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Statistics on simulation

  • Author: Fang Zhang
  • Date: 2016.9.20
  • E-mail: fza34@sfu.ca

Statistics

In my simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.001,0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4]
  • resistant rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25,0.3]
  • removal rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.02]

Thus, there are 3300 simulations in total. For each simulation, 10% unremoved patients are sampled for 20 times.

  • resistant_mean is the avarage number of resistant patients of 20 times sampling.
  • resistant_ratio is resistant_mean/number of sampling patients.
  • resistant_std is standard deviation of the number of resistant patients of 20 times sampling.
  • resistant_trans_mean is the avarage number of resistant patients caused by transmission of 20 times sampling.
  • resistant_trans_ratio is resistant_trans_mean/number of sampling patients
  • resistant_trans_std is standard deviation of the number of resistant patients caused by transmission of 20 times sampling.
  • resistant_acq_mean is the avarage number of resistant patients caused by acqusition of 20 times sampling.
  • resistant_acq_ratio is resistant_acq_mean/number of sampling patients
  • resistant_acq_std is standard deviation of the number of resistant patients caused by acquisition of 20 times sampling.
  • resistant_mevent_mean is the avarage number of resistant patients that have more than 1 resistant event of 20 times sampling.
  • resistant_mevent_ratio is resistant_mevent_mean/number of sampling resistant patients
  • resistant_mevent_std is standard deviation of the number of resistant patients that have more than 1 resistant event of 20 times sampling.
  • unresistant_oncere_mean is the avarage number of unresistant patients that are once resistant of 20 times sampling.
  • unresistant_oncere_ratio is unresistant_oncere_mean/number of sampling patients.
  • unresistant_oncere_std is the standard deviation of number of unresistant patients that are once resistant of 20 times sampling.

  1. Resistant patients.

    In this picture, resistant_var is resistant_std which is a mistake.

    • With reinfection rate changes from 0.001 to 0.4, resistant patients ratio is steady around 42 when resistant rate is above 0.01.
    • The mean number of resistant patients declines with the rise of removal rate.

  2. Resistant patients from transmission.

    • When time span is above 10, for different reinfection rate, resistant patients ratio from transmission achieve highest value when resistant rate is 0.05. When time span rises, for different reinfection rate, resistant rate is becoming smaller that makes resistant patients ratio from transmission highest.
    • Resistant patients ratio from transmission rises with reinfection rate.
    • Resistant patients ratio from transmission rises with time span.
    • Number of patients from transmission declines with removal rate.

  3. Resistant patients from acquisition.

    • Looks like Figure 1.

  4. Resistant patients caused by multiple events.

    • Resistant patients caused by multiple events ratio increases with time span rises.
    • Resistant patients caused by multiple events ratio slightly increases with reinfection rate rising.
    • Resistant patients caused by multiple events ratio increases with resistant rate rises, but will be steady when resistant rate is avove 0.1.
    • Number of resistant patients caused by multiple events declines with the rise of removal rate.

  5. Unresistant patients once resistant.

    • Rato of unresistant patients once resistant increases with the rise of reinfection rate.
    • Rato of unresistant patients once resistant increases with the rise of time span.
    • When resistant rate is smaller than some value, rato and number of unresistant patients once resistant increase with the rise of resistant rate. When resistant rate is larger than this value, rato and number of unresistant patients once resistant increase with the rise of resistant rate.
    • number of unresistant patients once resistant decrease with the rise of removal rate.

  6. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.3300 -0.0012 0.0006 -0.8775
resistant_mean 0.0521 0.0002 0.8216 -0.2244
resistant_ratio 0.1360 0.0007 0.8540 0.0014
resistant_std 0.0511 0.0009 0.8168 -0.2276
resistant_trans_mean -0.0470 0.3582 -0.1696 -0.1123
resistant_trans_ratio -0.0024 0.3618 -0.1884 0.0041
resistant_trans_std -0.0648 0.3649 -0.1821 -0.1134
resistant_acq_mean 0.0636 -0.0845 0.8666 -0.1992
resistant_acq_ratio 0.1360 -0.0861 0.8953 0.0004
resistant_acq_std 0.0619 -0.0851 0.8626 -0.2011
resistant_mevent_mean 0.1355 0.0526 0.8412 -0.2030
resistant_mevent_ratio 0.2069 0.0543 0.8650 0.0013
resistant_mevent_std 0.1352 0.0553 0.8377 -0.2037
unresistant_mean -0.3089 -0.0012 -0.6430 -0.5370
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117
  • sampling_mean is strongly nagetive correlated with removal rate.
  • resistant_mean, resistant_ratio, resistant_std, are strongly possitive correlated with resistant_rate.
  • resistant_trans_mean, resistant_trans_ratio, resistant_trans_std are weakly positive correlated with reinfact_rate.
  • resistant_acq_mean, resistant_acq_ratio, resistant_acq_std, resistant_mevent_mean, resistant_mevent_ratio and resistant_mevent_std are strongly possitive correlated with resistant_rate.
  • unresistant_mean is weakly negative correlated with resistant_rate and removal_rate.
  • unresistant_oncere_mean, unresistant_oncere_ratio and unresistant_oncere_std are weakly positive correlated with reinfact_rate and weakly negative correlated with resistant_rate

In my second simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.001,0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4]
  • resistant rate is [0.0001, 0.0005, 0.001, 0.003, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05,0.1]
  • removal rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, if a resistant patient is being infected by a non-resistant patient you can indeed keep the resistant strain, i.e. the transmission would be unsuccessful. And resistant rate declined.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

We can see that the resistant patients ratio is still very high. Resistant rate should decline.

In my third simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.001,0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4]
  • resistant rate is [0.0001, 0.0005, 0.001, 0.003, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05,0.1]
  • removal rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, resistant rate should declines.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

  5. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.3306 -0.0001 0.0013 -0.8776
resistant_mean 0.4357 0.5969 0.3271 -0.2605
resistant_std 0.4330 0.5931 0.3310 -0.2653
resistant_ratio 0.5204 0.6186 0.3329 0.0010
resistant_trans_mean 0.3705 0.6747 0.1269 -0.2254
resistant_trans_std 0.3692 0.6762 0.1320 -0.2262
resistant_trans_ratio 0.4404 0.6964 0.1210 0.0008
resistant_acq_mean 0.3971 -0.0001 0.7906 -0.2251
resistant_acq_std 0.3833 -0.0030 0.79576 -0.2231
resistant_acq_ratio 0.4641 0.0001 0.80946 0.0012
resistant_mevent_mean 0.4601 0.3106 0.5654 -0.1598
resistant_mevent_std 0.4635 0.3106 0.6124 -0.1498
resistant_mevent_ratio 0.4977 0.1074 0.7130 0.0008
unresistant_mean -0.5768 -0.4234 -0.2310 -0.5258
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117

We can see resistant patient ratio still is around 40% and transmission becomes dominant.

In my forth simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.001,0.003,0.005,0.007,0.009,0.01,0.013,0.015,0.017,0.02]
  • resistant rate is [0.0001, 0.0005, 0.001, 0.003, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05,0.1]
  • removal rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, transmission rate declines.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

  5. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.3307 0.0006 0.0003 -0.8774
resistant_mean 0.4286 0.0832 0.7598 -0.2226
resistant_std 0.4186 0.0767 0.7652 -0.2223
resistant_ratio 0.4932 0.0864 0.7757 0.0020
resistant_trans_mean 0.4498 0.4165 0.4464 -0.1541
resistant_trans_std 0.4635 0.4249 0.5081 -0.1472
resistant_trans_ratio 0.4838 0.4242 0.4541 0.0048
resistant_acq_mean 0.3979 -0.0028 0.7906 -0.2259
resistant_acq_std 0.3861 -0.0072 0.7942 -0.2219
resistant_acq_ratio 0.4654 -0.0031 0.8083 0.0012
resistant_mevent_mean 0.4696 0.0391 0.6413 -0.1606
resistant_mevent_std 0.4440 0.0321 0.7228 -0.1384
resistant_mevent_ratio 0.4310 -0.0204 0.7430 0.0001
unresistant_mean -0.4471 -0.0226 -0.2115 -0.8073
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117

In my 5th simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.001,0.003,0.005,0.007,0.009,0.01,0.013,0.015,0.017,0.02]
  • resistant rate is [0.0001, 0.0003, 0.0005, 0.0006,0.0008, 0.001,0.002,0.003,0.004, 0.005,0.006]
  • removal rate is [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, transmission rate declines.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

  5. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.330615 -0.0006792 0.0014201 -0.87679
resistant_mean 0.3863738 0.08432931 0.7767763 -0.18807
resistant_std 0.3854115 0.07807917 0.7779885 -0.19055
resistant_ratio 0.4378407 0.08609018 0.7893371 0.005143
resistant_trans_mean 0.4164699 0.36328203 0.4779737 -0.13841
resistant_trans_std 0.459618 0.39809109 0.5173632 -0.14047
resistant_trans_ratio 0.4419088 0.36619433 0.4851712 0.002924
resistant_acq_mean 0.3537976 0.00117896 0.811591 -0.19017
resistant_acq_std 0.3522072 -0.0013577 0.8091318 -0.1913
resistant_acq_ratio 0.4084041 6.7293E-05 0.8257526 0.005448
resistant_mevent_mean 0.3609348 0.04064148 0.6593349 -0.11368
resistant_mevent_std 0.3606039 0.03689527 0.7542162 -0.09489
resistant_mevent_ratio 0.3089968 -0.0058446 0.6821514 0.014986
unresistant_mean -0.395233 -0.0147613 -0.128291 -0.84565
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117

In my 6th simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.01,0.015,0.02,0.025,0.03]
  • resistant rate is [0.0005,0.0008,0.001,0.003,0.003]
  • removal rate is [0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, transmission rate increases.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

  5. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.38497 0.003148 0.001002 -0.85929
resistant_mean 0.533449 0.134907 0.630236 -0.25977
resistant_std 0.5313 0.120254 0.623036 -0.2719
resistant_ratio 0.610924 0.144098 0.644838 -0.01408
resistant_trans_mean 0.569642 0.320729 0.403607 -0.20246
resistant_trans_std 0.632158 0.32224 0.408768 -0.20035
resistant_trans_ratio 0.611163 0.333642 0.412658 -0.01594
resistant_acq_mean 0.455241 -0.00442 0.720124 -0.27239
resistant_acq_std 0.440919 -0.01524 0.708786 -0.28118
resistant_acq_ratio 0.547289 -0.00392 0.740823 -0.01131
resistant_mevent_mean 0.39717 0.044483 0.569534 -0.12613
resistant_mevent_std 0.436071 0.039401 0.612283 -0.10819
resistant_mevent_ratio 0.275068 -0.01807 0.445137 0.047221
unresistant_mean -0.44694 -0.01219 -0.07071 -0.83256
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117

In my 6th simulation:

  • time sapn is [10,20,30,40,50]
  • contact/reinfection rate is [0.02, 0.025, 0.03, 0.035, 0.04]
  • resistant rate is [0.0005, 0.0008, 0.001, 0.0015, 0.002]
  • removal rate is [0.0005, 0.001, 0.005, 0.01, 0.02]

In this simulation, transmission rate increases resistant rate declines.

  1. Resistant patients.

  2. Resistant patients from transmission.

  3. Resistant patients from acquisition.

  4. Resistant patients caused by multiple events.

  5. Correlation matrix

timespan reinfact_rate resistant_rate removal_rate
sampling_mean -0.38707 -0.00111 0.000998 -0.85716
resistant_mean 0.65138 0.161475 0.474998 -0.27462
resistant_std 0.642934 0.170417 0.470908 -0.26976
resistant_ratio 0.729319 0.167063 0.482624 0.002102
resistant_trans_mean 0.65597 0.272545 0.313794 -0.21341
resistant_trans_std 0.696832 0.270721 0.332125 -0.19069
resistant_trans_ratio 0.706903 0.279175 0.32234 0.002976
resistant_acq_mean 0.554587 -0.00742 0.621311 -0.31705
resistant_acq_std 0.545029 0.031082 0.570595 -0.31412
resistant_acq_ratio 0.659971 -0.0074 0.633462 0.000633
resistant_mevent_mean 0.459595 0.059566 0.40704 -0.13443
resistant_mevent_std 0.511887 0.060276 0.427159 -0.13986
resistant_mevent_ratio 0.269831 -0.04562 0.239832 -0.00442
unresistant_mean -0.4554 -0.01799 -0.04863 -0.82904
unresistant_std 0.0511 0.0009 0.8168 -0.2276
unresistant_oncere_mean 0.0679 0.3386 -0.3428 -0.1116
unresistant_oncere_ratio 0.0816 0.3483 -0.3312 0.0039
unresistant_oncere_std 0.0494 0.3434 -0.3629 -0.1117 




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Content source: zhangfang615/Tuberculosis

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