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
21 hours ago
Model based on several references: 1. https://insightmaker.com/insight/4iVOp2JcrDSTBvqjER7pxM/TA-Pemsim-SEIR-Covid-19-Model 2. https://insightmaker.com/insight/5GiU0WZLpKCLGOoe6xeIhT/SEIR-COVID-19-New-Kl-1 3. https://insightmaker.com/insight/DaOeZ0N9RcgU1Q87ofIj8/COVID-19-SEIR-Model-for-Indonesia  L
Model based on several references:
1. https://insightmaker.com/insight/4iVOp2JcrDSTBvqjER7pxM/TA-Pemsim-SEIR-Covid-19-Model
2. https://insightmaker.com/insight/5GiU0WZLpKCLGOoe6xeIhT/SEIR-COVID-19-New-Kl-1
3. https://insightmaker.com/insight/DaOeZ0N9RcgU1Q87ofIj8/COVID-19-SEIR-Model-for-Indonesia

Locus set on Indonesia, during 2021
  LEIA ANTES DE COMEÇAR   Milhões de pessoas ao redor do mundo estão em QUARENTENA em função da pandemia COVID-19. Se adaptar à quarentena pode ser um PROBLEMA para muitas pessoas.   Nosso DESAFIO é construir um DIAGRAMA CAUSAL que analise este PROBLEMA que é ficar em quarentena. Vamos lá!?       PR

LEIA ANTES DE COMEÇAR

Milhões de pessoas ao redor do mundo estão em QUARENTENA em função da pandemia COVID-19. Se adaptar à quarentena pode ser um PROBLEMA para muitas pessoas.

Nosso DESAFIO é construir um DIAGRAMA CAUSAL que analise este PROBLEMA que é ficar em quarentena. Vamos lá!?


PRIMEIRA TAREFA (até dia 13 de maio)

1) Qual a variável CHAVE que você acha que pode definir o problema? Crie uma VARIÁVEL dentro do folder CHAVE.

2) Quais as outras variáveis SECUNDÁRIAS que estão relacionadas com este problema? Crie variáveis secundárias dentro dos FOLDER que melhor identifica o tipo da variável.


SEGUNDA TAREFA

No dia 15 de maio discutiremos virtualmente no Zoom, as variáveis propostas e faremos um DIAGRAMA CAUSAL RASCUNHO.


TERCEIRA TAREFA

No dia 22 de maio discutiremos virtualmente Zoom, o DIAGRAMA CAUSAL RASCUNHO objetivando construir o DIAGRAMA CAUSAL DEFINITIVO.

Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
COVID-19 Outbreak in Burnie Tasmania Simulation Model    Introduction:     This model simulates the COVID-19 outbreak situation in Burnie and how the government responses impact local economy. The COVID-19 pandemic spread is influenced by several factors including infection rate, recovery rate, deat
COVID-19 Outbreak in Burnie Tasmania Simulation Model

Introduction:

This model simulates the COVID-19 outbreak situation in Burnie and how the government responses impact local economy. The COVID-19 pandemic spread is influenced by several factors including infection rate, recovery rate, death rate and government's intervention policies.Government's policies reduce the infection spread and also impact economic activities in Burnie, especially its tourism and local businesses.   

Assumptions: 

- This model was built based on different rates, including infection rate, recovery rate, death rate, testing rate and economic growth rate. There can be difference between 
this model and reality.

- This model considers tourism and local business are the main industries influencing local economy in Burnie.

- Government's intervention policies will positive influence on local COVID-19 spread but also negative impact on local economic activity.

- When there are more than 10 COVID-19 cases confirmed, the government policies will be triggered, which will brings effects both restricting the virus spread and reducing local economic growth.

- Greater COVID-19 cases will negatively influence local economic activities.

Interesting Insights:

Government's vaccination policy will make a important difference on restricting the infection spread. When vaccination rate increase, the number of deaths, infected people and susceptible people all decrease. This may show the importance of the role of government's vaccination policy.

When confirmed cases is more than 10, government's intervention policies are effective on reducing the infections, meanwhile local economic activities will be reduced.

   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.
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