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 Develop a basic Systemigram / Rich Picture to tell the story of covid 19 mitigation 
Systemigram Covid-19
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The complex model reflects the COVID-19 outbreak in Burnie, Tasmania. The model explains how the COVID-19 outbreak will influence the government policies and economic impacts. The infected population will be based on how many susceptible, infected, and recovered individuals in Burnie. It influences the probability of infected population meeting with susceptible individuals.

The fatality rate will be influenced by the elderly population and pre-existing medical conditions. Even though individuals can recover from COVID-19 disease, some of them will have immunity loss and become part of the susceptible individuals, or they will be diagnosed with long term illnesses (mental and physical). Thus, these variables influence the number of confirmed cases in Burnie and the implementation of government policies.

The government policies depend on the confirmed COVID-19 cases. The government policies include business restrictions, lock down, vaccination and testing rate. These variables have negative impacts on the infection of COVID-19 disease. However, these policies have some negative effects on commercial industry and positive effects on e-commerce and medical industry. These businesses growth rate can influence the economic growth of Burnie with the economic

Most of the variables are adjustable with the slider provided below. They can be adjusted from 0 to 1, which illustrates the percentages associated with the specific variables. They can also be adjusted to three decimal points, i.e., from 0.1 to 0.001.


Assumptions

- The maximum population of Burnie is 20000.
- The maximum number of infected individuals is 100.
- Government policies are triggered when the COVID-19 cases reach 10 or above.
- The government policies include business restrictions, lock down, vaccination and testing rates only. Other policies are not being considered under this model.
- The vaccination policy implemented by the government is compulsory.
- The testing rate is set by the government. The slider should not be changed unless the testing rate is adjusted by the government.
- The fatality rate is influenced by the elderly population and pre-existing medical conditions only. Other factors are not being considered under this model.
- People who recovered from COVID-19 disease will definitely suffer form immunity loss or any other long term illnesses.
- Long term illnesses include mental illnesses and physical illnesses only. Other illnesses are not being considered under this model.
- Economic activities are provided with an assumption value of 1000.
- The higher the number of COVID-19 cases, the more negative impact they have on the economy of Burnie. 


Interesting Insights

A higher recovery rate can decrease the number of COVID-19 cases as well as the probability of infected population meeting with susceptible persons, but it takes longer for the economy to recover compared to a lower recovery rate. A higher recovery rate can generate a larger number of people diagnosed with long term illnesses.

Testing rate triggers multiple variables, such as government policies, positive cases, susceptible and infected individuals. A lower testing rate can decrease the COVID-19 confirmed cases, but it can increase the number of susceptible people. And a higher testing rate can trigger the implementation of government policies, thus decreasing the infection rate. As the testing rate has a strong correlation with the government policies, it can also influence the economy of Burnie. 

BMA708 COVID-19 Outbreak in Burnie, Tasmania
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Covid-19 pandemi
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Santa Maria Covid-19
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
SEIR Model for COVID-19 in Indonesia
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This model aims to show that how Tasmania government's Covid-19 policy can address the spread of the pandemic and in what way these policy can damage the economy.

This model assumes that if the COVID-19 cases are more than 10, the government will take action such as quarantine and lockdown at the area. These policy can indirectly affect the local economy in many different way. At the same time, strict policy may be essential for combating Covid-19.

From the simulation of the model, we can clearly see that the economy of Burine will be steady increase when government successfully reduces the COVID-19 cased and make it spreading slower.

Interesting finding: In this pandemic, the testing rate and the recovery rate are important to stop Covid-19 spreading. Once the cases of Covid-19 less than 10, the government might stop intervention and the economy of Burnie will back to normal.

Model of Covid-19 outbreaks at Burnie (Yingchao Yang,503757)
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SEIR COVID-19 New Kl. 1
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A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy, though, of higher detected cases is negative. 




Burnie COVID-19 outbreak demo model version 2
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Systemigram Model COVID-19
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The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
SEIR - COVID-19 (v.1)
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Simple epidemiological model for Burnie, Tasmania
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts  

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model,  will stop for next 52 weeks).
Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)
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COVID-19 SEIR Model (Indonesia values)
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COVID-19 Pandemic Systemigram
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Model ini dirancang untuk membuat model tentang penyebaran Covid-19 dan vaksinasi di Kabupaten Sleman pada November 2022

Model ini dibuat untuk memenuhi tugas kelompok dari matakuliah Metode Penyelesaian Masalah dan Pemodelan, atas nama :
Sabilla Halimatus Mahmud
Nurul Widyastuti
Muhammad Najib



Edit Model Penyebaran Covid-19 di Kabupaten Sleman
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

SEIR Infectious Disease Model for COVID-19
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A simple SI (Susceptible-Infectious) model that captures the dynamics of COVID-19.
SI Model
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spread model of COVID-19
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Агентное моделирование по COVID-19
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Evolution of Covid-19 in Brazil:
A System Dynamics Approach

Villela, Paulo (2020)
villela.paulo@gmail.com

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
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Germany COVID-19
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fatality (death rate)= 2.5%
(as of November in U.S.; source oneworldindata.org)

reinfection rate- unknown, rare
(as of October; source:https://www.ajmc.com/view/first-case-of-covid-19-reinfection-detected-in-the-us)
Covid-19 model 2
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COVID-19 Kazakstan Abdrakhman