WIP summaries of bill mitchell's blog postings related to the connections between macroeconomics and wellbeing, particularly via unemployment and inflation

Based on Psychological Medicine Dec 2015 article Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder by A. K. Wittenborn, H. Rahmandad, J. Rick and N. Hosseinichimeh, mentioned here. See also 2018 N. Hosseinichimeh Plos ONE article for rumination focussed SD simulation

Simplified Rich picture version of Tanner's Clinical Judgment Model, with the addition of clinical reasoning cycle concepts from T Levett-Jones et al Nurse Education Today 30 (2010) 515-520

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA.

Understanding diabetes population dynamics through simulation modeling

and experimentation. American Journal of Public Health 2006;96(3):488-494.

http://ajph.aphapublications.org/cgi/content/abstract/96/3/488

Based on diagrams in the NAP 2015 Report Improving diagnosis in health care process Compare with IM-885 Clinical judgment to provide context and and Decision Ladder IM-689 to provide decision process task detail

Clone of IM-91683 from jacqui and vincy Summary of paper map produced by participants at the compelling case for prevention workshop 6 june 2017. Current premier version for vincy to update

To avoid blood sugar spikes, eat first fiber, then proteins and fats, and lastly, carbohydrates: pasta and sweets in general.

This video shows how to use this model. It shows the blood glucose results when you adjust the time at which you eat each of these types of food.

The video was made by capturing the screen of this computer simulation model, based on System Dynamics, and is intended exclusively to conceptually show in an interactive and dynamic way what happens to blood glucose when the order in which foods are eaten is changed, as described in detail in the book "Glucose Revolution: The Life-Changing Power of Balancing Your Blood Sugar" by Jessie Inchauspé.

Modified from Sterman (2006) and Gene Bellinger's Assumptions IM-351 by Dr Rosemarie Sadsad UNSW See also Complex Decision Technologies IM and IM-63975

Summary of Gizem Aktas M Sc thesis and prize winning paper from ISDC2018 extended abstract pdf 

Kermack–McKendrick Epidemic SIR Infectious Disease Model - Metrics by Guy Lakeman

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0.

For Applying the Systems Approach Document available as a word doc from Systemswiki page

From David Rees PhD dissertation "Developing a Theory of Implementation for Better Chronic Health Management" Health Services Research Centre, Victoria University of Wellington, New Zealand

WIP based on right care series in Lancet and OECD Tackling wasteful spending on health book 

WIP Stock Flow representation of Panarchy Adaptive Cycles

Extension of IM-14144 with ward outliers as a prelude to a modular ward dynamics framework. Should include ideas in IM-1008 For backlog and services see IM-8382

Population Model

Based on Chris Argyris 2010 Book Organizational Traps Oxford University Press, built around Insight 619 on single and double loop learning

Upgrade of Kermack–McKendrick Epidemic SIR Infectious Disease Model (circa 2015) - Metrics by Guy Lakeman

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0.

Addition of a slider for susceptibles is equivalent to accumulated total cases

SARS, MERS AND COVID are similar virus types only differing in their sub genus

The COVID outbreak has reached 150,000 infected people

This simulation allows an attempt at predicting how long the virus will persist and its longevity dependence on its high speed massive infection numbers that have reached pandemic proportions

SARS reached 8,000 infected total and ran for 9 months before stopping

MERS 2012 is still killing 8 years later with patients dying even after using interferon to try and cure them

A model of an infectious disease and control

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

DIabetes incidence inflow, Prevalence stock and deaths outflow

This model shows the dynamics of use of a new medical technology based on its scope of use and perceived relative benefit. The technology needs cumulative usage to fix its glitches, and the benefits will be diluted  as the scope of use widens.