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TNO / Covid-SEIR

Forecasting hospitalization and ICU rates of the COVID-19 outbreak: an efficient SEIR model
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Getting close to zero forecast values in prediction. #3

Open sarang-kharpate opened 3 years ago

sarang-kharpate commented 3 years ago

Hello Jan-Diederik,

  I am trying to utilize this model for my NCR region (where total population size is mostly 13 million). I have used your netherlands_april9_narrow.json as a reference and corona_esmda.py as a modelling technique.

In terms of parameters for my NCR regions I have changed the following parameters.

"t_max" : 350,

"population": 13e6,

"alpha" : [[0.1,0.5],[0.1,0.5],[0.6,0.8],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.8,1.0]],

"dayalpha" : [1, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"xmaxalpha": 280,

All the others numbers were unchanged. When I ran this with above parameters, the forecasted values I mostly got were close to zero for P5,P25,P50,P75,P90 in all the output file (e.g. posterior_prob_hospitalizedcum_calibrated_on_hospitalizedcum, ICU_calibrated_on_hospitalizedcum, infected_calibrated_on_hospitalizedcum etc.)

I have also attached the data and the changed parameter file that I have used for your reference.

Need your help and suggestion on the same and also wanted to know if we want to replicate this for any other region, what changes we need to incorporate in parameters to get a good forecast and what is the rationally behind changing the parameters.     I have attached my configuration file and input file as well.

NCR_7_May.txt input_file_7_May.txt input_file_icufrac_7_May.txt

Thanks

weesjdamv commented 3 years ago

Dear Sarang,

Thanks for reaching out. I will have a look at the datafile today, and also plan to update the code on githib the coming days as there are some further recent improvements, and I also will update the documentation

Kind regards, Jan-Diederik

From: sarang-kharpate @.> Sent: Friday, May 7, 2021 8:08 AM To: TNO/Covid-SEIR @.> Cc: Subscribed @.***> Subject: [TNO/Covid-SEIR] Getting negative forecast values in prediction. (#3)

Hello Jan-Diederik,

I am trying to utilize this model for my NCR region (where total population size is mostly 13 million). I have used your netherlands_april9_narrow.json as a reference and corona_esmda.py as a modelling technique.

In terms of parameters for my NCR regions I have changed the following parameters.

"t_max" : 350,

"population": 13e6,

"alpha" : [[0.1,0.5],[0.1,0.5],[0.6,0.8],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.8,1.0]],

"dayalpha" : [1, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"xmaxalpha": 280,

All the others numbers were unchanged. When I ran this with above parameters, the forecasted values I mostly got were close to zero for P5,P25,P50,P75,P90 in all the output file (e.g. posterior_prob_hospitalizedcum_calibrated_on_hospitalizedcum, ICU_calibrated_on_hospitalizedcum, infected_calibrated_on_hospitalizedcum etc.)

I have also attached the data and the changed parameter file that I have used for your reference.

Need your help and suggestion on the same and also wanted to know if we want to replicate this for any other region, what changes we need to incorporate in parameters to get a good forecast and what is the rationally behind changing the parameters.

I have attached my configuration file and input file as well.

NCR_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439361/NCR_7_May.txt input_file_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439367/input_file_7_May.txt input_file_icufrac_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439368/input_file_icufrac_7_May.txt

Thanks

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weesjdamv commented 3 years ago

Dear Sarang,

Attached you find an input file which should work I think (NCR_7_May2.txt).

The reason why you got the wrong results, mainly because:

  1. The beginning of the data series already has large values of hospitalized and ICU, best if the timeseries start from a low value.
  2. The fit on hospitalized did not work as this is a fit for column 6 and you put the column with hospitalized in column 4 (but this appears daily hospitalized (needs to be accumulated) or actual occupance (as ICU) but then needs to be in column 6. I interpreted it the latter way and shifted column 4 to 6
  3. I played a bit with ICUfrac (not reading from file but keeping constant over time), to obtain a reasonable fit with the hospitalized

Kind regards, Jan-Diederik

From: sarang-kharpate @.> Sent: Friday, May 7, 2021 8:08 AM To: TNO/Covid-SEIR @.> Cc: Subscribed @.***> Subject: [TNO/Covid-SEIR] Getting negative forecast values in prediction. (#3)

Hello Jan-Diederik,

I am trying to utilize this model for my NCR region (where total population size is mostly 13 million). I have used your netherlands_april9_narrow.json as a reference and corona_esmda.py as a modelling technique.

In terms of parameters for my NCR regions I have changed the following parameters.

"t_max" : 350,

"population": 13e6,

"alpha" : [[0.1,0.5],[0.1,0.5],[0.6,0.8],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.8,1.0]],

"dayalpha" : [1, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"xmaxalpha": 280,

All the others numbers were unchanged. When I ran this with above parameters, the forecasted values I mostly got were close to zero for P5,P25,P50,P75,P90 in all the output file (e.g. posterior_prob_hospitalizedcum_calibrated_on_hospitalizedcum, ICU_calibrated_on_hospitalizedcum, infected_calibrated_on_hospitalizedcum etc.)

I have also attached the data and the changed parameter file that I have used for your reference.

Need your help and suggestion on the same and also wanted to know if we want to replicate this for any other region, what changes we need to incorporate in parameters to get a good forecast and what is the rationally behind changing the parameters.

I have attached my configuration file and input file as well.

NCR_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439361/NCR_7_May.txt input_file_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439367/input_file_7_May.txt input_file_icufrac_7_May.txthttps://github.com/TNO/Covid-SEIR/files/6439368/input_file_icufrac_7_May.txt

Thanks

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{ "worldfile": false, "country": "../res/input_file_7_May.txt", "startdate": "7/2/20",

"t_max" : 460, "dt" : 0.1, "time_delay": 12, "population": 13e6,

"nr_prior_samples": 100, "nr_forecast_samples": 1500, "esmda_iterations": 8, "esmda_ignoredays": 0,

"N" : { "type": "uniform", "min": 50000, "max": 300000 }, "sigma" : 0.2, "gamma" : 0.5 , "R0" : { "type": "normal", "mean": 3.2, "stddev": 0.0 }, "m": 0.9, "c_alpha" : [[0.1,0.5],[0.1,0.5],[0.6,0.8],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.8,1.0]], "c_dayalpha" : [1, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"alpha" : [[0.1,0.8],[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.8,1.0]], "dayalpha" : [1, 20, 180, 214, 242, 270],

"delayHOS" : { "type": "uniform", "min": 9, "max": 9 }, "delayHOSREC" : { "type": "uniform", "min": 14, "max": 14, "smooth_sd": 4 }, "delayHOSD" : { "type": "uniform", "min": 1, "max": 4 }, "delayREC" : 12, "delayICUCAND": { "type": "uniform", "min": 0, "max": 0, "smooth_sd": 2 }, "delayICUD": { "type": "uniform", "min": 7, "max": 7, "smooth_sd": 3 }, "delayICUREC": { "type": "uniform", "min": 28, "max": 32, "smooth_sd": 8 },

"hosfrac" : 0.05, "dfrac" : 0.29, "icudfrac" : 0.3, "ICufrac": 0.075, "c_ICufrac": { "type":"normal", "mean": 0.19, "stddev": 0.01 }, "c_icufracfile": "../res/input_file_icufrac_7_May.txt",

"calibration_mode": [5], "observation_error": [100.0], "YMAX": 200e3, "XMAX": 360,

"hist_time_steps": [30,35,40,60], "p_values": [0.05, 0.3, 0.5, 0.7, 0.95], "plot" : { "legendloc" : "best", "legendfont" : "x-small", "y_axis_log": false, "hindcast_plume": true, "xmaxalpha": 360, "casename": "India-NCR" } } 1 27048 1283 18471 0 166 1560 2 28014 1300 19053 0 202 2363 3 28652 1328 19554 0 220 2615 4 29196 1346 20478 0 243 2822 5 30436 1359 21375 0 273 3018 6 31669 1381 22144 0 290 3147 7 32903 1404 23464 0 301 3280 8 34639 1422 24172 0 290 3271 9 36203 1449 25015 0 293 3437 10 37366 1467 25826 0 296 3558 11 38281 1489 26848 0 313 3605 12 39611 1515 27444 0 345 3823 13 40870 1548 27888 0 363 3955 14 42510 1586 28606 0 368 3969 15 44199 1618 30222 0 414 4056 16 45984 1663 31167 0 342 4119 17 47163 1712 32598 0 333 4117 18 47756 1743 33887 0 347 4206 19 49636 1774 35042 0 340 4187 20 51463 1803 36236 0 355 4367 21 53329 1844 37053 0 407 4577 22 55196 1884 38331 0 364 4512 23 57150 1930 39545 0 371 4559 24 58250 1963 40422 0 373 4639 25 58988 1998 41433 0 369 4597 26 61202 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sarang-kharpate commented 3 years ago

Hi Jan-Diederik,

First of all, thank for going through our problem statement and helping us out in this. Really appreciate it.

Few questions I have

  1. Calibration mode [5] indicates 'infected' or 'hospitalizedcum'?

  2. Can we keep observation errors constant across different calibration modes e.g. 100 in this case.

  3. We have numbers for infected, dead and recovered data starting from March 2020,but hospitals numbers are recorded from July 2020, because of this we decided to take input data from July only, which in turn causing this large numbers in start of data as numbers are getting cumulated from March-July. Can we put 0 for data related to hospitals and ICU for period from March to July? This will keep initial numbers of infected, dead and recovered on lower side for first 90 days of input data period.

  4. Input file columns as per below condition as stated on github page.

    • column 0 - day (number starting from 1)
    • column 1 - cumulative registered infected (positive test cases)
    • column 2 - cum dead
    • column 3 - cum recovered
    • column 4 - cumulative hospitalized
    • column 5 - actual IC units used (may be estimated or 0)
    • column 6 - actual hospitalized (put all to 0 to overwrite from estimates calculated from the hospital flow model)

It will be really helpful if you can guide us as to what should be our input columns be exactly like.

weesjdamv commented 3 years ago

Hi Sarang,

Thanks

Regarding your questions:

  1. This refers to the data column, so 5 is IC units used. The way it is specified in a list allows to do a calibration on multiple columns at a time.
  2. The calibration error is constant over the time range modelled, but you can exclude a starting number of days from fitting (the parameter "esmda_ignoredays":60). The number is absolute for columns 1,2,3,4 and interpreted as percentage (from 0 to 100) for 5 and 6. A 100% data error is save way to start the assimilation, to avoid overfitting if the model is not well capable to reproduce the data. Columns are as specified in the github page
  3. For extended the timeseries. Most likely this is not what you want to do, as you are putting emphasis on fitting data in the past. I forgot to mention you can also allow the model to grow into more infections at day 1 by putting a significantly higher number of timedelay, i.e. "time_delay": 27,this works well to overcome many of the erroneous results with negative values
  4. That is correct. I would not encourage to put 0, but estimates of what it could have been, but as mentioned in 2, the esmda_ignoredays also does the job well I think in combination with time_delay mentioned in 3. The github specification is correct, the input you had sent I think contained in column 4 actual hospitalized (which should be column 6).

Zeros in column 4 are fine, as long as you do not use them for data assimilation). With the changes above I provide you as an example with a joint assimilation for the both column 5 (ICU) and actual hospitalized (column 6). The joint fit is achieved through the variation in ICufrac

"ICufrac": { "type":"normal", "mean": 0.075, "stddev": 0.01 },

And ignores currently ignores the icufrac file which I commented out by the heading c_ "c_icufracfile":

It appears that the best fit (1-alfa) is decreasing sharply in the first months of 2021. If it is not caused by seasonal effects or relaxation of social distancing measures, this might also be in line with what you could expect from the entrance of VOC with ca 50% higher reproduction number such as the British mutant. This would also be in line with the 2-3 months it takes to become dominant (as seen in London area and European countries). We have seen the same in the past months in the Netherlands (but started lockdown at the start of entering of the VOC).

Kind regards, Jan-Diederik

From: sarang-kharpate @.> Sent: Saturday, May 8, 2021 2:39 AM To: TNO/Covid-SEIR @.> Cc: Wees, J.D.A.M. (Jan Diederik) van @.>; Comment @.> Subject: Re: [TNO/Covid-SEIR] Getting close to zero forecast values in prediction. (#3)

Hi Jan-Diederik,

First of all, thank for going through our problem statement and helping us out in this. Really appreciate it.

Few questions I have

  1. Calibration mode [5] indicates 'infected' or 'hospitalizedcum'?
  2. Can we keep observation errors constant across different calibration modes e.g. 100 in this case.
  3. We have numbers for infected, dead and recovered data starting from March 2020,but hospitals numbers are recorded from July 2020, because of this we decided to take input data from July only, which in turn causing this large numbers in start of data as numbers are getting cumulated from March-July. Can we put 0 for data related to hospitals and ICU for period from March to July? This will keep initial numbers of infected, dead and recovered on lower side for first 90 days of input data period.
  4. Input file columns as per below condition as stated on github page.

column 0 - day (number starting from 1) column 1 - cumulative registered infected (positive test cases) column 2 - cum dead column 3 - cum recovered column 4 - cumulative hospitalized column 5 - actual IC units used (may be estimated or 0) column 6 - actual hospitalized (put all to 0 to overwrite from estimates calculated from the hospital flow model)

It will be really helpful if you can guide us as to what should be our input columns be exactly like.

— You are receiving this because you commented. Reply to this email directly, view it on GitHubhttps://github.com/TNO/Covid-SEIR/issues/3#issuecomment-834905333, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AE2XGKZD2ITSDCCFDLIIHPTTMSB2HANCNFSM44JCTUAA. This message may contain information that is not intended for you. If you are not the addressee or if this message was sent to you by mistake, you are requested to inform the sender and delete the message. TNO accepts no liability for the content of this e-mail, for the manner in which you use it and for damage of any kind resulting from the risks inherent to the electronic transmission of messages.

{ "worldfile": false, "country": "../res/input_file_7_May.txt", "startdate": "7/2/20",

"t_max" : 460, "dt" : 0.1, "time_delay": 27, "population": 13e6,

"nr_prior_samples": 100, "nr_forecast_samples": 1500, "esmda_iterations": 8, "esmda_ignoredays": 60,

"N" : { "type": "uniform", "min": 50000, "max": 300000 }, "sigma" : 0.2, "gamma" : 0.5 , "R0" : { "type": "normal", "mean": 3.2, "stddev": 0.0 }, "m": 0.9, "c_alpha" : [[0.1,0.5],[0.1,0.5],[0.6,0.8],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.1,0.5],[0.8,1.0]], "c_dayalpha" : [1, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"alpha" : [[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.3,0.8],[0.5,1.0],[0.5,1.0],[0.5,1.0]], "dayalpha" : [1, 90, 120, 180, 214, 242, 270, 280, 290],

"delayHOS" : { "type": "uniform", "min": 9, "max": 9 }, "delayHOSREC" : { "type": "uniform", "min": 14, "max": 14, "smooth_sd": 4 }, "delayHOSD" : { "type": "uniform", "min": 1, "max": 4 }, "delayREC" : 12, "delayICUCAND": { "type": "uniform", "min": 0, "max": 0, "smooth_sd": 2 }, "delayICUD": { "type": "uniform", "min": 7, "max": 7, "smooth_sd": 3 }, "delayICUREC": { "type": "uniform", "min": 28, "max": 32, "smooth_sd": 8 },

"hosfrac" : 0.02, "dfrac" : 0.29, "icudfrac" : 0.3, "c_ICufrac": 0.075, "ICufrac": { "type":"normal", "mean": 0.075, "stddev": 0.01 }, "c_icufracfile": "../res/input_file_icufrac_7_May.txt",

"calibration_mode": [5,6], "observation_error": [50.0, 50.0], "YMAX": 1000e3, "XMAX": 360,

"hist_time_steps": [30,35,40,60], "p_values": [0.05, 0.3, 0.5, 0.7, 0.95], "plot" : { "legendloc" : "best", "legendfont" : "x-small", "y_axis_log": false, "hindcast_plume": true, "xmaxalpha": 360, "casename": "India-NCR" } }

sarang-kharpate commented 3 years ago

Hello Jan-Diederik,

Thanks for the guidance. With your help in tuning parameter files along with input file changes we are able to get pretty good fit and projection for Hospitalized and ICU values. The code is mimicking the trend pretty closely. I have attached new input file and latest configuration files. I do have few more questions for you

  1. We are still getting very high values for infected predictions( P0.095 and observed for file NCR_10_May_best_posterior_prob_infected_calibratedon[5, 6].csv), and when we try to optimize parameter for infection related changes ( e.g. m, R0, sigma, gamma values) our hospital prediction also get changes.

  2. When we try to use calibration mode as [1, 5, 6] code errors out like 

    (numpy.linalg.LinAlgError: Singular matrix error)

    even if we use [1] only it still gets errors out. But it runs smoothly for [5, 6] calibration mode. Am I putting something wrong in any parameters?

  3. This code generates .h5 file in output folder. If I explore .h5 file I can see model and posterior sub sections in h5 file. I think columns are forecasted values for time, susceptible, exposed, infected, remove, hospital, hospital cumulative, ICU, ICU cumulative, recovered ,dead and alpha run. All predicted by model. Is my interpretation correct for 13 columns present in file?

df = pd.DataFrame(hf['model']['posterior'][0]).T
cols = ['O_TIME' ,'O_SUS' ,'O_EXP' ,'O_INF' ,'O_REM' ,'O_HOS' ,'O_HOSCUM' ,'O_ICU' ,'O_ICUCUM' ,'O_REC' ,'O_DEAD' ,'O_CUMINF' ,'O_ALPHARUN']
df.columns = cols

Thanks

NCR_10_May_best.txt input_file_10_May_july_onwards_final.txt

weesjdamv commented 3 years ago

Hello Sarang,

Thanks for the questions.

Regarding the error with the cumulative infections, that is I think caused by an erroneous input for the error range on this. For 1,2,3,4 it should be absolute and I have chosen a very large value in the attached file (100000), then in principle it works.

However with the 1,5,6 it is also good to increase the number of runs 200-500 and you need to have uncertainty in the transfer from infected to hospitalized (hosfrac) and the relative fraction of hospitalized going to ICU.

I have also allowed to vary more the treatment times for hospitalizion and ICU and then I get a rather good fit for 5,6 and loose fit for 1.

To make it work I also ignored a fit for the first 100 days.

For question 3: I think you are correct

Kind regards, Jan-Diederik

From: sarang-kharpate @.> Sent: Tuesday, May 11, 2021 8:30 AM To: TNO/Covid-SEIR @.> Cc: Wees, J.D.A.M. (Jan Diederik) van @.>; Comment @.> Subject: Re: [TNO/Covid-SEIR] Getting close to zero forecast values in prediction. (#3)

Hello Jan-Diederik,

Thanks for the guidance. With your help in tuning parameter files along with input file changes we are able to get pretty good fit and projection for Hospitalized and ICU values. The code is mimicking the trend pretty closely. I have attached new input file and latest configuration files. I do have few more questions for you

  1. We are still getting very high values for infected predictions( P0.095 and observed for file NCR_10_May_best_posterior_prob_infected_calibratedon[5, 6].csv), and when we try to optimize parameter for infection related changes ( e.g. m, R0, sigma, gamma values) our hospital prediction also get changes.
  2. When we try to use calibration mode as [1, 5, 6] code errors out like

(numpy.linalg.LinAlgError: Singular matrix error)

even if we use [1] only it still gets errors out. But it runs smoothly for [5, 6] calibration mode. Am I putting something wrong in any parameters?

  1. This code generates .h5 file in output folder. If I explore .h5 file I can see model and posterior sub sections in h5 file. I think columns are forecasted values for time, susceptible, exposed, infected, remove, hospital, hospital cumulative, ICU, ICU cumulative, recovered ,dead and alpha run. All predicted by model. Is my interpretation correct for 13 columns present in file?

df = pd.DataFrame(hf['model']['posterior'][0]).T

cols = ['O_TIME' ,'O_SUS' ,'O_EXP' ,'O_INF' ,'O_REM' ,'O_HOS' ,'O_HOSCUM' ,'O_ICU' ,'O_ICUCUM' ,'O_REC' ,'O_DEAD' ,'O_CUMINF' ,'O_ALPHARUN']

df.columns = cols

Thanks

NCR_10_May_best.txthttps://github.com/TNO/Covid-SEIR/files/6456902/NCR_10_May_best.txt input_file_10_May_july_onwards_final.txthttps://github.com/TNO/Covid-SEIR/files/6456906/input_file_10_May_july_onwards_final.txt

— You are receiving this because you commented. Reply to this email directly, view it on GitHubhttps://github.com/TNO/Covid-SEIR/issues/3#issuecomment-837916528, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AE2XGK3NIBDSSG4CIKHYBVDTNDFGFANCNFSM44JCTUAA. This message may contain information that is not intended for you. If you are not the addressee or if this message was sent to you by mistake, you are requested to inform the sender and delete the message. TNO accepts no liability for the content of this e-mail, for the manner in which you use it and for damage of any kind resulting from the risks inherent to the electronic transmission of messages.

{ "worldfile": false, "country": "../res/input_file_10_May_july_onwards_final.txt", "startdate": "07/02/20",

"t_max" : 460, "dt" : 0.1, "time_delay": 27, "population": 13e6,

"nr_prior_samples": 500, "nr_forecast_samples": 2500, "esmda_iterations": 8, "esmda_ignoredays": 100,

"N" : { "type": "uniform", "min": 50000, "max": 300000 }, "sigma" : 0.2, "gamma" : 0.5 ,

"R0" : { "type": "normal", "mean": 3.2, "stddev": 0.0 },

"m": 0.90,

"alpha" : [[0.6, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.7, 1.0]], "dayalpha" : [1, 122, 152, 183, 214, 242, 270],

"c_alpha" : [[0.75, 0.95],[0.75, 0.95],[0.6, 0.74],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.6, 0.74],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.75, 0.95]], "c_dayalpha" : [1, 47, 62, 78, 92, 108, 123, 142, 157, 170, 200, 231, 261, 292, 323, 351, 379],

"delayHOS" : { "type": "uniform", "min": 9, "max": 9 }, "delayHOSREC" : { "type": "uniform", "min": 7, "max": 20, "smooth_sd": 7 }, "delayHOSD" : { "type": "uniform", "min": 1, "max": 4 },

"delayREC" : 12,

"delayICUCAND": { "type": "uniform", "min": 0, "max": 0, "smooth_sd": 2 },

"delayICUD": { "type": "uniform", "min": 7, "max": 7, "smooth_sd": 3 },

"delayICUREC": { "type": "uniform", "min": 26, "max": 34, "smooth_sd": 20 },

"c_hosfrac" : 0.05,

"hosfrac": { "type":"normal", "mean": 0.05, "stddev": 0.05 },

"dfrac" : 0.1,

"icudfrac" : 0.3,

"c_ICufrac": 0.1,

"ICufrac": { "type":"normal", "mean": 0.1, "stddev": 0.03 },

"calibration_mode": [1,5, 6], "observation_error": [100000,50, 50],

"YMAX": 200e3, "XMAX": 360,

"hist_time_steps": [30,35,40,60],

"p_values": [0.05, 0.3, 0.5, 0.7, 0.95],

"plot" : { "legendloc" : "best", "legendfont" : "x-small", "y_axis_log": false, "hindcast_plume": true, "xmaxalpha": 360, "casename": "India-NCR" } }

weesjdamv commented 3 years ago

Dear Sarang,

In extension to my mail of last night I think I found a way to solve largely the fitting issue for the starting data.

You need to add one line of code in bin/corona_esmda.py at line 404: dayr = max(dayr,1)

After this change you can use the attached input file (which contains the alpha and dayalpha values you have sent me yesterday).

The essence of fitting the first data is to set the the day_alpha of the first step to a negative number (i.e. -20) such that the step’s alpha is applied before the data starts and the convolution with treatment times allows to reproduce the first data points. To make it work you also need to put the timedelay to a sufficienty large number (“time_delay”: 40)

@.D74700.6CABF5E0] @.D74700.6CABF5E0]

From: sarang-kharpate @.> Sent: Tuesday, May 11, 2021 8:30 AM To: TNO/Covid-SEIR @.> Cc: Wees, J.D.A.M. (Jan Diederik) van @.>; Comment @.> Subject: Re: [TNO/Covid-SEIR] Getting close to zero forecast values in prediction. (#3)

Hello Jan-Diederik,

Thanks for the guidance. With your help in tuning parameter files along with input file changes we are able to get pretty good fit and projection for Hospitalized and ICU values. The code is mimicking the trend pretty closely. I have attached new input file and latest configuration files. I do have few more questions for you

  1. We are still getting very high values for infected predictions( P0.095 and observed for file NCR_10_May_best_posterior_prob_infected_calibratedon[5, 6].csv), and when we try to optimize parameter for infection related changes ( e.g. m, R0, sigma, gamma values) our hospital prediction also get changes.
  2. When we try to use calibration mode as [1, 5, 6] code errors out like

(numpy.linalg.LinAlgError: Singular matrix error)

even if we use [1] only it still gets errors out. But it runs smoothly for [5, 6] calibration mode. Am I putting something wrong in any parameters?

  1. This code generates .h5 file in output folder. If I explore .h5 file I can see model and posterior sub sections in h5 file. I think columns are forecasted values for time, susceptible, exposed, infected, remove, hospital, hospital cumulative, ICU, ICU cumulative, recovered ,dead and alpha run. All predicted by model. Is my interpretation correct for 13 columns present in file?

df = pd.DataFrame(hf['model']['posterior'][0]).T

cols = ['O_TIME' ,'O_SUS' ,'O_EXP' ,'O_INF' ,'O_REM' ,'O_HOS' ,'O_HOSCUM' ,'O_ICU' ,'O_ICUCUM' ,'O_REC' ,'O_DEAD' ,'O_CUMINF' ,'O_ALPHARUN']

df.columns = cols

Thanks

NCR_10_May_best.txthttps://github.com/TNO/Covid-SEIR/files/6456902/NCR_10_May_best.txt input_file_10_May_july_onwards_final.txthttps://github.com/TNO/Covid-SEIR/files/6456906/input_file_10_May_july_onwards_final.txt

— You are receiving this because you commented. Reply to this email directly, view it on GitHubhttps://github.com/TNO/Covid-SEIR/issues/3#issuecomment-837916528, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AE2XGK3NIBDSSG4CIKHYBVDTNDFGFANCNFSM44JCTUAA. This message may contain information that is not intended for you. If you are not the addressee or if this message was sent to you by mistake, you are requested to inform the sender and delete the message. TNO accepts no liability for the content of this e-mail, for the manner in which you use it and for damage of any kind resulting from the risks inherent to the electronic transmission of messages.

{ "worldfile": false, "country": "../res/input_file_10_May_july_onwards_final.txt", "startdate": "07/02/20",

"t_max" : 460, "dt" : 0.1, "time_delay": 40, "population": 13e6,

"nr_prior_samples": 200, "nr_forecast_samples": 2500, "esmda_iterations": 8, "esmda_ignoredays": 0,

"N" : { "type": "uniform", "min": 50000, "max": 300000 }, "sigma" : 0.2, "gamma" : 0.5 ,

"R0" : { "type": "normal", "mean": 3.2, "stddev": 0.0 },

"m": 0.90,

"alpha" : [[0.2, 0.8], [0.2, 0.8], [0.5, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.7, 1.0]], "dayalpha" : [-20, 14, 33, 48, 61, 91, 122, 152, 183, 214, 242, 270],

"c_alpha" : [[0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.2, 0.8], [0.7, 1.0]], "c_dayalpha" : [-20, 122, 152, 183, 214, 242, 270],

"c_alpha" : [[0.75, 0.95],[0.75, 0.95],[0.6, 0.74],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.6, 0.74],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.1, 0.59],[0.75, 0.95]], "c_dayalpha" : [1, 47, 62, 78, 92, 108, 123, 142, 157, 170, 200, 231, 261, 292, 323, 351, 379],

"delayHOS" : { "type": "uniform", "min": 9, "max": 9 }, "delayHOSREC" : { "type": "uniform", "min": 7, "max": 20, "smooth_sd": 7 }, "delayHOSD" : { "type": "uniform", "min": 1, "max": 4 },

"delayREC" : 12,

"delayICUCAND": { "type": "uniform", "min": 0, "max": 0, "smooth_sd": 2 },

"delayICUD": { "type": "uniform", "min": 7, "max": 7, "smooth_sd": 3 },

"delayICUREC": { "type": "uniform", "min": 26, "max": 34, "smooth_sd": 20 },

"c_hosfrac" : 0.05,

"hosfrac": { "type":"normal", "mean": 0.05, "stddev": 0.05 },

"dfrac" : 0.1,

"icudfrac" : 0.3,

"c_ICufrac": 0.1,

"ICufrac": { "type":"normal", "mean": 0.1, "stddev": 0.03 },

"calibration_mode": [1,5, 6], "observation_error": [100000,50, 50],

"YMAX": 200e3, "XMAX": 360,

"hist_time_steps": [30,35,40,60],

"p_values": [0.05, 0.3, 0.5, 0.7, 0.95],

"plot" : { "legendloc" : "best", "legendfont" : "x-small", "y_axis_log": false, "hindcast_plume": true, "xmaxalpha": 360, "casename": "India-NCR" } }

sarang-kharpate commented 3 years ago

Hello Jan-Diederik,

Thanks a lot for you quick and prompt reply, because of this we have progressed so much in our work. I am attaching our final inputs and configuration files for which we are getting best fits. We have finalized on 3 configuration files (infections, Hospitalized and ICU).

I just want to understand one logic. When we calculate values for writing back to csv files (0.05, 0.25,...0.95) we sort the values and then take (length of file * confidence interval) and then take that index position values from array and write it back.

Basically I just want to understand below logic.

for post_day in posterior_curves[t_ind, :]:
    array_sorted = np.sort(post_day)
    p_array.append([array_sorted[int(posterior_length * p)] for p in p_values])

Definitely there must be some thought behind this step, I am not able to understand it. Also what if I take last value of last iteration array? Last value of posterior as final my final output.

Thanks and Regards, Sarang Kharpate

input_file_13_May.txt Best_Infected.txt best_hospital.txt Best_ICU.txt

weesjdamv commented 3 years ago

Hello Sarang,

The posterior_curves data are taken on a day by day basis (post_day) starting after time-delay (t_ind). The post_day values are sorted in array_sorted and then you pick for each day the index corresponding to the p value. If posterior_length=250 (this corresponds to the number of "nr_prior_samples": 250), and the outcome values have been sorted, the p=0.5 corresponds to index 125.

I hope that helps, and I am very glad to hear this all helps in progressing on your work.

One further thing what crossed my mind when looking at the models. Initially in the WHO paper we assumed hosfrac close to 0.05 but later we adjusted this to lower values for the Netherlands to ca 0.015 based on percentage of people with antibodies of blood donors.

I guess in your case in reality the number of infected and people with antibodies may have accumulated to a 10 fold higher value than registered (if you assume hosfrac of about 0.01). This number I guess is still relatively high for the relatively young population in India compared to the Netherlands. Including a low hosfrac may give rise to herd immunity and assist in brining down the future spread significantly

I guess you may also have noticed that the convolution of infected with treatment times is done with a gamma/ (lognormal) function, which is more realistic and behaves better than gaussian smoothing (as it avoids negative treatment times). These are also explained with best fit parameters in the Netherlands in this manuscript:

Performance of progressive and adaptive COVID-19 exit strategies: a stress test analysis for managing intensive care unit rates (medrxiv.org)https://www.medrxiv.org/content/medrxiv/early/2020/05/20/2020.05.16.20102947.full.pdf

Kind regards, Jan-Diederik

From: sarang-kharpate @.> Sent: Thursday, May 13, 2021 5:54 PM To: TNO/Covid-SEIR @.> Cc: Wees, J.D.A.M. (Jan Diederik) van @.>; Comment @.> Subject: Re: [TNO/Covid-SEIR] Getting close to zero forecast values in prediction. (#3)

Hello Jan-Diederik,

Thanks a lot for you quick and prompt reply, because of this we have progressed so much in our work. I am attaching our final inputs and configuration files for which we are getting best fits. We have finalized on 3 configuration files (infections, Hospitalized and ICU).

I just want to understand one logic. When we calculate values for writing back to csv files (0.05, 0.25,...0.95) we sort the values and then take (length of file * confidence interval) and then take that index position values from array and write it back.

Basically I just want to understand below logic.

for post_day in posterior_curves[t_ind, :]:

array_sorted = np.sort(post_day)

p_array.append([array_sorted[int(posterior_length * p)] for p in p_values])

Definitely there must be some thought behind this step, I am not able to understand it. Also what if I take last value of last iteration array? Last value of posterior as final my final output.

Thanks and Regards, Sarang Kharpate

input_file_13_May.txthttps://github.com/TNO/Covid-SEIR/files/6473577/input_file_13_May.txt Best_Infected.txthttps://github.com/TNO/Covid-SEIR/files/6473587/Best_Infected.txt best_hospital.txthttps://github.com/TNO/Covid-SEIR/files/6473588/best_hospital.txt Best_ICU.txthttps://github.com/TNO/Covid-SEIR/files/6473589/Best_ICU.txt

— You are receiving this because you commented. Reply to this email directly, view it on GitHubhttps://github.com/TNO/Covid-SEIR/issues/3#issuecomment-840654762, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AE2XGK3VBINWVJ2EXQA2ALTTNPY2DANCNFSM44JCTUAA. This message may contain information that is not intended for you. If you are not the addressee or if this message was sent to you by mistake, you are requested to inform the sender and delete the message. TNO accepts no liability for the content of this e-mail, for the manner in which you use it and for damage of any kind resulting from the risks inherent to the electronic transmission of messages.