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A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

COVID-19 Delta Variant Spread Among Emory Students
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COVID-19 in Iran 2 - ө. ж.
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Ausbreitung von SARS-CoV-19 in verschiedenen Ländern
- bitte passen Sie die Variablen über die Schieberegler weiter unten entsprechend an

Italien

    ältere Bevölkerung (>65): 0,228
    Faktor der geschätzten unentdeckten Fälle: 0,6
    Ausgangsgröße der Bevölkerung: 60 000 000
    hoher Blutdruck: 0,32 (gbe-bund)
    Herzkrankheit: 0,04 (statista)
    Anzahl der Intensivbetten: 3 100


Deutschland

    ältere Bevölkerung (>65): 0,195 (bpb)
    geschätzte unentdeckte Fälle Faktor: 0,2 (deutschlandfunk)
    Ausgangsgröße der Bevölkerung: 83 000 000
    hoher Blutdruck: 0,26 (gbe-bund)
    Herzkrankheit: 0,2-0,28 (Herzstiftung)
   
Anzahl der Intensivbetten: 5 880


Frankreich

    ältere Bevölkerung (>65): 0,183 (statista)
    Faktor der geschätzten unentdeckten Fälle: 0,4
    Ausgangsgröße der Bevölkerung: 67 000 000
    Bluthochdruck: 0,3 (fondation-recherche-cardio-vasculaire)
    Herzkrankheit: 0,1-0,2 (oecd)
   
Anzahl der Intensivbetten: 3 000


Je nach Bedarf:

    Anzahl der Begegnungen/Tag: 1 = Quarantäne, 2-3 = soziale Distanzierung , 4-6 = erschwertes soziales Leben, 7-9 = überhaupt keine Einschränkungen // Vorgabe 2
    Praktizierte Präventivmassnahmen (d.h. sich regelmässig die Hände waschen, das Gesicht nicht berühren usw.): 0.1 (niemand tut etwas) - 1 (sehr gründlich) // Vorgabe 0.8
    Aufklärung durch die Regierung: 0,1 (sehr schlecht) - 1 (sehr transparent und aufklärend) // Vorgabe 0,9
    Immunitätsrate (aufgrund fehlender Daten): 0 (man kann nicht immun werden) - 1 (wenn man es einmal hatte, wird man es nie wieder bekommen) // Vorgabe 0,4


Schlüssel

    Anfällige: Menschen sind nicht mit SARS-CoV-19 infiziert, könnten aber infiziert werden
    Infizierte: Menschen sind infiziert worden und haben die Krankheit COVID-19
    Geheilte: Die Menschen haben sich gerade von COVID-19 erholt und können es in diesem Stadium nicht mehr bekommen
    Tote: Menschen starben wegen COVID-19
    Immunisierte: Menschen wurden immun und können die Krankheit nicht mehr bekommen
    Kritischer Prozentsatz der Wiederherstellung: Überlebenschance ohne spezielle medizinische Behandlung



SARS-CoV-19 Modell von Lucia Vega Resto
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covid-19
10 months ago
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Tugas 3 Pemodelan Transportasi Laut_Yopy Anjas
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This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents. 

 

Assumptions:

Government policies will be triggered when reported COVID-19 case are 10 or less;

Government policies reduces the infection and economic growth at the same time.

 


Interesting Insights:

In the first two weeks, the infected people showed an exponential growth, in another word, that’s the most important period to control the number of people who got affected. 

 

Clone of Model of COVID-19 Outbreak in Burnie, Tasmania
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Данная модель отражает распространение COVID-19 в России на основе статистики за 2020 год. Модель построена в среде Insight Maker по типу SEIRD (Susceptible–Exposed–Infected–Recovered–Dead), с упрощённой динамикой.
Основные параметры:
-Исходное население (масштабировано): 1000 человек
-Заражённые в начале: 2.12% → 21 человек
-Выздоровевшие (Recovery period): через 14 дней
-Смертность: 1.71% от заболевших
-Потеря иммунитета: не учитывается (0%)
-Exogenous (внешнее заражение): 2.12%
-Transmit: 0.3 (зависит от количества заражённых и восприимчивых)
covid-19 in russia
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Агентное моделирование Covid-19
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Using the reading assignment from El-Taliawi and Hartley on using a SSM for COVID-19 follow the steps for SSM to include:

1)  Describe the Problem (unstructured).

2)  Develop a Root Definition for the COVID-19 problem space by identifying the three elements:  what, how, why.   A System to do X, by (means of) Y, in order to achieve Z.

        X - What the system does

        Y -  How it does it

        Z - Why is it being done

(see slide 33 in the Systems Thinking Workshop reading)

3)  Identify the Perspectives (CATWOE)

4)  Develop a basic Systemigram / Rich Picture to tell the story.

Submit your assignment as a Word document or PDF that addresses #1-4.  You can use InsightMaker to create your systemigram or use the Systemitool which you can access at SERC hereLinks to an external site.

If you use InsightMaker, try presenting your results as a Story using the Storytelling capabilityLinks to an external site..

You will have TWO WEEKS to complete this assignment (due on March 7th).

Systemigram Model Building Exercise Luis Vega
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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]

Based on my student Sean's work, I altered the death rate to introduce the notion that doctors are getting better at saving lives:
[deathrate] = 0.02/(.0022*Days()^1.8+1)
I don't agree with this model of the death rate, but it was a start motivated by his work. Thanks Sean!:)

Resources:
  * Recent news: "Since the early days of the outbreak in China, scientists have known that SARS-CoV-2 is unusually contagious — more so than influenza or a typical cold virus. Scientific estimates of the reproduction number — the R0, which is the number of new infections that each infected person generates on average — have varied among different communities and different points but have generally been between 2 and 4. That is significantly higher than seasonal influenza."
  * https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported
  * https://covid19.healthdata.org/italy
Key of Final Version of Italian COVID-19 outbreak
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Tugas Pemodelan Transportasi Laut
Memodelkan persebaran pandemik covid-19 di Indonesia menggunakan insightmaker

Dosen : Dr.-Ing Ir Setyo Nugroho
Pemodelan Corona_Tugas 3_Yustika Athiya Hasna_04411740000023
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Initial data from:
Italian data [link], as of Mar 28
Incubation estimation [link

Model focuses on outbreak dynamics and control, this version ignores symptom onset to hospital admission and the rest of recovery dynamics.
Italian COVID 19 outbreak control V2
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If no attempt is made to eradicate SARS-CoV-2 it will eventually become endemic, ineradicable, at a high never-ending cost to world in terms of economic growth, human health and lives. The current strategy adopted by most governments is to impose  restrictive measures when the virus threatens to overwhelm hospital services and to relax these restrictions as this danger recedes. This is short-sighted. It cannot eliminate the highly infectious delta variant, which has an estimated R0-value of between 6 & 9. Periodic lockdowns will be hard to avoid in the future.

However, eradication is possible, herd immunity can be achieved quickly worldwide, reducing the R0 permanently to below 1, which will lead to the disappearance of the virus. Critical in achieving this is Ivermectin, a medicine that is cheap,  readily available and can be manufactured by most countries. A recent meta study has shown that Ivermectin, prophylactically employed, can prevent infection with the virus  by 86 % on average – very similar to the efficacy of vaccines. Eradication will require employment of all the instruments shown in the graph: future generations do not have to live with this plague. 

SarsCov 2: Countering its Dynamic
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Tugas 1_Akhdhan Muhammad Muaz_04411740000017_Pemodelan Transportasi Laut

Dosen Pengampu: Dr-Ing Ir. Setyo Nugroho
Pemodelan Virus Covid-19 di Indonesia
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comportamiento de la población Afectada por el Covid-19
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Seminární práce
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This model describes the whole process about government response and economic impact when the covid-19 outbreak in Burnie, Tasmania. When the reported cases increase to a certain level, the government realizes its high risk, then publishes a series of policies to protect the public, such as travel restriction, social distance and quarantine. The economic damage is also severe, especially for tourism and hostility industry and retail industry.

 

Clearly, in the beginning, the number of infected people and death cases increase sharply, but due to government policies and vaccination, it effectively reduces covid-19 cases. For economy, on one hand, the government health policies slow down the pace of growth, on the other hand, the government build vaccine confidence, which leads to more people getting vaccinated, and help the economy back to normal.

Covid-19 outbreak in Burnie Tasmania
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The System Dynamics Of Covid-19 Pandemic at Puerto Princesa City Palawan
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My Insight 1 covid-19
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covid 19 in china 2
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Simulation (SIR) Covid-19
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SIRD COVID-19 Хубэй