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
This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
 Introduction:  This model aims to show that how the Tasmania government's COVID-19 policy can address the spread of the pandemic and in what way these policies can damage the economy.        Assumption:    Variables such as infection rate, death rate and the recovery rate are influenced by the actu
Introduction:
This model aims to show that how the Tasmania government's COVID-19 policy can address the spread of the pandemic and in what way these policies can damage the economy.

Assumption:
Variables such as infection rate, death rate and the recovery rate are influenced by the actual situation.
The government will implement stricter travel bans and social distant policies as there are more cases.
Government policies reduce infection and limit economic growth at the same time.
A greater number of COVID-19 cases has a negative effect on the economy.

Interesting insights:
A higher testing rate will make the infection increase and the infection rate will slightly increase as well. 
Government policies are effective to lower the infection, however, they will damage the local economy. While the higher number of COVID-19 cases also influences economic activities.
  Explanation:    Explanation:  This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be c
Explanation:
Explanation:
This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be concluded as Susceptible, Infection and Recovery, which are used as the main data in this model. However, the improvement of decreasing of reported infection rates of this infectious disease and increasing of recovery rates are contributed by the implementation of the Government Health Policy. 

Assumption
The decrease of both infection rate and economic growth are all influenced by the Government Health Policy simultaneously. The Government Health Policy is only triggered when there are 10 cases reported. However, the increase in reporting COVID-19 cases affects economic growth negatively. 

Interesting Insights:
There are two interesting insights that have been revealed from the simulation. First, the death rate continuously increased even though the infection rate goes down. However, the increase in testing rates contributed to the stability of the death rate towards the end of the week. Moreover, higher testing rates also trigger faster government intervention, which can reduce infectious cases.  Second, as the Government Health Policy limited the chance of going out and shopping, the economic growth is negative due to the higher cases. 

This diagram will map out the spread of the Coronavirus (SAR-CoV-2) and its complexities of health care.
This diagram will map out the spread of the Coronavirus (SAR-CoV-2) and its complexities of health care.
 Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

The System Dynamics Model presents the the COVID-19 status in Сhina
The System Dynamics Model presents the the COVID-19 status in Сhina