Health | Insight Maker

6

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

Clone of Human Body Systems Efficiency

SARS Modelling with SEIR Model.

Author: Aulia Nur Fajriyah & Lutfi Andriyanto

SEIR

Lutfi Andri

5

This model should be used purely for personal interest. I’m a designer and have no training in epidemiological studies. So, take this as an interesting experiment and nothing else.

I made it for a university subject looking into modelling and thought this was a very interesting topic area.

Using figures from:

Zhang, L., Peng, P., Wu, Y., Ma, X., Soe, N. N., Huang, X., Wu, H., Markowitz, M., & Meyers, K. (2018). Modelling the epidemiological impact and cost-effectiveness of prep for HIV transmission in MSM in China. *AIDS and Behavior*, *23*(2), 523-533. https://doi.org/10.1007/s10461-018-2205-3

Schneider, K., Gray, R. T., & Wilson, D. P. (2014). A cost-effectiveness analysis of HIV Preexposure prophylaxis for men who have sex with men in Australia. *Clinical Infectious Diseases*, *58*(7), 1027-1034. https://doi.org/10.1093/cid/cit946

AFAO. (2021). *HIV IN AUSTRALIA*. Australian Federation of AIDS Organisations. https://www.afao.org.au/wp-content/uploads/2020/12/HIV-in-Australia-2021.pdf

Department of Health. (2018). *National HIV Strategy* (8). Commonwealth of Australia. https://www1.health.gov.au/internet/main/publishing.nsf/Content/ohp-bbvs-1/$File/HIV-Eight-Nat-Strategy-2018-22.pdf

Monitoring HIV pre-exposure prophylaxis (Prep) uptake in Australia: Issue 1*. (2021, June 29). Kirby Institute. https://kirby.unsw.edu.au/report/monitoring-hiv-prep-uptake-australia-issue1

Monitoring HIV pre-exposure prophylaxis (Prep) uptake in Australia: Issue 4*. (2021, June 29). Kirby Institute. https://kirby.unsw.edu.au/report/monitoring-hiv-prep-uptake-australia-issue4

PrEP affordability and its effects on infections

Jason Moisiadis

Ophthalmologist Craig Blackwell described the 'Dry Eye Cycle' in two excellent videos with graphic descriptions. From his description it becomes clear that the Dry Eye Cycle really consist of two self-reinforcing feedback loops. I have constructed these feedback loops here because they help explain the cycle, and understanding it may be very useful for suffers of the often debilitating 'dry eye syndrome '. The process usually starts with one of two deficiencies: the oil secreted from oil glands is too thick and does not adequately cover the aqueous film leading to evaporation and dryness or tear glands do not produce enough tears. Dr. Blackwell stresses the role of inflammation which can lead to the progressive worsening of the condition and which should be controlled. He suggests various treatments. I highly recommend watching the videos

https://www.youtube.com/watch?v=ooYcjzNtn58

https://www.youtube.com/watch?v=TwpWKCM31JI

DRY EYE CYCLE

Hanns-Jürgen Hodann

From NAP Toward Quality Measures for Population Health and the Leading Health Indicators Report with detailed Maternal Infant and Child Health Example Fig.3-5

Clone of Health Outcome Logic Model

Abdul Rahman Shour

Simple model of Paramecium with constrained growth.

Daffa Muhammad Romero

20/456363/TK/50493

Paramecium - Daffa Romero

Daffa Muhammad Romero

EEIS Health App

Mona

Description:

Model of Covid-19 outbreak in Burnie, Tasmania

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.

BMA708_Assignment 3_Nguyen Dang Khoa Vo_520272_COVID-19 outbreak and Burnie economy

NGUYEN DANG KHOA VO

Systems thinking study is challenging and has various a positive or negative impact on brain cell production.

Impact of systems thinking study on brain cell production

Richard

Moving from Disease Progression to Prevention Modelling - In this module, we add interventions and output indicators to create a ‘prevention’ model.

Complex CVD Prevention Model

Hadley Nanayakkara

A Susceptible-Infected-Recovered (SIR) disease model for Rage

SIRKimpossibles

Kimpossibles

Simulation of MTBF with controls

F(t) = 1 - e ^ -λt

Where

• F(t) is the probability of failure

• λ is the failure rate in 1/time unit (1/h, for example)

• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime,
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life

If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life

The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.

Wearout

The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period.

Clone of Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

Alena Peskova

Our Economy is all about making air filters using factories that make the air worse, causing more people to buy air filters.

Our Economy

Maximilian Wild

Simulation of MTBF with controls

F(t) = 1 - e ^ -λt

Where

• F(t) is the probability of failure

• λ is the failure rate in 1/time unit (1/h, for example)

• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime,
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life

If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life

The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.

Wearout

The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period.

Clone of Clone of Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

Alena Peskova

This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.

COVID Model

Justine Grace Jumawan

12 months ago

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

Clone of Human Body Systems Efficiency

Josué de Jesús Mira Hernández

This is the base stock and flow diagram I will use to develop a larger system of influencing factors, from health, agri-food systems, and environmental models. Data was taken from UNICEF and UNFPA. Time = 0 starts at 1987.

Child Stunting Guatemala

Lee Voth-Gaeddert

4

Diagrams of theories of control of destiny at multiple scales as fundamental causes of social determinants of health from Whitehead 2016 article in Health and Place

Control over decisions and health inequalities at multiple scales of experience

Geoff McDonnell

This model shows the relationship between placement to Bourke Hospital and Infection Rate, Recovery rate and release from Bourke Hospital.

Assumptions

This model assumes that:

upper value for Sensitive to get infected is 50 people

upper value for Placed into Bourke hospital is 50 people

upper value for Released from Bourke hospital is 50 people

Variables

Infection Rate - can be adjusted upwards or downwards to stimulate infection rate.

Infection Factor - can be adjusted upwards or downwards to stimulate infection rate.

Recovery Rate - can be adjusted upwards or downwards to stimulate infection rate.

Clone of Bourke Infection Rate

Sherif Abdelhamid

How do drugs affect us on individual and popular levels? Let's take a look at drug addiction as a system and pick it apart based on its biological, financial, mental, and communal effects.

Clone of The System of Drug Addiction

Tyler Williams

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.

BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST.

CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

Clone of 2014 Weather & Climate Extreme Loss of Arable Land and Ocean Fertility - The World3+ Model: Forecaster

MNTS

Health Models