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The System Dynamics Model presents the the COVID-19 status in Сhina
Жангир Шаханов Covid-19 in china
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Explanation of the Model

This is a sample model of Covid-19 outbreak in Burnie, Tasmania showing how the Government responds by implementing relevant health policy and the effects on the Economy of the area. 
 
Assumptions

Economic growth rate is dependent on the proportion of the population who can be exposed. Number of COVID cases negatively impacts the economy. Govt policy is triggered when COVID-19 cases are 10 or more.

Interesting Insights

1) Exposure to the disease has a positive relationship with economic growth rate because the more people goes out, more business activity takes place, resulting in Economic Growth.

2) Increasing the Testing rate results in:

- Higher cases being detected

- Stricter Govt Policy

- Less Deaths


 


Covid-19 outbreak in Burnie Tasmania
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Otu_COVID-19_CV
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Системная динамика COVID-19 в Казахстане в 2020 году
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Өзіндік жұмыс (3-бөлімге)
Өзіндік жұмыс Агент
24 4 months ago
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Covid-19 model
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Simulation of how a virus infects after entering the body, how it replicates inside living cells, and how the body's immune system responds towards the virus
System Dynamic Model 1b (Previously-infected individual)
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Agent based Modeling Simulation for Pandemic COVID-19 Disease
Covid-19(ABM)_VHK
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This Model was developed from the SEIR model (Susceptible, Enposed, Infected, Recovered). It was designed to explore relationships between the government policies regarding the COVID-19 and its impact upon the economy as well as well-being of residents. 

Assumptions:

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


Government Policies affect the economy and the COV-19 infection negatively at the same time;


Government Policies can be divided as 4 categories, which are Social Distancing, Business Restrictions, Lock Down, Travel Ban, and Hygiene Level, and they represented strength of different aspects;

 

Parameters:

Policies like Social Distancing, Business Restrictions, Lock Down, Travel Ban all have different weights and caps, and they add up to 1 in total;

 

There are 4 cases on March 9th; 

Ro= 5.7  Ro is the reproduction number, here it means one person with COVID-19 can potentially transmit the coronavirus to 5 to 6 people;


Interesting Insights:

Economy will grow at the beginning few weeks then becoming stagnant for a very long time;

Exposed people are significant, which requires early policies intervention such as social distancing.

Model of COVID-19 Outbreak in Burnie, Tasmania
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The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
Ph_Covid19SDM_AngelKateCacayan
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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]



Resources:
  * https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported
Final Version of Italian COVID-19 outbreak
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AGENT-BASED MODEL OF COVID-19
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This model is to show the status of numbers of infected people, recovered people and deaths during COVID-19 in Burnie Australia. It also shows impact on the growth of economy. 

Variables
The infection rate and the percentage of people washing their hands are influencing the infected number of people. Also, there are death rate and recovery rate and immunity lost rate determining the numbers of deaths, recovered and infected-again people.  
for the economy growth, there are several factors, including unemployment rate, infection rate, economic growth rate and government health policy. 

Perspective
After some time, people will recovered, also the economic activities. 
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