A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
This model simulates the COVID-19 outbreaks in Burnie, the government reactions, as well as the economic impact. The government's strategy is based on the number of COVID-19 cases reported and testing rates and recovered.
Assumptions
In the same trend that government policy decreases infection, it also reduces economic growth.
When there are ten or fewer COVID-19 cases reported, government policy is triggered.
The economy suffers as a result of an increase in COVID-19 cases.
Interesting insights
The higher testing rates appear to result in a more quick government response, resulting in fewer infectious cases. However, it has a negative influence on the economy.
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government policies reduces the infection and economic growth at the same time.
Interesting Insights:
In the first two weeks, the infected people showed an exponential growth, in another word, that’s the most important period to control the number of people who got affected.
This Model was developed from the SEIR model (Susceptible, Enposed, Infected, Recovered). It was designed to explore relationships between the government policies regarding the COVID-19 and its impact upon the economy as well as well-being of residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government Policies affect the economy and the COV-19 infection negatively at the same time;
Government Policies can be divided as 4 categories, which are Social Distancing, Business Restrictions, Lock Down, Travel Ban, and Hygiene Level, and they represented strength of different aspects;
Parameters:
Policies like Social Distancing, Business Restrictions, Lock Down, Travel Ban all have different weights and caps, and they add up to 1 in total;
There are 4 cases on March 9th;
Ro= 5.7 Ro is the reproduction number, here it means one person with COVID-19 can potentially transmit the coronavirus to 5 to 6 people;
Interesting Insights:
Economy will grow at the beginning few weeks then becoming stagnant for a very long time;
Exposed people are significant, which requires early policies intervention such as social distancing.
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.
This model is upgraded on the basis of the SIR model and adds more variables.
A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy
Government policy will help control the growth of COVID-19 cases by getting people tested.
This is a system dynamics model of COVID-19 outbreak in Burnie which shows the process of infections and how government responses, impact on the local economy.
First part is outbreak model, we can know that when people is infected, there are two situations. One is that he recovers from treatment, but even if he recovered, the immunity loss rate increase, makes him to become infected again. The other situation is death. In this outbreak, the government's health policies (ban on non-essential trips, closure of non-essential retailers, limits on public gatherings and quarantine ) help to reduce the spread of the COVID-19 new cases. Moreover, government legislation is dependent on number of COVID-19 cases and testing rates.
Second part: the model of Govt legislation and economic impact. Gov policy can help to reduce infection rate and local economy at same way. The increase of number of COVID-19 cases has a negative impact on local Tourism industry and economic growth rate. On the other hand, Govt legislation also can be change when reported COVID-19 case are less or equal to 10.
This model which simulates the competition of Logging with Mountain Tourism in Derby, Tasmania. This main reason of this simulation is to find if logging will affect the mountain tourism and by any chance they can co-exist.
How the model works.
Both Timber harvesting and mountain tourism can bring the economic contribution to Tasmania. In the Logging industry, it helps increase the need of employment and at the same time logging generate the profit through selling those timbers. In the Mountain Tourism industry, it can get the revenue through couple of ways which include accommodation (approximately 3 days find in paper), Restaurant and parking fee. However, the low growth rate of the trees is not keeping up with the rate of logging, if the trees getting less in Derby mountain, it will affect the sights and the riding experience for tourists, which will affect the satisfaction and expectation as it depends on the sights and experience. The satisfaction and expectation will influence the number of visitors, if they satisfied, they can come again or tell others about the great experience, if not, more and more people will not come again.
Interesting insights
It seems like logging has no significant negative effect to the mountain tourism, compare the forestry income with the tourism income, tourism income gradually higher than the forestry income at last, which means tourism is in a very important position, as long as the visitors are stable, tourism industry can provide greater economic contribution, stakeholders and governments can find the balance by maintain the status or better slightly reduce logging in order to make them co-exist.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts
Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).
[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS]
A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply.
It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases are 10 or less.
Interesting insights
The infection rate will decline if the government increase the testing ranges, meanwhile, the more confirmed cases will increase the pressure on hospital capacity and generate more demand for medical resources, which will promote government policy intervention to narrow the demand gap and affect economic performance by increasing hospital construction with financial investment.
This model demonstrates the COVID-19 outbreak in Bernie, Tasmania, and shows the relationship between coVID-19 outbreaks, government policy and the local economy. The spread of pandemics is influenced by many factors, such as infection rates, mortality rates, recovery rates and government policies. Although government policy has brought the Covid-19 outbreak under control, it has had a negative impact on the financial system, and the increase in COVID-19 cases has had a negative impact on economic growth.
Assumptions:
The model is based on different infection rates, including infection rate, mortality rate, detection rate and recovery rate. There is a difference between a real case and a model. Since the model setup will only be initiated when 10 cases are reported, the impact on infection rates and economic growth will be reduced.
Interesting insights:
Even as infection rates fall, mortality rates continue to rise. However, the rise in testing rates and government health policies contribute to the stability of mortality. The model thinks that COVID-19 has a negative impact on offline industry and has a positive impact on online industry.
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts
Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
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.
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.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
Overview This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.
How the model works
The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to increased visitors using the forest of mountain biking. Accompanying variables effect the tourism income that flows from use of the bike trails. On the right side, the forest flow begins with tree growth then a demand for timber leading to the logging production. The sales from the logging then lead to the forestry income. The model works by identifying how the different variables interact with both mountain biking and logging. As illustrated there are variables that have a shared effect such as scenery and adventure and entertainment.
Variables The variables are essential in understanding what drives the flow within the model. For example mountain biking demand is dependent on positive word mouth which in turn is dependent on scenery. This is an important factor as logging has a negative impact on how the scenery changes as logging deteriorates the landscape and therefore effects positive word of mouth. By establishing variables and their relationships with each other, the model highlights exactly how mountain biking and forestry logging effect each other and the income it supports.
Interesting Insights The model suggests that though there is some impact from logging, tourism still prospers in spite of negative impacts to the scenery with tourism increasing substantially over forestry income. There is also a point at which the visitor population increases exponentially at which most other variables including adventure and entertainment also increase in result. The model suggests that it may be possible for logging and mountain biking to happen simultaneously without negatively impacting on the tourism income.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
![[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS] A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply. It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases](https://insightmakercloud-files.storage.googleapis.com/44b61667-a1e2-4b2a-ade5-e63899744c9d/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20260107%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20260107T002216Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
