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Description:

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.

Assumptions:

The government policy is triggered when the number of infected is more than ten.

The government policies will take a negative effect on Covid-19 outbreaks and the financial system.

Parameters:

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameter: Background disease.

Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be changed from vaccination rate.

Government policy's parameters

Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)

Economic's parameters

Fixed parameter: Tourism

Adjusted parameter: Economic growth rate(from 0.3 to 0.5)

Interesting insight

An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.

BMA708_Assignment 3_Nguyen Dang Khoa Vo_520272_COVID-19 outbreak and Burnie economy
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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 

ECM-Training - SEIR Infectious Disease Model for COVID-19
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This is a mitigated model showing the potential spread of COVID-19 across the healthcare system.

COVID phased community model
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агент мансұр
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Model of Covid-19 Outbreak in Burnie, Tasmania

When reported COVID-19 cases begin to show a rapid increase, the government will initiate control policies to deal with the spread.As the number of people tested increases and measures such as isolation and medical assistance are implemented, the number of people infected will decline rapidly.Therefore, the government's policy is to reduce and eliminate sources of transmission by increasing the number of tests and initiating control measures.At the same time, it also shows the negative impact of economic growth, which according to the model will stop in the next 20 weeks.

Model of Covid-19 Outbreak in Burnie, Tasmania (Yimeng Yao 448253)
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ABM COVID-19
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Introduction;

This model shows COVID-19 outbreak in Burnie have some impact for local economy situation and government policy. The main government policy is lockdown during the spreading period which can help reduce the infected rate, and also increase the test scale to help susceptible confirm their situation.


Variables;

Infection rate, Death rate, Recovery rate, test rate, susceptible, immunity rate, economy growth rate

These variables are influenced by different situation.


When cases over 10, government will implement lockdown policy.


Conclusion;

When cases increase too much , they will influence the economic situation.


Interesting insights:

If the recover rate is higher, more people will recover from the disease. It seems to be a positive sign. However, it would lead to a higher number of recovered people and more susceptible. As a result, there would be more cases, and would have a negative impact on the economic growth. 

Model of COVID-19 Outbreak in Burnie, Tamania ( WANTING BAO, 536865)
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The Infection / Recovery & Immune / Rate of a Global a Population of 8.2 Billion People.
The Pandemic Management of COVID-19 - Richard A. Estefan
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Data provided by: PHE and Worldometers

UK COVID 19 Simulator
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covid-19 in France
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SD MODEL COVID-19
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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
SARS-CoV-19 model