tno / covid-seir Goto Github PK

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

Is it possible to see the dataset used for calibration of Lombardy region?
Thank you.

Hello,
sorry to disturb again, I have another question. I calibrated the model using the hospitalized data of my italian region (Umbria) with corona_mc.py and then I tried to rerun the simulation with a longer time, introducing a gradual variation of the lockdown ("alpha" : [[0.45,0.55],[0.75,0.85], [0.65, 0.75], [0.55,0.65],[0.45,0.55],[0.35,0.45],[0.25,0.35],[0.15,0.25],[0.05,0.15],[0,0]] and
"dayalpha" : [5, 11, 65, 99, 113, 127, 141, 155, 176, 190]). In this case, the posterior distribution for hospitalized patients (but also dead) has a really high peak which seems to be unrealistic (attached you can find the hospitalized posterior distribution). I tried that also with your example netherlands_9april_narrow.json (attached there is the hospitalized posterior distribution). Does it make sense or did I make some mistakes on the simulation? Thank you very much for your time.
Chiara