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 Blutd
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



 The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022.         Total population of Brgy. Sicsican - 22625    Total Covid19 cases as of May 27, 2022 - 250    Local transmission - 241    Imported transmission - 9    Recovery - 226    Death Due
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
 Introduction:  This model demonstrates the COVID-19 outbreak in Burnie, Tasmania. It shows how the government policy tries to reduce the spread of COVID-19 whilst also impacting the local economy.      Assumptions:   This model has four variables that influence the number of COVID-19 cases: infecti
Introduction:
This model demonstrates the COVID-19 outbreak in Burnie, Tasmania. It shows how the government policy tries to reduce the spread of COVID-19 whilst also impacting the local economy.

Assumptions:
This model has four variables that influence the number of COVID-19 cases: infection rate, immunity loss rate, recovery rate and death rate.

In order to reduce the pandemic spread, in this model, assume the government released six policies when Burnie COVID-19 cases are equal or over 10 cases. Policies are vaccination promotion, travel restriction to Burnie, quarantine, social distance, lockdown and testing rate.

Government policies would reduce the pandemic. However, it decreases economic growth at the same time. In this model, only list three variable that influence local economic activities. 
Travel restrictions and quarantine will reduce Burnie tourism and decrease the local economy. On the other hand, quarantine, social distance, lockdown allow people to stay at home, increasing E-commerce business.
As a result, policies that cause fewer COVID-19 cases also cause more considerable negative damage to the economy.

Interesting insights:
One of the interesting findings is that the government policy would reduce the COVID-19 spread significantly if I adjust the total government policies are over 20% (vaccine promotion, travel restriction, quarantine, social distance, lockdown), 3560 people will die, then no more people get COVID-19.
However, if I change the total government policy to less than 5%, the whole Burnie people will die according to the model. Therefore, we need to follow the polices, which saves our lives.
 This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021     Insight Author: Pia Mae M. Palay
This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay
 Modelo epidemiológico simples   SIR: Susceptíveis - Infectados - Recuperados         Clique aqui  para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.          Dados iniciais de  infec
Modelo epidemiológico simples
SIR: Susceptíveis - Infectados - Recuperados

Clique aqui para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.


Dados iniciais de infectados, recuperados e óbitos para diversos países (incluindo o Brasil) podem ser obtidos aqui neste site.
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
8 months ago
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
This model shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.    It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10.      Government interventi
This model shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.

It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10. 

Government intervention - lock down the state, suppress the development of COVID-19 effectively. It is related to most of people stay at home to reduce the exposure in public area.
On the other hand, it also bring the economy of Burnie in the recession, as no tourists, no dining out activities and decrease in money spending in the city.
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
 Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

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

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?  
 SARS-CoV-19 spread  in different countries - please  adjust variables accordingly        Italy     elderly population (>65): 0.228  estimated undetected cases factor: 4-11  starting population size: 60 000 000  high blood pressure: 0.32 (gbe-bund)  heart disease: 0.04 (statista)        Germany
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 65 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly)
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating)
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again)

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
 SARS-CoV-19 spread  in different countries - please  adjust variables accordingly        Italy     elderly population (>65): 0.228  estimated undetected cases factor: 4-11  starting population size: 60 000 000  high blood pressure: 0.32 (gbe-bund)  heart disease: 0.04 (statista)  free intensive
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account. The susceptible people are the local Burnie residents. If residents were infected, they would either recovered or dead. However, even they do r
The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account.
The susceptible people are the local Burnie residents. If residents were infected, they would either recovered or dead. However, even they do recover, there is a chance that they will get infected again if immunity loss occurs.
From the simulation result we can see that with the implementation of local government policies including travel ban and social distancing,  the number of infected people will decrease. The number of recovered people will increase in the first 5 weeks but then experience a decrease.
In addition, with the implementation of local government policy, the economic environment in Burnie will be relatively stable when the number of COVID-19 cases is stable.
 SARS-CoV-19 spread  in different countries - please  adjust variables accordingly        Italy     elderly population (>65): 0.228  estimated undetected cases factor: 4-11  starting population size: 60 000 000  high blood pressure: 0.32 (gbe-bund)  heart disease: 0.04 (statista)  free intensive
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
 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 w

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. 

 

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 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

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.