
3/20/20,296.6819571865443,49137.7112668483,827.9828633212821,34.30586983041465
3/21/20,393.348623853211,49032.787867900035,928.68471229339,38.52741980656942
3/22/20,514.2813455657492,48915.38339124859,1041.3533141557398,43.26329459566571
3/23/20,667.6299694189603,48784.216631243034,1167.2149787562737,48.5683900006854
3/24/20,821.651376146789,48637.92838005173,1307.5705292758196,54.5010906724482
3/25/20,1005.7798165137615,48475.23815719861,1463.632752792264,61.12909000912159
3/26/20,1281.8960244648317,48294.55190123027,1636.9083208388115,68.53977793091411
3/27/20,1554.388379204893,48093.39034634198,1829.7869013768861,76.82275228112964
3/28/20,1857.4617737003057,47869.19051439562,2044.730256305486,86.07922929888963
3/29/20,2154.571865443425,47619.33475639337,2284.2426197884333,96.4226238181884
3/30/20,2474.571865443425,47341.150752477726,2550.8706982543795,107.97854926789326
3/31/20,2877.2477064220184,47031.91151193145,2847.2036703968,120.88481767174422
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
1 Actual S I R
2 1/22/20 0.01529051987767584 49999.0 1.0 0.0
3 1/23/20 0.01529051987767584 49998.87142424206 1.1234972154720178 0.005078542461735381
4 1/24/20 0.03058103975535168 49998.726971267446 1.2622445067142218 0.010784225844925973
5 1/25/20 0.03058103975535168 49998.564682877775 1.418122720321673 0.017194401905434777
6 1/26/20 0.0764525993883792 49998.38234997699 1.5932536885634114 0.024396334439772325
7 1/27/20 0.0764525993883792 49998.1761616234 1.7912976312291053 0.032540745365268506
8 1/28/20 0.0764525993883792 49997.94267086507 2.0155651686230516 0.04176396630732312
9 1/29/20 0.0764525993883792 49997.68103501622 2.2668658997885043 0.052099083983799914
10 1/30/20 0.0764525993883792 49997.389420767366 2.5469608746381214 0.06361835799887736
11 1/31/20 0.10703363914373089 49997.064956030794 2.858608840222778 0.07643512898078467
12 2/1/20 0.12232415902140673 49996.70372994068 3.2055662407315637 0.09070381858180188
13 2/2/20 0.12232415902140673 49996.30079285303 3.592587217491785 0.10661992947825968
14 2/3/20 0.16819571865443425 49995.85015634566 4.025423608968964 0.12442004537053958
15 2/4/20 0.16819571865443425 49995.344793218246 4.510824950766836 0.14438183098307383
16 2/5/20 0.16819571865443425 49994.77663749231 5.05653847562736 0.16682403206434543
17 2/6/20 0.16819571865443425 49994.13658441118 5.671309113430702 0.1921064753868881
18 2/7/20 0.16819571865443425 49993.41449044005 6.36487949119525 0.2206300687472865
19 2/8/20 0.16819571865443425 49992.59917326595 7.1479899330776036 0.2528368009661757
20 2/9/20 0.16819571865443425 49991.67841179774 8.032378460372584 0.2892097418882419
21 2/10/20 0.16819571865443425 49990.6389461661 9.03078079151322 0.3302730423822217
22 2/11/20 0.1834862385321101 49989.46647772359 10.156930342070767 0.3765919343409029
23 2/12/20 0.1834862385321101 49988.145669044556 11.425558224754685 0.4287727306811233
24 2/13/20 0.19877675840978593 49986.66014392524 12.85239324941266 0.4874628253437725
25 2/14/20 0.19877675840978593 49984.99248738367 14.454161923030584 0.5533506932937899
26 2/15/20 0.19877675840978593 49983.12424565975 16.248588449732576 0.6271658905201664
27 2/16/20 0.19877675840978593 49981.02889523054 18.261139779188554 0.70996499027104
28 2/17/20 0.19877675840978593 49978.651306317464 20.54472852896415 0.8039651535683541
29 2/18/20 0.19877675840978593 49975.97895646455 23.11139937053732 0.909644164911142
30 2/19/20 0.19877675840978593 49972.9967703992 25.97565285443824 1.027576746355153
31 2/20/20 0.19877675840978593 49969.678659363395 29.162561937280955 1.1587786993212803
32 2/21/20 0.2293577981651376 49965.98752111364 32.70777198176339 1.3047069045955584
33 2/22/20 0.2293577981651376 49961.875239921 36.65750075666734 1.4672593223291648
34 2/23/20 0.2293577981651376 49957.2826865711 41.068538436858454 1.6487749920384194
35 2/24/20 0.2293577981651376 49952.13971836411 46.0082476032863 1.8520340326047855
36 2/25/20 0.2293577981651376 49946.365179114735 51.55456324298426 2.0802576422748684
37 2/26/20 0.2293577981651376 49939.86689915227 57.795992749069626 2.337108098660416
38 2/27/20 0.24464831804281345 49932.54169532051 64.83161592074356 2.626688758738319
39 2/28/20 0.24464831804281345 49924.275370977855 72.7710849632911 2.95354405885061
40 2/29/20 0.3669724770642202 49914.94271599721 81.73462448808112 3.322659514704465
41 3/1/20 0.4587155963302752 49904.40750676606 91.85303151256642 3.739461721372202
42 3/2/20 0.8103975535168195 49892.522506186426 103.26767546028361 4.209818353291284
43 3/3/20 1.1162079510703364 49879.12946367488 116.13049816085325 4.740038164264311
44 3/4/20 1.5902140672782874 49864.05911516256 130.6040138499797 5.336870987459033
45 3/5/20 2.6299694189602447 49847.13118309514 146.86130916945123 6.007507735408336
46 3/6/20 3.318042813455657 49828.154376432845 165.08604316714002 6.759580400010251
47 3/7/20 5.137614678899083 49806.92639065047 185.472447297002 7.601162052527955
48 3/8/20 6.8807339449541285 49783.15387362768 208.30131583295304 8.544810539364274
49 3/9/20 7.859327217125382 49756.46216012896 233.93144509123292 9.606394779802377
50 3/10/20 10.825688073394495 49726.5796666417 262.62464902953786 10.79568432875744
51 3/11/20 16.896024464831804 49693.16121368366 294.7130775011809 12.125708815156308
52 3/12/20 23.807339449541285 49655.78361959187 330.60339369236084 13.612986715763734
53 3/13/20 32.82874617737003 49613.94570052266 370.7767741221621 15.277525355182393
54 3/14/20 43.68501529051988 49567.068270451586 415.7889086425552 17.14282090585288
55 3/15/20 44.6177370030581 49514.49414117355 466.2700004383959 19.2358583880537
56 3/16/20 65.85626911314985 49455.48812230267 522.9247660274261 21.587111669901272
57 3/17/20 93.21100917431193 49389.237021272376 586.5324352602737 24.23054346734994
58 3/18/20 135.67278287461772 49314.84964333535 657.9467513204519 27.20360534419196
59 3/19/20 215.5045871559633 49231.35679156358 738.0959707243596 30.5472377120575
60 3/20/20 296.6819571865443 49137.7112668483 827.9828633212821 34.30586983041465
61 3/21/20 393.348623853211 49032.787867900035 928.68471229339 38.52741980656942
62 3/22/20 514.2813455657492 48915.38339124859 1041.3533141557398 43.26329459566571
63 3/23/20 667.6299694189603 48784.216631243034 1167.2149787562737 48.5683900006854
64 3/24/20 821.651376146789 48637.92838005173 1307.5705292758196 54.5010906724482
65 3/25/20 1005.7798165137615 48475.23815719861 1463.632752792264 61.12909000912159
66 3/26/20 1281.8960244648317 48294.55190123027 1636.9083208388115 68.53977793091411
67 3/27/20 1554.388379204893 48093.39034634198 1829.7869013768861 76.82275228112964
68 3/28/20 1857.4617737003057 47869.19051439562 2044.730256305486 86.07922929888963
69 3/29/20 2154.571865443425 47619.33475639337 2284.2426197884333 96.4226238181884
70 3/30/20 2474.571865443425 47341.150752477726 2550.8706982543795 107.97854926789326
71 3/31/20 2877.2477064220184 47031.91151193145 2847.2036703968 120.88481767174422
72 4/1/20 3262.4159021406726 46688.835373177644 3175.8731871739956 135.29143964835436
73 4/2/20 3725.0152905198775 46309.086003779696 3539.553371809094 151.36062441120953
74 4/3/20 4213.792048929664 45889.772400441274 3940.960819790049 169.26677976866858
75 4/4/20 4722.400611620795 45427.94888900639 4382.85459886964 189.1965121239631
76 4/5/20 5153.899082568807 44920.61512445932 4868.036249065472 211.3486264751976
77 4/6/20 5606.422018348624 44364.71609092467 5399.349782659975 235.93412641534957
78 4/7/20 6058.4250764526 43757.14210166731 5979.681684200406 263.1762141322693

3
out/SARS-SIR-data.csv Normal file
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@ -0,0 +1,3 @@
Beta: 0.031662971068960946
Gamma: 0.018519768809938723
R0: 1.7096850070811285
1 Beta: 0.031662971068960946
2 Gamma: 0.018519768809938723
3 R0: 1.7096850070811285

77
out/SARS-SIR-prediction.csv Normal file
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@ -0,0 +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
4/23/03,729.0,9325.38611794234,566.1060158201255,108.5078662375327
4/24/03,744.0,9308.593695690377,572.3564692682052,119.04983504141725
4/25/03,755.0,9291.647076432952,578.6450722781254,129.70785128892103
4/26/03,763.5,9274.546254563056,584.9716358776624,140.48210955928025
4/28/03,778.5,9257.291082520527,591.3356116538148,151.37330582565713
4/29/03,786.0,9239.88140918478,597.736342445242,162.38224836997682
4/30/03,794.5,9222.31712462584,604.173152454918,173.50972291924018
5/1/03,800.0,9204.598160104344,610.6453472501305,184.75649264552393
5/2/03,805.5,9186.724488071535,617.1522137624829,196.1232981659804
5/3/03,810.5,9168.696122169269,623.6930202878924,207.61085754283772
5/5/03,818.5,9150.513117230008,630.2670164865909,219.21986628339982
5/6/03,823.0,9132.175569276827,636.8734333831252,230.95099734004629
5/7/03,827.0,9113.68361552341,643.5114833663562,242.80490111023255
5/8/03,830.5,9095.03743437405,650.1803601894597,254.78220543648973
5/9/03,833.5,9076.237245423648,656.8792389699258,266.8835156064247
5/10/03,837.0,9057.283309457718,663.6072761895597,279.10941435272
5/12/03,841.5,9038.175928452385,670.3636096944803,291.4604618531342
5/13/03,844.5,9018.915445574376,677.1473586951217,303.9371957305013
5/14/03,849.0,8999.502245181035,683.9576237662324,316.5401310527311
5/15/03,851.5,8979.936752820313,690.7934868468756,329.26976033280937
5/16/03,853.0,8960.219435230772,697.6540112404288,342.1265535287975
5/17/03,855.0,8940.350800341583,704.5382416145842,355.11095804383234
5/19/03,857.0,8920.331397272523,711.4452040013485,368.223398726127
5/20/03,859.0,8900.161816333986,718.3739057970432,381.46427786896993
5/21/03,859.5,8879.84268902697,725.3233357623042,394.8339752107257
5/22/03,861.0,8859.374688043083,732.2924640220818,408.3328479348343
5/23/03,862.0,8838.758527264547,739.2802420656409,421.96123066981147
5/24/03,862.0,8817.994961764189,746.2856027465614,435.719435489249
5/26/03,863.0,8797.084787805446,753.3074602827372,449.60775191181426
5/27/03,864.0,8776.028842842372,760.3447102563772,463.62644690125006
5/28/03,865.0,8754.828005519617,767.3962296140044,477.77576486637554
5/29/03,866.0,8733.483195672457,774.4608766664569,492.05592766108515
5/30/03,868.0,8711.995374326762,781.5374910888871,506.46713458434937
5/31/03,869.5,8690.365543699023,788.6248939207617,521.0095623802142
6/2/03,873.0,8668.594747196334,795.7218875658625,535.6833652378015
6/3/03,873.5,8646.684069416404,802.8272557922855,550.4886747913089
6/4/03,874.0,8624.634636147548,809.9397637324413,565.4256001200094
6/5/03,874.0,8602.447614368692,817.0581578830551,580.4942277482527
6/6/03,875.0,8580.12421224937,824.1811661051668,595.6946216454631
6/9/03,876.5,8557.665679149726,831.3074976241306,611.0268232261413
6/10/03,877.0,8535.073305620519,838.4358430296157,626.490851349864
6/11/03,877.0,8512.34842340311,845.5648742756055,642.0867023212829
6/12/03,877.5,8489.492405429475,852.6932446803977,657.8143498901256
6/13/03,877.5,8466.506665822197,859.8195889266052,673.6737452511964
6/16/03,877.5,8443.392659894469,866.9425230611552,689.664817044374
6/17/03,877.5,8420.151884150095,874.0606444952892,705.7874713546134
6/18/03,877.5,8396.78587628349,881.1725320045638,722.0415917119461
6/19/03,877.5,8373.296215179671,888.2767457288496,738.4270390914784
6/20/03,877.5,8349.684520914274,895.3718271723321,754.943651913392
6/23/03,877.5,8325.952454753542,902.4562992035115,771.5912460429457
6/24/03,877.5,8302.101719154323,909.528666055202,788.3696147904728
6/25/03,877.5,8278.134057764082,916.587413324533,805.2785289113833
6/26/03,877.5,8254.051255420887,923.6310079729482,822.3177366061623
6/27/03,877.5,8229.855138153422,930.6578983262057,839.4869635203708
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
1 Actual S I R
2 4/10/03 499.0 9500.0 500.0 0.0
3 4/11/03 529.5 9484.884979595565 505.80148109959003 9.313539304844733
4 4/12/03 554.0 9469.619417390539 511.64566678773895 18.73491582172085
5 4/14/03 595.0 9454.202845099702 517.5322376669819 28.26491723331429
6 4/15/03 616.0 9438.634815392412 523.4608571171718 37.90432749041551
7 4/16/03 634.0 9422.91490886804 529.4311682856298 47.65392284632816
8 4/17/03 648.5 9407.04273428322 535.4427939878531 57.51447172892478
9 4/18/03 679.0 9391.017928551839 541.4953367075137 67.48673474064691
10 4/19/03 679.0 9374.840156745033 547.5883785964601 77.57146465850494
11 4/21/03 701.0 9358.509112091206 553.7214814747153 87.76940643407822
12 4/22/03 717.0 9342.024515976005 559.894186830479 98.08129719351507
13 4/23/03 729.0 9325.38611794234 566.1060158201255 108.5078662375327
14 4/24/03 744.0 9308.593695690377 572.3564692682052 119.04983504141725
15 4/25/03 755.0 9291.647076432952 578.6450722781254 129.70785128892103
16 4/26/03 763.5 9274.546254563056 584.9716358776624 140.48210955928025
17 4/28/03 778.5 9257.291082520527 591.3356116538148 151.37330582565713
18 4/29/03 786.0 9239.88140918478 597.736342445242 162.38224836997682
19 4/30/03 794.5 9222.31712462584 604.173152454918 173.50972291924018
20 5/1/03 800.0 9204.598160104344 610.6453472501305 184.75649264552393
21 5/2/03 805.5 9186.724488071535 617.1522137624829 196.1232981659804
22 5/3/03 810.5 9168.696122169269 623.6930202878924 207.61085754283772
23 5/5/03 818.5 9150.513117230008 630.2670164865909 219.21986628339982
24 5/6/03 823.0 9132.175569276827 636.8734333831252 230.95099734004629
25 5/7/03 827.0 9113.68361552341 643.5114833663562 242.80490111023255
26 5/8/03 830.5 9095.03743437405 650.1803601894597 254.78220543648973
27 5/9/03 833.5 9076.237245423648 656.8792389699258 266.8835156064247
28 5/10/03 837.0 9057.283309457718 663.6072761895597 279.10941435272
29 5/12/03 841.5 9038.175928452385 670.3636096944803 291.4604618531342
30 5/13/03 844.5 9018.915445574376 677.1473586951217 303.9371957305013
31 5/14/03 849.0 8999.502245181035 683.9576237662324 316.5401310527311
32 5/15/03 851.5 8979.936752820313 690.7934868468756 329.26976033280937
33 5/16/03 853.0 8960.219435230772 697.6540112404288 342.1265535287975
34 5/17/03 855.0 8940.350800341583 704.5382416145842 355.11095804383234
35 5/19/03 857.0 8920.331397272523 711.4452040013485 368.223398726127
36 5/20/03 859.0 8900.161816333986 718.3739057970432 381.46427786896993
37 5/21/03 859.5 8879.84268902697 725.3233357623042 394.8339752107257
38 5/22/03 861.0 8859.374688043083 732.2924640220818 408.3328479348343
39 5/23/03 862.0 8838.758527264547 739.2802420656409 421.96123066981147
40 5/24/03 862.0 8817.994961764189 746.2856027465614 435.719435489249
41 5/26/03 863.0 8797.084787805446 753.3074602827372 449.60775191181426
42 5/27/03 864.0 8776.028842842372 760.3447102563772 463.62644690125006
43 5/28/03 865.0 8754.828005519617 767.3962296140044 477.77576486637554
44 5/29/03 866.0 8733.483195672457 774.4608766664569 492.05592766108515
45 5/30/03 868.0 8711.995374326762 781.5374910888871 506.46713458434937
46 5/31/03 869.5 8690.365543699023 788.6248939207617 521.0095623802142
47 6/2/03 873.0 8668.594747196334 795.7218875658625 535.6833652378015
48 6/3/03 873.5 8646.684069416404 802.8272557922855 550.4886747913089
49 6/4/03 874.0 8624.634636147548 809.9397637324413 565.4256001200094
50 6/5/03 874.0 8602.447614368692 817.0581578830551 580.4942277482527
51 6/6/03 875.0 8580.12421224937 824.1811661051668 595.6946216454631
52 6/9/03 876.5 8557.665679149726 831.3074976241306 611.0268232261413
53 6/10/03 877.0 8535.073305620519 838.4358430296157 626.490851349864
54 6/11/03 877.0 8512.34842340311 845.5648742756055 642.0867023212829
55 6/12/03 877.5 8489.492405429475 852.6932446803977 657.8143498901256
56 6/13/03 877.5 8466.506665822197 859.8195889266052 673.6737452511964
57 6/16/03 877.5 8443.392659894469 866.9425230611552 689.664817044374
58 6/17/03 877.5 8420.151884150095 874.0606444952892 705.7874713546134
59 6/18/03 877.5 8396.78587628349 881.1725320045638 722.0415917119461
60 6/19/03 877.5 8373.296215179671 888.2767457288496 738.4270390914784
61 6/20/03 877.5 8349.684520914274 895.3718271723321 754.943651913392
62 6/23/03 877.5 8325.952454753542 902.4562992035115 771.5912460429457
63 6/24/03 877.5 8302.101719154323 909.528666055202 788.3696147904728
64 6/25/03 877.5 8278.134057764082 916.587413324533 805.2785289113833
65 6/26/03 877.5 8254.051255420887 923.6310079729482 822.3177366061623
66 6/27/03 877.5 8229.855138153422 930.6578983262057 839.4869635203708
67 6/30/03 877.5 8205.547573180975 937.6665140743783 856.7859127446457
68 7/1/03 877.5 8181.130468913446 944.6552662718534 874.214264814699
69 7/2/03 877.5 8156.605774951346 951.622547337333 891.7716777113201
70 7/3/03 877.5 8131.9754820857925 958.5667310538335 909.4577868603722
71 7/4/03 877.5 8107.241622298517 965.4861725686861 927.2722051327953
72 7/7/03 877.5 8082.406268761857 972.3792083935365 945.2145228446052
73 7/8/03 877.5 8057.47153583876 979.2441564043446 963.2843077568929
74 7/9/03 877.5 8032.439579082788 986.0793158413854 981.4811050758253
75 7/10/03 877.5 8007.312595238105 992.8829673092482 999.8044374526455
76 7/11/03 877.5 7982.09282223949 999.6533727768367 1018.2538049836719
77 8/7/03 877.5 7956.78253921233 1006.3887755773695 1036.8286852102988

3
out/SARS-data.csv
View file

@ -1,3 +0,0 @@
Beta: 1e-08
Gamma: 0.0061248427719888315
R0: 1.6326949723074842e-06
1 Beta: 1e-08
2 Gamma: 0.0061248427719888315
3 R0: 1.6326949723074842e-06

77
out/SARS-prediction.csv
View file

@ -1,77 +0,0 @@
,Actual,S,I,R
4/10/03,0.1566473081148956,9999.0,1.0,0.0
4/11/03,0.16622194318003453,9998.999999990032,0.9938938857782499,0.00610612419019138
4/12/03,0.17391304347826086,9998.999999980124,0.9878250561849686,0.01217496369104556
4/14/03,0.18678386438549677,9998.999999970278,0.9817932835629529,0.018206746160137382
4/15/03,0.1933762360696908,9998.99999996049,0.9757983416361868,0.02420169787385227
4/16/03,0.1990268403704285,9998.999999950764,0.9698400055077305,0.03016004372949699
4/17/03,0.20357871605713387,9998.999999941096,0.9639180446502709,0.036082014254761215
4/18/03,0.2131533511222728,9998.999999931486,0.958032229361117,0.0419678391527109
4/19/03,0.2131533511222728,9998.999999921936,0.9521823415639332,0.04781773650003748
4/21/03,0.2200596452676189,9998.999999912445,0.9463681643524197,0.05363192320339512
4/22/03,0.22508240464605242,9998.99999990301,0.9405894819398969,0.05941061504981562
4/23/03,0.22884947417987755,9998.999999893635,0.9348460796593057,0.0651540267067084
4/24/03,0.233558311097159,9998.999999884314,0.9291377439632077,0.07086237172186058
4/25/03,0.23701145816983205,9998.999999875054,0.9234642624237851,0.07653586252343685
4/26/03,0.23967979908962483,9998.999999865848,0.9178254237328403,0.08217471041997958
4/28/03,0.24438863600690627,9998.999999856698,0.9122210177017966,0.08777912560040879
4/29/03,0.246743054465547,9998.999999847605,0.9066508352616974,0.09334931713402214
4/30/03,0.2494113953853398,9998.999999838567,0.9011146684632072,0.09888549297049494
5/1/03,0.25113796892167634,9998.999999829583,0.8956123104766106,0.10438785993988008
5/2/03,0.2528645424580129,9998.999999820655,0.8901435555918127,0.10985662375260818
5/3/03,0.25443415476377335,9998.999999811782,0.8847081992183395,0.11529198899948748
5/5/03,0.2569455344529901,9998.999999802963,0.8793060378853373,0.1206941591517038
5/6/03,0.2583581855281745,9998.999999794198,0.8739368692415727,0.1260633365608207
5/7/03,0.2596138753727829,9998.999999785487,0.8686004920554333,0.1313997224587793
5/8/03,0.26071260398681523,9998.999999776828,0.8632967062149268,0.1367035169578984
5/9/03,0.26165437137027153,9998.99999976822,0.8580253127276819,0.14197491905087448
5/10/03,0.26275309998430385,9998.999999759668,0.8527861137209471,0.14721412661078156
5/12/03,0.2641657510594883,9998.999999751168,0.8475789124415923,0.1524213363910714
5/13/03,0.26510751844294456,9998.999999742719,0.8424035132561074,0.1575967440255734
5/14/03,0.266520169518129,9998.999999734322,0.8372597216506029,0.16274054402849447
5/15/03,0.26730497567100925,9998.999999725975,0.8321473442308099,0.1678529297944193
5/16/03,0.2677758593627374,9998.99999971768,0.8270661887220798,0.17293409359831027
5/17/03,0.26840370428504157,9998.999999709435,0.8220160639693852,0.17798422659550728
5/19/03,0.2690315492073458,9998.999999701242,0.8169967799373182,0.18300351882172788
5/20/03,0.26965939412964995,9998.999999693096,0.8120081477100923,0.18799215919306728
5/21/03,0.269816355360226,9998.999999685002,0.8070499794915413,0.1929503355059984
5/22/03,0.2702872390519542,9998.999999676957,0.8021220886051191,0.1978782344373717
5/23/03,0.27060116151310626,9998.999999668962,0.7972242894939008,0.20277604154441542
5/24/03,0.27060116151310626,9998.999999661015,0.7923563977205816,0.20764394126473523
5/26/03,0.27091508397425834,9998.999999653117,0.7875182299674773,0.21248211691631466
5/27/03,0.2712290064354104,9998.999999645266,0.7827096040365243,0.21729075069751472
5/28/03,0.2715429288965625,9998.999999637463,0.7779303388492795,0.22207002368707426
5/29/03,0.27185685135771465,9998.999999629708,0.7731802544469202,0.22682011584410958
5/30/03,0.2724846962800188,9998.999999622001,0.7684591719902444,0.23154120600811465
5/31/03,0.27295557997174696,9998.999999614342,0.7637669137596708,0.23623347189896113
6/2/03,0.2740543085857793,9998.999999606727,0.7591033031552382,0.24089709011689836
6/3/03,0.27421126981635535,9998.99999959916,0.754468164696606,0.24553223614255326
6/4/03,0.27436823104693137,9998.99999959164,0.7498613240230546,0.2501390843369304
6/5/03,0.27436823104693137,9998.999999584164,0.7452826078934843,0.2547178079414121
6/6/03,0.2746821535080835,9998.999999576736,0.7407318441864165,0.25926857907775797
6/9/03,0.27515303719981166,9998.999999569352,0.7362088618999928,0.2637915687481058
6/10/03,0.27530999843038767,9998.999999562013,0.7317134911519751,0.26828694683497056
6/11/03,0.27530999843038767,9998.999999554719,0.7272455646461425,0.27275488063484693
6/12/03,0.27546695966096374,9998.99999954747,0.7228049183018899,0.27719553422860754
6/13/03,0.27546695966096374,9998.999999540265,0.7183913861666602,0.28160907356908
6/16/03,0.27546695966096374,9998.999999533102,0.7140048031056461,0.2859956637913417
6/17/03,0.27546695966096374,9998.999999525984,0.7096450049155073,0.2903554690990009
6/18/03,0.27546695966096374,9998.999999518912,0.7053118283243721,0.2946886527641958
6/19/03,0.27546695966096374,9998.999999511881,0.7010051109918364,0.2989953771275956
6/20/03,0.27546695966096374,9998.999999504893,0.6967246915089644,0.30327580359839945
6/23/03,0.27546695966096374,9998.999999497948,0.6924704093982882,0.3075300926543374
6/24/03,0.27546695966096374,9998.999999491043,0.6882421051138082,0.3117584038416696
6/25/03,0.27546695966096374,9998.999999484184,0.6840396200409922,0.3159608957751869
6/26/03,0.27546695966096374,9998.999999477364,0.6798627964967765,0.32013772613821057
6/27/03,0.27546695966096374,9998.999999470589,0.6757114777295653,0.32428905168259226
6/30/03,0.27546695966096374,9998.999999463853,0.671585507919231,0.32841502822871405
7/1/03,0.27546695966096374,9998.999999457157,0.6674847321771137,0.3325158106654888
7/2/03,0.27546695966096374,9998.999999450503,0.6634089965460216,0.3365915529503593
7/3/03,0.27546695966096374,9998.999999443891,0.6593581480002312,0.34064240810929924
7/4/03,0.27546695966096374,9998.999999437317,0.6553320344454865,0.34466852823681265
7/7/03,0.27546695966096374,9998.999999430785,0.651330504719,0.3486700644959339
7/8/03,0.27546695966096374,9998.999999424292,0.647353408589452,0.35264716711822797
7/9/03,0.27546695966096374,9998.99999941784,0.643400596756991,0.35659998540379023
7/10/03,0.27546695966096374,9998.999999411426,0.6394719208532331,0.36052866772124653
7/11/03,0.27546695966096374,9998.99999940505,0.635567233441263,0.3644333615077531
8/7/03,0.27546695966096374,9998.999999398715,0.6316863880156327,0.36831421326899677
1 Actual S I R
2 4/10/03 0.1566473081148956 9999.0 1.0 0.0
3 4/11/03 0.16622194318003453 9998.999999990032 0.9938938857782499 0.00610612419019138
4 4/12/03 0.17391304347826086 9998.999999980124 0.9878250561849686 0.01217496369104556
5 4/14/03 0.18678386438549677 9998.999999970278 0.9817932835629529 0.018206746160137382
6 4/15/03 0.1933762360696908 9998.99999996049 0.9757983416361868 0.02420169787385227
7 4/16/03 0.1990268403704285 9998.999999950764 0.9698400055077305 0.03016004372949699
8 4/17/03 0.20357871605713387 9998.999999941096 0.9639180446502709 0.036082014254761215
9 4/18/03 0.2131533511222728 9998.999999931486 0.958032229361117 0.0419678391527109
10 4/19/03 0.2131533511222728 9998.999999921936 0.9521823415639332 0.04781773650003748
11 4/21/03 0.2200596452676189 9998.999999912445 0.9463681643524197 0.05363192320339512
12 4/22/03 0.22508240464605242 9998.99999990301 0.9405894819398969 0.05941061504981562
13 4/23/03 0.22884947417987755 9998.999999893635 0.9348460796593057 0.0651540267067084
14 4/24/03 0.233558311097159 9998.999999884314 0.9291377439632077 0.07086237172186058
15 4/25/03 0.23701145816983205 9998.999999875054 0.9234642624237851 0.07653586252343685
16 4/26/03 0.23967979908962483 9998.999999865848 0.9178254237328403 0.08217471041997958
17 4/28/03 0.24438863600690627 9998.999999856698 0.9122210177017966 0.08777912560040879
18 4/29/03 0.246743054465547 9998.999999847605 0.9066508352616974 0.09334931713402214
19 4/30/03 0.2494113953853398 9998.999999838567 0.9011146684632072 0.09888549297049494
20 5/1/03 0.25113796892167634 9998.999999829583 0.8956123104766106 0.10438785993988008
21 5/2/03 0.2528645424580129 9998.999999820655 0.8901435555918127 0.10985662375260818
22 5/3/03 0.25443415476377335 9998.999999811782 0.8847081992183395 0.11529198899948748
23 5/5/03 0.2569455344529901 9998.999999802963 0.8793060378853373 0.1206941591517038
24 5/6/03 0.2583581855281745 9998.999999794198 0.8739368692415727 0.1260633365608207
25 5/7/03 0.2596138753727829 9998.999999785487 0.8686004920554333 0.1313997224587793
26 5/8/03 0.26071260398681523 9998.999999776828 0.8632967062149268 0.1367035169578984
27 5/9/03 0.26165437137027153 9998.99999976822 0.8580253127276819 0.14197491905087448
28 5/10/03 0.26275309998430385 9998.999999759668 0.8527861137209471 0.14721412661078156
29 5/12/03 0.2641657510594883 9998.999999751168 0.8475789124415923 0.1524213363910714
30 5/13/03 0.26510751844294456 9998.999999742719 0.8424035132561074 0.1575967440255734
31 5/14/03 0.266520169518129 9998.999999734322 0.8372597216506029 0.16274054402849447
32 5/15/03 0.26730497567100925 9998.999999725975 0.8321473442308099 0.1678529297944193
33 5/16/03 0.2677758593627374 9998.99999971768 0.8270661887220798 0.17293409359831027
34 5/17/03 0.26840370428504157 9998.999999709435 0.8220160639693852 0.17798422659550728
35 5/19/03 0.2690315492073458 9998.999999701242 0.8169967799373182 0.18300351882172788
36 5/20/03 0.26965939412964995 9998.999999693096 0.8120081477100923 0.18799215919306728
37 5/21/03 0.269816355360226 9998.999999685002 0.8070499794915413 0.1929503355059984
38 5/22/03 0.2702872390519542 9998.999999676957 0.8021220886051191 0.1978782344373717
39 5/23/03 0.27060116151310626 9998.999999668962 0.7972242894939008 0.20277604154441542
40 5/24/03 0.27060116151310626 9998.999999661015 0.7923563977205816 0.20764394126473523
41 5/26/03 0.27091508397425834 9998.999999653117 0.7875182299674773 0.21248211691631466
42 5/27/03 0.2712290064354104 9998.999999645266 0.7827096040365243 0.21729075069751472
43 5/28/03 0.2715429288965625 9998.999999637463 0.7779303388492795 0.22207002368707426
44 5/29/03 0.27185685135771465 9998.999999629708 0.7731802544469202 0.22682011584410958
45 5/30/03 0.2724846962800188 9998.999999622001 0.7684591719902444 0.23154120600811465
46 5/31/03 0.27295557997174696 9998.999999614342 0.7637669137596708 0.23623347189896113
47 6/2/03 0.2740543085857793 9998.999999606727 0.7591033031552382 0.24089709011689836
48 6/3/03 0.27421126981635535 9998.99999959916 0.754468164696606 0.24553223614255326
49 6/4/03 0.27436823104693137 9998.99999959164 0.7498613240230546 0.2501390843369304
50 6/5/03 0.27436823104693137 9998.999999584164 0.7452826078934843 0.2547178079414121
51 6/6/03 0.2746821535080835 9998.999999576736 0.7407318441864165 0.25926857907775797
52 6/9/03 0.27515303719981166 9998.999999569352 0.7362088618999928 0.2637915687481058
53 6/10/03 0.27530999843038767 9998.999999562013 0.7317134911519751 0.26828694683497056
54 6/11/03 0.27530999843038767 9998.999999554719 0.7272455646461425 0.27275488063484693
55 6/12/03 0.27546695966096374 9998.99999954747 0.7228049183018899 0.27719553422860754
56 6/13/03 0.27546695966096374 9998.999999540265 0.7183913861666602 0.28160907356908
57 6/16/03 0.27546695966096374 9998.999999533102 0.7140048031056461 0.2859956637913417
58 6/17/03 0.27546695966096374 9998.999999525984 0.7096450049155073 0.2903554690990009
59 6/18/03 0.27546695966096374 9998.999999518912 0.7053118283243721 0.2946886527641958
60 6/19/03 0.27546695966096374 9998.999999511881 0.7010051109918364 0.2989953771275956
61 6/20/03 0.27546695966096374 9998.999999504893 0.6967246915089644 0.30327580359839945
62 6/23/03 0.27546695966096374 9998.999999497948 0.6924704093982882 0.3075300926543374
63 6/24/03 0.27546695966096374 9998.999999491043 0.6882421051138082 0.3117584038416696
64 6/25/03 0.27546695966096374 9998.999999484184 0.6840396200409922 0.3159608957751869
65 6/26/03 0.27546695966096374 9998.999999477364 0.6798627964967765 0.32013772613821057
66 6/27/03 0.27546695966096374 9998.999999470589 0.6757114777295653 0.32428905168259226
67 6/30/03 0.27546695966096374 9998.999999463853 0.671585507919231 0.32841502822871405
68 7/1/03 0.27546695966096374 9998.999999457157 0.6674847321771137 0.3325158106654888
69 7/2/03 0.27546695966096374 9998.999999450503 0.6634089965460216 0.3365915529503593
70 7/3/03 0.27546695966096374 9998.999999443891 0.6593581480002312 0.34064240810929924
71 7/4/03 0.27546695966096374 9998.999999437317 0.6553320344454865 0.34466852823681265
72 7/7/03 0.27546695966096374 9998.999999430785 0.651330504719 0.3486700644959339
73 7/8/03 0.27546695966096374 9998.999999424292 0.647353408589452 0.35264716711822797
74 7/9/03 0.27546695966096374 9998.99999941784 0.643400596756991 0.35659998540379023
75 7/10/03 0.27546695966096374 9998.999999411426 0.6394719208532331 0.36052866772124653
76 7/11/03 0.27546695966096374 9998.99999940505 0.635567233441263 0.3644333615077531
77 8/7/03 0.27546695966096374 9998.999999398715 0.6316863880156327 0.36831421326899677

3
out/TEST-data.csv
View file

@ -1,3 +0,0 @@
Beta: 0.4000000000000001
Gamma: 1.0000000000049032e-08
R0: 39999999.99980388
1 Beta: 0.4000000000000001
2 Gamma: 1.0000000000049032e-08
3 R0: 39999999.99980388

4
out/TEST-prediction.csv
View file

@ -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
1 Actual S I R
2 1/22/20 1 13499.000074068588 0.9999259314124879 0.0
3 1/23/20 2 13498.508297406446 1.4917025812581148 1.2295557754239627e-08
4 1/24/20 3 13497.774897248144 2.225102721222464 3.06330734100344e-08

4
sir_model.cpp
View file

@ -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];

BIN
sir_model.exe Normal file
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61
solver2.py
View file

@ -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)