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March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the insight.

Initial data from:

Italian data [link] (Mar 4)

Incubation estimation [link]

Andy Long

April 9th, 2020

I have since updated the dataset, to include total cases from February 24th to April 9th.

I went to
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KDFYZW
and downloaded the archive for April 9th:
https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/KDFYZW/C2HSTK&version=19.0

I dug through the files, and found the file dpc-covid19-ita-regioni.csv, which had regional totals (21 regions); I grabbed the column "totale_casi" and used some lsp code to get the daily totals from the 24th of February til the 9th of April.

The good news is that the cases I obtained in this way matched those used by Gabo HN.

The initial data started on March 3rd (that's 0 in this Insight).

You can get a good fit to the data 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.

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:

* https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported

MAT375 Version of Italian COVID 19 outbreak control

Andrew E Long

32

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 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:

* https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported

Final Version of Italian COVID-19 outbreak

Andrew E Long

16

COVID-19 Systemigram Model Building Exercise

Josiah Best

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates trigger more rapid government intervention, which reduces infectious cases. The impact on the economy, though, of higher detected cases is negative.

Burnie COVID-19 outbreak demo model version 2

Steven D'Alessandro

39

Covid proyecto

E T

3

spread model of COVID-19

HIKARU ASANO

Сovid 19 South Korea

Dauren07

7

Covid-19

Марина Якимова

Covid France

Bisyltan

botash

Ақбота Нәрікбай

5

Model ini dirancang untuk membuat model tentang penyebaran Covid-19 dan vaksinasi di Kabupaten Sleman pada November 2022

Model ini dibuat untuk memenuhi tugas kelompok dari matakuliah Metode Penyelesaian Masalah dan Pemodelan, atas nama :

Sabilla Halimatus Mahmud

Nurul Widyastuti
Muhammad Najib

Edit Model Penyebaran Covid-19 di Kabupaten Sleman

Najib Muhammad

7

An SIR model for Covid-19

This is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.

Let's think about things on the scale of a week. What happens over a week?

With an Ro of 2 (2 people infected for each infected individual, over the course of a week); recovery rate of 1 (every infected person loses their infectiousness after a week), and resusceptible rate of .05 (meaning .05, or a twentieth of the recovered lose their immunity each week), the disease peaks -- does the wave, then waves again before the year is out, then ultimately becomes

"endemic" (that is, it's never going away, which is clear after two years -- that is, a time of 104 weeks). This is like our seasonal flu (only the disease in this simulation doesn't illustrate seasonality -- that requires a more complicated model).

With an Ro of .9, recovery rate of 1, and resusceptible rate of .05, the disease is eliminated.

Masking, social distancing (including quarantining following contact), and quarantines all serve to reduce infectivity. And if we can drive infectivity down far enough, the disease can be eliminated. Other things that help is slowing down the resusceptibility, by vaccinating. Vaccines (in general) impart an immune response that reduces -- or even eliminates -- your susceptibility. We are still learning the extent to which these vaccines impart long-term immunity.

Other tools at our disposal include Covid-19 treatments, which increase the recovery rate, and vaccinations, which reduce the resusceptible rate. These can also serve to help us eradicate a disease, so that it doesn't become endemic (and so plague us forever).

Andy Long

Mathematics and Statistics

Some resources:

Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

MAT115 Covid Simulation

Andrew E Long

COVID-19 Vaccination of indigenous West Australians

Elizabeth McLachlan

Environment, Health, & Business

Covid-19 Systemigram

Christopher Phillips

This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.

The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.

covid-19 in France

Егор Саяпин

7 months ago

COVID-SIR Model w/optimization

Sonia Aronson

Evolution of Covid-19 in Brazil:

A System Dynamics Approach

Villela, Paulo (2020)
villela.paulo@gmail.com

This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.

Evolution of Covid-19 in Brazil: A System Dynamics Approach

Paulo Villela

3

Covid-19 Pandemic

Kaushal Reddy

COVID Systemigram

Olivia Hersey

Modelo covid

Gabriele Cesar Iwashima

Өзіндік жұмыс 2

Аружан Серік

18

Covid 19 in China

Aruzhan

Data provided by: PHE and Worldometers

UK COVID 19 Simulator

Richard Bartholomew

15

Explanation of the Model

This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate.

Assumptions

Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.

Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.

Interesting Insight

There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death.

In contrast, lower testing rates increase the death rate and cases.

Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.

BMA 708, Assessment Tast 3: Complex System, Burnie COVID-19 outbreak, Diprina Shakya-519673

Diprina Shakya

6