Insight diagram

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 parameters: Infection rate, Background disease, recovery rate.

Adjusted parameter: 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.

Untitled Insight
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This model is developed to simulate how Burnie can deal with a new outbreak of COVID-19 considering health and economic outcomes. The time limit of the simulation is 100 days when a stable circumstance is reached. 

Stocks
There are four stocks involved in this model. Susceptible represents the number of people that potentially could be infected. Infected refers to the number of people infected at the moment. Recovered means the number of people that has been cured, but it could turn into susceptible given a specific period of time since the immunity does not seem everlasting. Death case refers to the total number of death since the beginning of outbreak. The sum of these four stocks add up to the initial population of the town.

Variables
There are four variables in grey colour that indicate rates or factors of infection, recovery, death or economic outcomes. They usually cannot be accurately identified until it happen, therefore they can be modified by the user to adjust for a better simulation outcome.

Immunity loss rate seems to be less relevant in this case because it is usually unsure and varies for individuals, therefore it is fixed in this model.

The most interesting variable in green represents the government policy, which in this situation should be shifting the financial resources to medical resources to control infection rate, reduce death rate and increase recovery rate. It is limited from 0 to 0.8 since a government cannot shift all of the resources. Bigger scale means more resources are shifted. The change of government policy will be well reflected in the economic outcome, users are encouraged to adjust it to see the change.

The economic outcome is the variable that roughly reflects the daily income of the whole town, which cannot be accurate but it does indicate the trend.

Assumptions:
The recovery of the infected won't happen until 30 days later since it is usually a long process. And the start of death will be delayed for 14 days considering the characteristic of the virus.
Economic outcome will be affected by the number of infected since the infected cannot normally perform financial activities.
Immunity effect is fixed at 30 days after recovery.

Interesting Insights:
 In this model it is not hard to find that extreme government policy does not result in the best economic outcome, but the values in-between around 0.5 seems to reach the best financial outcome while the health issues are not compromised. That is why usually the government need to balance health and economic according to the circumstance. 
 

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This model simulates a COVID outbreak occurring at Burnie, Tasmania. It links the extent to the pandemic with governments intervention policies aiming to limit the spread of the virus. The other part of the model illustrates how will the COVID statistics and the government enforcement jointly influence the economic environment in the community. A number of variables are taken into account, indicating positive or negative relationship in the infection and the economy model respectively.

 

Assumptions

·         Government takes responsive actions when the number of acquired cases exceeds 10.

·         Government’s prompt actions, involving closure of the state border, lockdown within the city, plans on mandatory vaccination and testing, effectively control the infection status.

·         Economic activities are reduced due to stagnation in statewide tourism, closure of brick-and-mortar businesses, and increased unemployment rate, as results of government restrictions.

 

Insights

Government’s rapid intervention can effectively reduce the infected cases. The national vaccination rollout campaign raises vaccination rate in Australians, and particularly influence the death rate in the infection model. Please drag the slider of vaccination to a higher rate and run the model to compare the outcomes.

Although local economy is negatively affected by government restriction policies, consumer demand in online shopping and government support payments neutralize the negative impact on economy and maintain the level of economic activities when infections get controlled. 

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