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.
Mountain Bike riding versus logging in Derby, Tasmania.
This is a model that shows logging vs adventure tourism in Derby. Derby is on the north-east of Tasmania and is a small town that is known for it's beautiful forestry, scenery and more recently it's mountain bike trials. Due to dense forestry it also means the Derby is known for logging within the same area. This has meant competing priorities have emerged between mountain bike riding on their world famous mountain bike trails and logging on the same trials impacting both sides. The impact of noise from machinery and interrupted views has meant some dissatisfaction in tourism and will decrease tourism numbers in the area. An increase in adventure tourism can detract from logging as well, which until more trails opened in Derby Forestry had the most impact the local economy. Most of the logging goes towards high-quality products such as tas oak furniture which also has a high demand. This model shows that logging and mountain bike riding in Derby can co-exist. As the demand for the mountain bike Derby and park capacity increases the adventure tourism decreases as less people will want to visit Derby for mountain biking if over crowded. As both create revenue for the economy it is important that they co-exist and logging can be contained to certain areas away from mountain biking.
This is a dynamic model that shows the correlation between the
health-related policies implemented by the Government in response to COVID-19 outbreak
in Burnie, Tasmania, and the policies’ impact on the Economic activity of the
area.
ASSUMPTIONS
The increase in the number of COVID-19 cases is directly
proportional to the increase in the Government policies in the infected region.
The Government policies negatively impact the economy of Burnie, Tasmania.
INTERESTING INSIGHTS
1. When the borders are closed by the government, the economy
is severely affected by the decrease of revenue generated by the Civil aviation/Migration
rate. As the number of COVID-19 cases increase, the number of people allowed to
enter Australian borders will also decrease by the government.
2. The Economic activity sharply increases and stays in uniformity.
3. The death rate drastically decreased as we increased test rate by 90%.
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.
This model aims to show that how the Tasmania government's COVID-19 policy can address the spread of the pandemic and in what way these policies can damage the economy.
Assumption:
Variables such as infection rate, death rate and the recovery rate are influenced by the actual situation.
The government will implement stricter travel bans and social distant policies as there are more cases.
Government policies reduce infection and limit economic growth at the same time.
A greater number of COVID-19 cases has a negative effect on the economy.
Interesting insights:
A higher testing rate will make the infection increase and the infection rate will slightly increase as well.
Government policies are effective to lower the infection, however, they will damage the local economy. While the higher number of COVID-19 cases also influences economic activities.
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 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;
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:
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.
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.
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 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.
When reported COVID-19 cases begin to show a rapid increase, the government will initiate control policies to deal with the spread.As the number of people tested increases and measures such as isolation and medical assistance are implemented, the number of people infected will decline rapidly.Therefore, the government's policy is to reduce and eliminate sources of transmission by increasing the number of tests and initiating control measures.At the same time, it also shows the negative impact of economic growth, which according to the model will stop in the next 20 weeks.
This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate.
Assumptions
Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.
Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.
Interesting Insight
There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death.
In contrast, lower testing rates increase the death rate and cases.
Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.
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.
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
