Insight diagram
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
Clone of SARS-CoV-19 model
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This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay
Clone of System Dynamic Model of COVID 19 in Puerto Princesa City
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Modelo epidemiológico simples
SIR: Susceptíveis - Infectados - Recuperados

Dados iniciais do Brasil em 04 Abr 2020
Fonte:
https://www.worldometers.info/coronavirus/country/brazil/
Clone of Modelo SIR simples - Covid-19
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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.
Model of COVID-19 outbreak in Burnie Tasmania - Xiaoqing Ren 525418
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
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.
Clone of Modelo SIR simples - Covid 19
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The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
Clone of Ph_Covid19SDM_AngelKateCacayan
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Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

BackUp of Infectious Disease Model (Version 4.0)
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COVID-19 SEIR Model for Indonesia
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.

This model is upgraded on the basis of the SIR model and adds more variables.

A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy

Government policy will help control the growth of COVID-19 cases by getting people tested.


BMA708 Model of COVID-19 Outbreak in Burnie island. Ming Liu 501335
Insight diagram
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Infectious Disease Model V1.0
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This is the first in a series of models that explore the dynamics of and policy impacts on infectious diseases. This basic  model divides the population into three categories -- Susceptible (S), Infectious (I) and Recovered (R).  

Press the simulate button to run the model and see what happens at different values of the Reproduction Number (R0).

The second model that includes a simple test and isolate policy can be found here.
Future Learn Basic SIR Model
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Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 4.0)
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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?  
Clone of Covid 19 - irreversible and catastrophic consequences
Insight diagram

Introduction:

This model demonstrates the COVID-19 outbreak in Bernie, Tasmania, and shows the relationship between coVID-19 outbreaks, government policy and the local economy. The spread of pandemics is influenced by many factors, such as infection rates, mortality rates, recovery rates and government policies. Although government policy has brought the Covid-19 outbreak under control, it has had a negative impact on the financial system, and the increase in COVID-19 cases has had a negative impact on economic growth.

 

Assumptions:

The model is based on different infection rates, including infection rate, mortality rate, detection rate and recovery rate. There is a difference between a real case and a model. Since the model setup will only be initiated when 10 cases are reported, the impact on infection rates and economic growth will be reduced.

 

Interesting insights:

Even as infection rates fall, mortality rates continue to rise. However, the rise in testing rates and government health policies contribute to the stability of mortality. The model thinks that COVID-19 has a negative impact on offline industry and has a positive impact on online industry.

Model of COVID-19 outbreak in Burnie, Tasmania
Insight diagram
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.
Clone of Burnie COVID-19 outbreaks and economic impacts_Pui Chu Daisy Cheung 524767
Insight diagram
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 4.0)
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SEIRSD MODEL
3 months ago
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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.

Clone of Model of COVID-19 Outbreak in Burnie, Tasmania
Insight diagram
3бөлім өзіндік
Insight diagram

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.

Clone of Model of COVID-19 Outbreak in Burnie, Tasmania
Insight diagram
Украина короновирус, 2021 жыл
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Tugas Permo
Tugas Permo_Corona