Introduction:   This simulation model demonstrates the outbreak of Covid-19 in Burnie, Tasmania and how the corresponding government’s responses affect the spreading of Covid-19. Meanwhile, this model also shows how the economy in Burnie is influenced by the impacts of both Covid-19 and government
Introduction:
This simulation model demonstrates the outbreak of Covid-19 in Burnie, Tasmania and how the corresponding government’s responses affect the spreading of Covid-19. Meanwhile, this model also shows how the economy in Burnie is influenced by the impacts of both Covid-19 and government policies.

Variables: 
This simulation contains some relevant variables as follow:

Variables in Covid-19 outbreaks: (1) Infection rate, (2) Recovery rate, (3) Death rate, (4) Immunity loss rate

Variables in Government policies: (1) Vaccination rate, (2) Lockdown, (3) Travel ban, (4)Quarantine

Variables in Economy: (1) E-commerce business, (2) Unemployment rate, (3) Economic growth rate.

Assumption:
Government responses would be triggered when reported Covid-19 cases are at least 10.

The government policies reduce the spreading of Covid-19, but they would also limit economic development at the same time due to the negative impact of the policies on the economy is greater than the positive impact.

The increase in reported Covid-19 cases would negatively affect economic growth.

Interesting Insights:
The first finding is that the death number would keep increasing even though the infection rate has decreased, but with stronger government policies (such as implementing a coefficient over 25%), no more death numbers will occur caused by Covid-19.

The second finding is that as government policies limit business activities, with the increasing number of reported Covid-19 cases, economic growth will suffer a severe blow even if e-commerce grows, it can’t make up for this economic loss.
Collapse of the economy, not just recession, is now very likely. To give just one possible cause,
in the U.S. the fracking industry is in deep trouble. It is not only that most
fracking companies have never achieved a   free cash flow   (made a profit)
since the fracking boom started in 2008, but th
Collapse of the economy, not just recession, is now very likely. To give just one possible cause, in the U.S. the fracking industry is in deep trouble. It is not only that most fracking companies have never achieved a free cash flow (made a profit) since the fracking boom started in 2008, but that  an already very weak  and unprofitable oil industry cannot cope with extremely low oil prices. The result will be the imminent collapse of the industry. However, when the fracking industry collapses in the US, so will the American economy – and by extension, probably, the rest of the world economy. To grasp a second and far more serious threat it is vital to understand the phenomenon of ‘Global Dimming’. Industrial activity not only produces greenhouse gases, but emits also sulphur dioxide which converts to reflective sulphate aerosols in the atmosphere. Sulphate aerosols act like little mirrors that reflect sunlight back into space, cooling the atmosphere. But when economic activity stops, these aerosols (unlike carbon dioxide) drop out of the atmosphere, adding perhaps as much as 1° C to global average temperatures. This can happen in a very short period time, and when it does mankind will be bereft of any means to mitigate the furious onslaught of an out-of-control and merciless climate. The data and the unrelenting dynamic of the viral pandemic paint bleak picture.  As events unfold in the next few months,  we may discover that it is too late to act,  that our reign on this planet has, indeed,  come to an abrupt end?  
This Model described the outbreak simulation under government policy and impacts on Economics.     Assumptions    The social distance policy can reduce 80% of infection.        Interesting Insights   The story tell the difference when social distance applied or not        Click on View story to star
This Model described the outbreak simulation under government policy and impacts on Economics.

Assumptions 
The social distance policy can reduce 80% of infection.

Interesting Insights
The story tell the difference when social distance applied or not

Click on View story to start simulations

   Introduction    This model simulates the COVID-19 outbreaks in Burnie, the government reactions, as well as the economic impact. The government's strategy is based on the number of COVID-19 cases reported and testing rates and recovered.       Assumptions    In the same trend that government poli
Introduction
This model simulates the COVID-19 outbreaks in Burnie, the government reactions, as well as the economic impact. The government's strategy is based on the number of COVID-19 cases reported and testing rates and recovered.

Assumptions
In the same trend that government policy decreases infection, it also reduces economic growth.
When there are ten or fewer COVID-19 cases reported, government policy is triggered.
The economy suffers as a result of an increase in COVID-19 cases.

Interesting insights
The higher testing rates appear to result in a more quick government response, resulting in fewer infectious cases. However, it has a negative influence on the economy.
Somulacion clase 2, retroalimentación + y - , primer versión
Somulacion clase 2, retroalimentación + y - , primer versión
 Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.  The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

 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

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:
 Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.  The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

 The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune. 

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.   
Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.    Modified by Rio dan Pras
Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.

Modified by Rio dan Pras