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.

WIP based on Bill mitchell's blogs. 

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

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

Any activity  requires the use of energy. Economic activity is not possible without energy,  especially fossil fuels. An increase in economic activity necessarily leads to an increase in the use  fossil fuels and greenhouse gas emissions. In addition there will   be a commensurate increase in waste products, pollution and heat. This is dictated by the laws of physics and unavoidable.  A problem arise when the cost of this degeneration caused by continual economic growth surpasses the benefit society derives from it. The ecological economist Professor Herman Daly (2014) explained that when the impact on the ecosystem is correctly measured, global growth has reached a point where the total private and social costs of economic growth outweigh the private and social benefits. In other words, more economic growth is making global society worse off overall - growth has become uneconomic! The model shows that eventually pressures will build up that counteract the perennial belief that all social ills can be solved with economic growth. 

This model demonstrates the intertwining relationship between the economic contribution of industrial logging and that of adventure tourism (dominated by mountain biking).

In terms of the revenue from industrial logging at Derby, it is driven by demand of timber and the timber price. However, the forest resources are limited, which will put constraints on the expansion of industrial logging due to regrowth rate and existing forestation.

The tourism can bring economic benefits to Derby from hospitality and selling tickets to local adventure activities. The hospitality income can be determined by the average length of holidaying at Derby and average local pricing for accommodation, food and beverages and related essentials. Tickets sales are largely affected by the similar factors such as average expense per activity and average number of activities that tourists usually choose. Having explained the streams of possible income from the tourism, the key driver for tourism income is the desire or demand to travel. Unlikely logging, tourism is renewable and perpetual. However, logging can be conceived as a major constraint on attracting as many tourists as the economy so desires.

This is because deforestation caused by logging will diminish the natural scenery at Derby and in turn, the tourist operations and attractions based upon natural scenery. Loss of forest resources is likely to make Derby less attractive to visitors.

In short, the tourism and logging both provides economic benefits to Derby but in a competing relationship. However, the sustainability possessed by tourism cannot be rivaled by industrial logging in long term. Logging revenue reveals its advantage at inception of observed time period. Such advantage wears out over the time due to reduction in resources and sluggish regrowth. Eventually. the tourism income turns into the major player. To understand how they co-exist, please simulate the model. 

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

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.

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

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

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

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

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.

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.

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

Based on chapter 14 of Modeling Dynamic Economic Systems

WIP Ideas from Science Special Issue May 2014

Summary WIP of Thomas Palley's 2012 Book