Barangay IRAWAN Systems Model

​BACKGROUND:

Very basic stock-flow diagram of compound interest with table and graph output in interest and savings development per year. Initial deposit, interest rate, yearly deposit and withdrawal can all be modified.

Social determinants of health are economic and social conditions that influence the health of people and communities. These conditions are shaped by the amount of money, power, and resources that people have, all of which are influenced by policy choices. Social determinants of health affect factors that are related to health outcomes. Factors related to health outcomes include:

• How a person develops during the first few years of life (early childhood development)
• How much education a persons obtains
• Being able to get and keep a job
• What kind of work a person does
• Having food or being able to get food (food security)
• Having access to health services and the quality of those services
• Housing status
• How much money a person earns
• Discrimination and social support

Ocean/atmosphere/biosphere model coupled to economics-based simulations from Y2k on.

Overview:

Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.

Variables

The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc. 

Assumptions:

From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.

In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.

Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.

Step 1: Try to set all value variables at their lowest point and then stimulate. 

 

Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.

Step 2: Try to increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate

Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).

With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.

Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate

Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.

In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.

Step 4: Try to change the GDP Growth Rate to 0.14, then Tourism Growth Rate to 0.02, others keep the same as step 3, and then simulate the model

Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632. 

This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.

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

An economic model of Oregon's Marijuana. 

HOW A NEW COMMUNITY ENGAGEMENT INITATIVE MAY IMPACT YOUTH CRIME IN THE TOWN OF BOURKE, NSW

MKT563 Assessment 4:  Kari Steele 

 

Aim of Simulation: 

Bourke is a town in which Youth are involved in high rates of criminal behaviour (Thompson, 2016).  This simulation focuses on how implementation of a community engagement initiative may impact crime patterns of youths in Bourke.   The specific aim is to assess whether the town should initiate a program such as the Big Brothers Big Sisters Community-Based Mentoring (CBM) (Blueprints for Healthy Youth Development, 2018) program to reduce crime and antisocial behaviour (National Institute of Justice, n.d).  Big Brothers Big Sisters is a community mentoring program which matches a volunteer adult mentor to an at-risk child or adolescent to delay or reduce antisocial behaviours; improve academic success, attitudes and behaviours, peer and family relationships; strength self-concept; and provide social and cultural enrichment (Blueprints for Healthy Youth Development, 2018). 

 

Model Explanation:

An InsightMaker model is used to simulate the influence of Big Brothers Big Sisters Initiative on Criminal Behaviour (leading to 60% juvenile detention rates) with variables including participation rate and also drug and alcohol use.

Assumptions:

1/ ‘Youth’ are defined, for statistical purposes, as those persons between the ages of 15 and 24 (United Nations Department of Economic and Social Affairs, n.d).

2/ Youth population (15 – 24 years) makes up 14.1% of the total population of LGA Bourke which according to the most up-to-date freely available Census data (2008) is 3091 (Australian Bureau of Statistics, 2010).  Therefore, youth population has been calculated as 435 individuals.

3/ Big Brothers Big Sisters Program is assumed to impact LGA Bourke in a similar manner that has been shown in previous studies (Tierney, Grossman, and Resch, 2000) where initiative showed mentored youths in the program were 46% significantly less likely to initiate drug use and 27 percent less likely to initiate alcohol use, compared to control.  They were 32 less likely to have struct someone during the previous 12 months.  Compared to control group, the mentored youths earned higher grades, skipped fewer classes and fewer days of school and felt more competent about doing their schoolwork (non-significant).  Research also found that mentored youths, compared with control counterparts, displayed significantly better relationships with parents.  Emotional support among peers was higher than controls. 

Initial Values:

Youth Population = 435

Criminal Behaviour = 100

40% of youth population who commit a crime are non-convicted

60% of youth population who commit a crime are convicted

20% of youth involved in the Big Brothers Big Sisters Initiative are non-engaged

80% of youth involved in the Big Brothers Big Sisters Initiative are engaged

Variables:

The variables include ‘Participation Rate’ and ‘Drug and Alcohol Usage’.  These variables can be adjusted as these levels may be able to be impacted by other initiatives which the community can assess for introduction; these variables may also change in terms of rate over time.

Interesting Parameters

As can be seen by increasing the rate of participation to 90% we can see juvenile detention rate decreases with engagement (even with the 20% non-engagement of youths involved in program).  By moving the slider to 10% participation however you can see the criminal behaviour increase.   

Conclusion:

From the simulation, we can clearly see that the community of Bourke would benefit in terms of the Big Brothers Big Sisters Initiative decreasing criminal behaviour in youths (15 – 24 years of age) over a 5-year timeframe.  Further investigation regarding expenditure and logistics to implement such a program is warranted based on the simulation of impact.

 

References:

Australian Bureau of Statistics.  (2010).  Census Data for Bourke LGA.  Retrieved from www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/LGA11150Population/People12002-2006?opendocument&tabname=Summary&prodno=LGA11150&issue=2002-2006

 

Blueprints for Healthy Youth Development.  (2018).  Big Brothers Big Sisters of America Blueprints Program Rating: Promising, viewed 26 May 2018, <www.blueprintsprograms.com/evaluation-abstract/big-brothers-big-sisters-of-america>

 

National Institute of Justice.  (n.d.).  Program Profile: Big Brothers Big Sisters (BBBS) Community-Based Mentoring (CBM) Program, viewed 26th May 2018, <https://www.crimesolutions.gov/ProgramDetails.aspx?ID=112>

 

Tierney, J.P., Grossman, J.B., and Resch, N.L. (2000). Making a Difference: An Impact Study of Big Brothers/Big Sisters. Philadelphia, Pa.: Public/Private Ventures.
http://ppv.issuelab.org/resource/making_a_difference_an_impact_study_of_big_brothersbig_sisters_re_issue_of_1995_study

 

Thompson, G. (2016) Backing Bourke: How a radical new approach is saving young people from a life of crime.  Retrieved from < www.abc.net.au/news/2016-09-19/four-corners-bourkes-experiment-in-justice-reinvestment/7855114>

 

United Nations Department of Economic and Social Affairs (UNDESA).  (n.d.).  Definition of Youth, viewed 24th May 2018, www.un.org/esa/socdev/documents/youth/fact-sheets/youth-definition.pdf

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This Model described the outbreak simulation under government policy and impacts on Economics.

In this model I am trying to depict the multiple factors and interactions that impact student academic achievement.  As educators, our goal is to optimize the progression of academic achievement, or as represented in this stock flow diagram maintain the stock (academic achievement) at the highest level.  Multiple factors enhance achievement and, conversely, multiple factors interact to reduce the stock/rate of achievement.  As individual teachers, we must understand the factors and relationships that increase and decrease achievement.  In particular, teachers in training need to begin to build a mental model of these factors and relationships.  Only then can we optimize our individual learning environments to ensure each child reaches his/her academic achievement potential.

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