Posterior distribution long time simulation

[chiaraunipg]

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

[weesjdamv]

Dear Chiara, Thanks for sharing. Basically, you modelled a new outbreak. The only way to circumvent this is to assume a very strict regime on contact tracing (alfa = 0.6-0.7), and/or renewed social distancing after the start of outbreak. In the next updated we will also output the infections for the confidence (and ensemble) plots, this makes it more insightful what is the level of remaining infections in the population Kind regards Jan-Diederik From: Chiara Antonini <[email protected]> Sent: Wednesday, April 15, 2020 1:14 PM To: TNO/Covid-SEIR <[email protected]> Cc: Subscribed <[email protected]> Subject: [TNO/Covid-SEIR] Posterior distribution long time simulation (#2) 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 [umbriaposterior_ensemble_hospitalized]<https://user-images.githubusercontent.com/26300936/79330833-778fa000-7f1a-11ea-8c53-fb1ed28956df.png> [netherlands_april9_narrowposterior_ensemble_hospitalized_longterm_alt]<https://user-images.githubusercontent.com/26300936/79330981-bd4c6880-7f1a-11ea-8764-a7a8fb6b3dca.png> — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub<#2>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AE2XGKYDRL3JQAGDBBGI6PDRMWJJHANCNFSM4MIPR6LQ>. 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.