This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.

This model is based on the SIR, Susceptible, Infection, Recovery (or Removed) These are the three possible states related to the members of the Burnie population when a contagious decease spreads.

The Government/Government Health Policy, played a big part in the successful decrease in Covid-19 infections. The Government enforced the following.
- No travel (interstate or international)
- Isolation within the residents homes
- Social distancing by 1.5m
- Quarantine
- Non essential companies to be temporarily closed
- Limitations on public gatherings
- And limits on time and kilometers aloud to travel from ones home within a local community

This resulted in lower reported infection rates of Covid-19 and higher recovery rates.

In my opinion: When the first case was reported the Government could have been even faster to enforce these rules to decrease the fatality rates further for the Burnie, population.  

Assumption: Government policies were only triggered when 10 cases were recorded.
Also, more cases that had been recorded effected the economic growth during this time.

Interesting Findings: In the simulation it shows as the death rates increases towards the end of the week, the rate of testing goes down. You would think that the government would have enforced a higher testing rate over the duration of this time to decrease the number of infections, exposed which would increase the recovery rates faster and more efficiently.  

Figures have been determined by the population of Burnie being 19,380 at the time of assignment.

Overview of Part E Ch 20 to 24 of Mitchell Wray and Watts Textbook see IM-164967 for book overview

Map of Geoffrey M Hodgson's 2015 Conceptualizing Capitalism book summary pdf  with other ideologies added sept 2021 from new politics website

WIP of Rammelt's 2019 System Dynamics Review Article which has STELLA and Minsky software versions as supplements. Compare with the older IM-2011 version

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

Circular equations WIP for Runy.

Summary WIP of Thomas Palley's 2012 Book

A Model for COVID-19 outbreak

This Insight is used for simulating growth of a company with specified parameters.

Extended from Im-628 Supply and demand by adding control folder.

This is Figure 6 from Lancastle, N. (2012) 'Circuit Theory Extended: The Role of Speculation in Crises' based on Keen, S. (2010). Solving the Paradox of Monetary Profits.

I have tried to capture the unemployment benefits budget in a causal loop diagram. You can make this as extensive as you want, but I have tried to focus on how unemployment benefits are financed and on the main determinants of expenditures and income. I was not (yet) able to 'close te loop' - to build the diagram up from feedback cycles. 
The diagram is in Dutch.

Book Summary of The Great Transformation by Karl Polanyi see Wikipedia . See also more Karl Polanyi ideas IM-181325

This model is made to illustrate the components that impact profitability on a basic level. 

Clone of Wagdy Samir Macroeconomics work in progress IM-901 Additions and deletions based on Robert Skidelsky's description of Keynes general THeory from his Biography Vol2 p 549 -571

Social determinants of health are economic and social conditions that influence the health of people and communities. These conditions are shaped by the amount of money, power, and resources that people have, all of which are influenced by policy choices. Social determinants of health affect factors that are related to health outcomes. Factors related to health outcomes include:

• How a person develops during the first few years of life (early childhood development)
• How much education a persons obtains
• Being able to get and keep a job
• What kind of work a person does
• Having food or being able to get food (food security)
• Having access to health services and the quality of those services
• Housing status
• How much money a person earns
• Discrimination and social support

Model description:

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania. It also tell us the impact of economic policies on outbreak models and economic growth.

 

Variables:

The simulation takes into account the following variables and its adjusting range: 

 

On the left of the model, the variables are: infection rate( from 0 to 0.25), recovery rate( from 0 to 1), death rate( from 0 to 1), immunity loss rate( from 0 to 1), test rate ( from 0 to 1), which are related to Covid-19.

 

In the middle of the model, the variables are: social distancing( from 0 to 0.018), lock down( from 0 to 0.015), quarantine( from 0 to 0.015), vaccination promotion( from 0 to 0.019), border restriction( from 0 to 0.03), which are related to governmental policies.

 

On the right of the model, the variables are: economic growth rate( from 0 to 0.3), which are related to economic growth.

 

Assumptions:

(1) The model is influenced by various variables and can produce different results. The following values based on the estimation, which differ from actual values in reality.

 

(2) Here are just five government policies that have had an impact on infection rates in epidemic models. On the other hand, these policies will also have an impact on economic growth, which may be positive or negative.

 

(3) Governmental policy will only be applied when reported cases are 10 or more. 

 

(4) This model lists two typical economic activities, namely e-commerce and physical stores. Government policies affect these two types of economic activity separately. They together with economic growth rate have an impact on economic growth.

 

Enlightening insights:

(1) In the first two weeks, the number of susceptible people will be significantly reduced due to the high infection rate, and low recovery rate as well as government policies. The number of susceptible people fall slightly two weeks later. Almost all declines have a fluctuating downward trend.

 

(2) Government policies have clearly controlled the number of deaths, suspected cases and COVID-19 cases.

 

(3) The government's restrictive policies had a negative impact on economic growth, but e-commerce economy, physical stores and economic growth rate all played a positive role in economic growth, which enabled the economy to stay in a relatively stable state during the epidemic.

Adam Smith's The Invisible Hand: The Feedback Structure of Markets. From Sterman JD Business Dynamics p170 Fig 5-26. A price-mediated resource allocation system..