This is a balanced loop model that demonstrates how COVID
19 outbreak in Burnie and the response of the government (e.g. by enforcing health
policies: Lockdown; quarantine, non-necessary business closure; border closure)
affect the local economy. This model has 13 positive loops and seven negative loops. Government response
is dependent on the number of reported COVID-19 cases which in turn thought to be dependent on the testing rates less those who recovered from COVID 19 and
dead. Economic activity is dependent on the economic growth rate, increased in online shopping, increased in unemployment, number of people who do not obey the rules, COVID 19 cases and health policies.
ASSUMPTIONS
· Both infection and economic growth is reduced by
enforcing government policies
· However, the negative effect of government policies is reduced
by the number of people who do not obey government health policies
· Govt policies are enforced when the reported
COVID-19 case are 10 or greater.
· Number of COVID cases reported is dependent on the testing rates less those who recovered and dead.
· The higher number of COVID-19 cases have a
negative effect on local economy. This phenomena is known as negative signalling.
· Government policies have a negative effect on economic activity because health policies limit both social and economic activities which directly or indirectly affect the economy in Burnie .
· This negative effect is somewhat reduced by the
increase in online shopping and the number of people who do not obey heath
rules.
INTERESTING INSIGHTS
The test ratings seem to play a vital role in controlling COVID-19 outbreak. Higher Rates of COVID testings decrease the number of COVID 19 deaths and number of infected. This is because higher rates of testing accelerate the
government involvement (as the government intervention is triggered earlier, 10 COVID cases mark is reached earlier). Delaying the government intervention by reducing the COVID testing rates increases the death rates and number of infected.
Increased testing rates allow the figures (deaths, susceptible, infected) to reach a plateau quickly.
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 simulates the comparison between mountain biking industry and forestry/logging in Derby Tasmania.
How the model works
On the left-hand side, Derby Mountain biking, tourists visit the mountain according to reviews and recommendation of mountain scenery and entertainment activities. The number of people who hire bikes and who choose to dine on the mountain are limited by bike availability. Both bike hiring and biker dining contribute to tourist revenue in Derby. On the right-hand side, forest trees grow at certain rates, but are negatively affected by timber demand. Timber logging generate revenue, which depends on sale price and associated cost.
Interesting insights
Although forestry contributes more revenue in a certain time, it seems that Derby Mountain bike generate more tourist revenue from dining services and bike hiring in a long term.
This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be concluded as Susceptible, Infection and Recovery, which are used as the main data in this model. However, the improvement of decreasing of reported infection rates of this infectious disease and increasing of recovery rates are contributed by the implementation of the Government Health Policy.
Assumption
The decrease of both infection rate and economic growth are all influenced by the Government Health Policy simultaneously. The Government Health Policy is only triggered when there are 10 cases reported. However, the increase in reporting COVID-19 cases affects economic growth negatively.
Interesting Insights:
There are two interesting insights that have been revealed from the simulation. First, the death rate continuously increased even though the infection rate goes down. However, the increase in testing rates contributed to the stability of the death rate towards the end of the week. Moreover, higher testing rates also trigger faster government intervention, which can reduce infectious cases. Second, as the Government Health Policy limited the chance of going out and shopping, the economic growth is negative due to the higher cases.
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.
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.
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 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.
This model shows the changing happened in forest industry and mountain tourism in Derby Tasmania. Logging will degrade mountain tourism while benefit the forestry industry. Simulation borrowed from the Easter Island simulation.
According to the analysis, logging does not reduce tourism income. With the increase of number of bike guide, tourism income will increase as well. Also, in forest industry, timber income is higher than the harvest spending which means the industry always gain profits from logging. Therefore, the main concern is that the logging should be balanced between the Mountain Tourism and the forest industry.
A model situmalte the relationship between moutain bikes and logging industry in Derby, Tasmania, It explains more when the number of visitors increases or decreses.
How the model works
The left side shows when the number of travellers increase, the income from travellers rental of bike and stay of hotel increase simultaneously. However, there is a capacity for both parking lots and hotel venues, which means that the top ability of hospitality of Derby. The right side shows the logging industry of Derby and income from logging. It has a impact on how travellers would value Derby moutain.
Insights
As the number of travellers increase, it increases the total income of Derby, and in return, the local government will re-revest in Derby Moutain and will also maintain the forrestry logging industry.
This model simulates logging and mountain biking competition in Derby, Tasmania. The Simulation is referenced to simulate Derby mountain biking with logging.
Model Work
The tourism industry is represented on the model's left side, and the logging industry is on the right side. Interactions between these two industries generate tax revenues. Logging and tourism have different growth rates regarding people working/consuming. The initial values of these two industries in the model are not fixed but increase yearly due to inflation or economic growth.
Detail Insights
From the perspective of tourism, as the number of tourists keeps growing, the number of people who choose to ride in Derby City also gradually increases. And the people who ride rate the ride. The negative feedback feeds back into the cycling population. Similarly, positive cycling reviews lead to more customer visits. And all the customers will create a revenue through tourism, and a certain proportion of the income will become tourism tax.
From a logging perspective, it is very similar to the tourism industry. As the number of people working in the industry is forecast to increase, the industry's overall size is predicted to grow. And as the industry's size continues to rise, the taxes on the logging industry will also continue to rise. Since logging is an industry, the tax contribution will be more significant than the tourism excise tax.
This model assumption is illustrated below:
1. The amount of tax reflects the level of industrial development.
2. The goal of reducing carbon emissions lets us always pay attention to the environmental damage caused by the logging industry.
3. The government's regulatory goal is to increase overall income while ensuring the environment.
4. Logging will lead to environmental damage, which will decrease the number of tourists.
This model is based on tourism tax revenue versus logging tax revenue. Tourism tax revenue is more incredible than logging tax revenue, indicating a better environment. As a result of government policy, the logging industry will be heavily developed in the short term. Growth in the logging industry will increase by 40%. A growth rate of 0.8 and 0.6 of the original is obtained when logging taxes are 2 and 4 times higher than tourism taxes.
Furthermore, tourism tax and logging tax also act on the positive rate, which is the probability that customers give a positive evaluation. The over-development of the logging industry will lead to the destruction of environmental resources and further affect the tourism industry. The logging tax will also affect the tourism Ride Rate, which is the probability that all tourism customers will choose Derby city.
This model more accurately reflects logging and tourism's natural growth and ties the two industries together environmentally. Two ways of development are evident in the two industries. Compared to tourism, logging shows an upward spiral influenced by government policies. Government attitudes also affect tourism revenue, but more by the logging industry.
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 logging and mountain biking competition in Derby, Tasmania. The Simulation is referenced to simulate Derby mountain biking with logging.
Model Work
The tourism industry is represented on the model's left side, and the logging industry is on the right side. Interactions between these two industries generate tax revenues. Logging and tourism have different growth rates regarding people working/consuming. The initial values of these two industries in the model are not fixed but increase yearly due to inflation or economic growth.
Detail Insights
From the perspective of tourism, as the number of tourists keeps growing, the number of people who choose to ride in Derby City also gradually increases. And the people who ride rate the ride. The negative feedback feeds back into the cycling population. Similarly, positive cycling reviews lead to more customer visits. And all the customers will create a revenue through tourism, and a certain proportion of the income will become tourism tax.
From a logging perspective, it is very similar to the tourism industry. As the number of people working in the industry is forecast to increase, the industry's overall size is predicted to grow. And as the industry's size continues to rise, the taxes on the logging industry will also continue to rise. Since logging is an industry, the tax contribution will be more significant than the tourism excise tax.
This model assumption is illustrated below:
1. The amount of tax reflects the level of industrial development.
2. The goal of reducing carbon emissions lets us always pay attention to the environmental damage caused by the logging industry.
3. The government's regulatory goal is to increase overall income while ensuring the environment.
4. Logging will lead to environmental damage, which will decrease the number of tourists.
This model is based on tourism tax revenue versus logging tax revenue. Tourism tax revenue is more incredible than logging tax revenue, indicating a better environment. As a result of government policy, the logging industry will be heavily developed in the short term. Growth in the logging industry will increase by 40%. A growth rate of 0.8 and 0.6 of the original is obtained when logging taxes are 2 and 4 times higher than tourism taxes.
Furthermore, tourism tax and logging tax also act on the positive rate, which is the probability that customers give a positive evaluation. The over-development of the logging industry will lead to the destruction of environmental resources and further affect the tourism industry. The logging tax will also affect the tourism Ride Rate, which is the probability that all tourism customers will choose Derby city.
This model more accurately reflects logging and tourism's natural growth and ties the two industries together environmentally. Two ways of development are evident in the two industries. Compared to tourism, logging shows an upward spiral influenced by government policies. Government attitudes also affect tourism revenue, but more by the logging industry.
This model shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.
It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10.
Government intervention - lock down the state, suppress the development of COVID-19 effectively. It is related to most of people stay at home to reduce the exposure in public area.
On the other hand, it also bring the economy of Burnie in the recession, as no tourists, no dining out activities and decrease in money spending in the city.
