Based on this particular model created by Lutfi Andriyanto and Aulia Nur Fajriyah: https://insightmaker.com/insight/2wxxIeiWJsHNFGNH6cf6ke/SEIR     Updated by (Kelompok 2):  Daffa Muhammad Romero	20/456363/TK/50493  Iskan Mustamir			20/456367/TK/50497  Tasya Nafisah Kamal		20/460569/TK/51158  Hervi

Based on this particular model created by Lutfi Andriyanto and Aulia Nur Fajriyah: https://insightmaker.com/insight/2wxxIeiWJsHNFGNH6cf6ke/SEIR


Updated by (Kelompok 2):

Daffa Muhammad Romero 20/456363/TK/50493

Iskan Mustamir 20/456367/TK/50497

Tasya Nafisah Kamal 20/460569/TK/51158

Hervi Nur Rahmadien 20/463601/TK/51593

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.
Creating a basic simulation model on Covid-19
Creating a basic simulation model on Covid-19
   Evolution of Covid-19 in Brazil:  
  A System Dynamics Approach  
 Villela, Paulo (2020) paulo.villela@engenharia.ufjf.br  This model is based on  Crokidakis, Nuno . (2020).  Data analysis and modeling of the evolution of COVID-19 in Brazil . For more details see full paper  here .
Evolution of Covid-19 in Brazil:
A System Dynamics Approach

Villela, Paulo (2020)
paulo.villela@engenharia.ufjf.br

This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.

From Yuan Tian's 2024  paper  Early
COVID-19 Pandemic Preparedness: Informing Public Health Interventions and
Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling and   PhD Dissertation 2025  USask Fig 5.1 p96 
From Yuan Tian's 2024 paper Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling and  PhD Dissertation 2025 USask Fig 5.1 p96 
2 months ago
A simple Susceptible - Infected - Recovered disease model.
A simple Susceptible - Infected - Recovered disease model.
The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.
The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.
 Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.  We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.  The initial parametrization is based on the su

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

We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.

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

The questions that we want to answer in this kind of models are not the shape of the curves, that are almost known from the beginning, but, when this happens, and the amplitude of the shapes. This is crucial, since in the current circumstance implies the collapse of certain resources, not only healthcare.

The validation process hence becomes critical, and allows to estimate the different parameters of the model from the data we obtain. This simulation approach allows to obtain somethings that is crucial to make decisions, the causality. We can infer this from the assumptions that are implicit on the model, and from it we can make decisions to improve the system behavior.

Yes, simulation works with causality and Flows diagrams is one of the techniques we have to draw it graphically, but is not the only one. On https://sdlps.com/projects/documentation/1009 you can review soon the same model but represented in Specification and Description Language.

 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



This model shows an SIR model of COVID-19 infection in the Philippines. The data used in this model are recent data from COVID-19 statistics reports this 2022. The format of this Philippine COVID-19 model is guided by an Infection Model developed by martin.
This model shows an SIR model of COVID-19 infection in the Philippines. The data used in this model are recent data from COVID-19 statistics reports this 2022. The format of this Philippine COVID-19 model is guided by an Infection Model developed by martin.