The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account.

Cutbacks can have a counterintuitive effect. The government knows precisely how much it custs in spending. However, it cannot know the extent to which tax revenues shrink in a non-linear complex economic system as the economy contracts. In addition, the treasury has to spend more as automatic stabilizers activate and payments are made to an increasing number of unemployed workers. The effect of this is that initially the deficit shrinks, but later it rises as tax revenues fall short of expectations and more spending takes place. The ironic part is that often the very indicator that promted austerity measurs, the defcit to GDP ratio, becomes worse than it was at the outset. We could observe this in Spain and Portugal where planned deficits have been repeatedly missed, as austerity measures  (fiscal cutbacks) were introduced to deal with the effects of  the 2008 financial crisis.

Causal loop structure generated by Gene Bellinger's Gemini scripts from Steve Keen's Jan 2026 substack posting Equilibrium (is) for Dummies

Regulation of resource allocation to production in response to inventory adequacy and delivery delay. A non-price-mediated resource allocation system. From Sterman JD Business Dynamics p172 Fig 5-27

WIP Ideas from Science Special Issue May 2014

WIP SD REpresentation of Steve Keen's BOMD Minsky model (described in Fig.5 of his patreon Jan2021 Draft New Economics Manifesto) to hope to make the causal structure clearer

A simple budget planning system.  What additional complexities can you add?

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

Book summary of Albert O Hirschman's 1982 book, explaining cycles of collective public action.

From Warren C. Sanderson in Population - Development - Environment, Wolfgang Lutz (Ed.), 1994, Springer.

Spending by the government creates its own 'financial resource' as the process of crediting an account in the private sector takes place. This may sound like nonsense, but in fact it is 'monetary reality'. This premise is supported by Bell (1998; 2000) and Wray (1998a) who argue that the Treasury does not need to collect or borrow funds in order to spend, but crates new funds as it spends.

Perhaps the following thought experiment  helps to understand how this is possible.  

If you imagine two drawers, each representing an account. The first drawer contains 100 gold coins and the second is empty. Also imagine that there are no other gold coins available at this time. Let's call the first drawer account A and the second account B. Now if you want to transfer 30 gold coins from account A to account B, you would actually first have to take the coins out of drawer A and then place them into drawer B. Account A will then necessarily have 30 coins less in it. Now imagine accounts A and B are held in a computer as electronic money. Instead of 100 gold coins, account A only contains the computer generated number '100'  and account B shows '0'. To get account B to show a balance of '30', it would now simple be necessary to change the '0' to '30' on the computer. The need to raid account A and to take '30' from the number '100' before you could credit  account B does not exist. Money is created as it is entered in B's account irrespective of whether A's account is debited before or after this process or not at

In this Insight I focus on the demand site of the Market and Price model, leaving the supply side out.

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.

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.

Simulation of MTBF with controls


The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
Useful Life

Wearout

Based on G.P. Cimellaro et al. Framework for analytical quantification of disaster resilience Engineering Structures 32 (2010) 3639–3649 paper

Summary WIP of Thomas Palley's 2012 Book