This insight began as a March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to
Gabo HN for the original insight. The following links are theirs:
Initial data from:
Italian data [
link] (Mar 4)
Incubation estimation [
link]
Northern Kentucky University
May 2nd, 2020
This is an update of our model from April 9th, 2020. As we prepare for our final exam, I read
a story in The Guardian about Italy's struggle to return to normalcy. The final paragraphs:
During the debate in the Senate on Thursday, the opposition parties
grilled Conte. Ex-prime minister Matteo Renzi, who has called for less
restraint in the reopening, remarked, “The people in Bergamo and Brescia
who are gone, those who died of the virus, if they could speak, they’d
tell us to relaunch the country for them, in their honour.”
Renzi’s controversial statement was harshly criticised by doctors who
warned that the spread of the disease, which, as of Thursday, had
killed almost 30,000 people in the country and infected more than
205,000 [ael: my emphasis], was not over and that a misstep could take the entire country
back to mid-March coronavirus levels.
“We risk a new wave of infections and outbreaks if we’re not
careful,” said Tullio Prestileo, an infectious diseases specialist at
Palermo’s Benefratelli Hospital. “If we don’t realise this, we could
easily find ourselves back where we started. In that case, we may not
have the strength to get back up again.”
I have since updated the dataset, to include total cases from February 24th to May 2nd. I went to
Harvard's Covid-19 website for Italy and and then to their daily updates, available at
github. I downloaded the
regional csv file for May 2nd, which had regional totals (21 regions); I grabbed the column
"totale_casi" and did some processing to get the daily
totals from the
24th of February to the 2nd of May.
The cases I obtained in this way matched those used by
Gabo HN.
The initial data they used started on March 3rd (that's the 0 point in this Insight).
You
can get a good fit to the data through April 9th by choosing the following (and notice
that I've short-circuited the process from the Infectious to the Dead
and Recovered). I've also added the Infectious to the Total cases.
The question is: how well did we do at modeling this epidemic through May 2nd (day 60)? And how can we change the model to do a better job of capturing the outbreak from March 3rd until May 2nd?
Incubation Rate: .025
R0: 3
First Lockdown: IfThenElse(Days() == 5, 16000000, 0)
Total Lockdown: IfThenElse(Days() >= 7, 0.7,0)
(I
didn't want to assume that the "Total Lockdown" wasn't leaky! So it
gets successively tighter, but people are sloppy, so it simply goes to 0
exponentially, rather than completely all at once.)
deathrate: .01
recoveryrate: .03
"Death flow": [deathrate]*[Infectious]
"Recovery flow": [recoveryrate]*[Infectious]
Total Reported Cases: [Dead]+[Surviving / Survived]+[Infectious]
Resources:
![This insight began as a March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the original insight. The following links are theirs: Initial data from: Italian data [ link ] (Mar 4) Incubation estimation [ link ] Andy Long Northern Kentucky University May](https://insightmakercloud-files.storage.googleapis.com/f8abb1ce-8f04-48b8-881d-b5abdf4c3b7d/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20241123%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20241123T172408Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
