The System Dynamics Model presents the the COVID-19 status in Сhina
The System Dynamics Model presents the the COVID-19 status in Сhina
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.
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
Agent based Modeling Simulation for Pandemic COVID-19 Disease
Agent based Modeling Simulation for Pandemic COVID-19 Disease
 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.
 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
Simulasi ini digunakan untuk memodelkan persebaran virus corona di Indonesia untuk kepentingan tugas kuliah
Simulasi ini digunakan untuk memodelkan persebaran virus corona di Indonesia untuk kepentingan tugas kuliah
Tugas Pemodelan Transportasi Laut    Memodelkan persebaran pandemik covid-19 menggunakan insightmaker     Dosen pembimbing : Dr-Ing Ir. Setyo Nugroho
Tugas Pemodelan Transportasi Laut

Memodelkan persebaran pandemik covid-19 menggunakan insightmaker

Dosen pembimbing : Dr-Ing Ir. Setyo Nugroho