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.

From Powers et al 2011 paper and Mansell's video

Health Care, CHC Assessment process

This is a first attempt at identifying the actors that may need to be involved in the development of the model - at one stage or other.

DIabetes incidence inflow, Prevalence stock and deaths outflow

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

This is an insight about food-born pathogens and what factors are affected by it's outbreaks. This is a huge issue that is not very well-known. The pathogens in food has been increasing their resistance to antibiotics by mutations. Because we are generally using antibiotics more each day, the resistance in these pathogens is growing more rapidly then it did before.

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

Expansion of IM-24317 for adding other funding insights Im-633 and 636 . See also IM-57110 contrasting economic drivers of mental health with other health care based on JHPPL David Mechanic 2016 issue articles, esp Glied

Clone of Clone 4of IM 27348, of JPS IM-27150 of original JPS IM-14117 See reference in diagram notes. WIP for Environment part of primary care regional model

WIP based on Geoffrey Brennan's Selection and the Currency of Reward chapter expanded from IM-396 

WIP Clone of IM-435 for Research (IM-345 was https://insightmaker.com/insight/435/Technology-and-Healthcare-Costs-and-Outcomes-2A simplified dynamic model, adapted from Engineering perspectives on healthcare delivery: Can we afford technological innovation in healthcare? Rouse, William B Systems Research and Behavioral Science 2009 Vol 26 (5) p573-582 abstract  Developed by Mark Heffernan See also IM-614 for learning effects.

WIP based on WHO Decade of Healthy Ageing baseline report  2020 Annex

Simplification of Prevention Investment Framework (private) IM See WIP integrating with economic view insight (private) and multiscale version IM private

Or the self-fulfilling prophecy

This is a high level system dynamics model which is built to determine the dynamic relationships of the FSA and Followups capacity. Therefore, it can help clinicians to find out the optimistic method in order to reduce the waiting list. At past clinicians were seeing more FSA patients, however, after few months, the followups patients overwhelmed the clinics. Therefore waiting list has been built up again. By running this model, clinicians can find out the balanced leverage point(s).

Emergency Room overcrowding dynamics one stock model

An adaptation of the URBAN1 Model from Navid Ghaffarzadegan, John Lyneis and George P Richardson's How small system dynamics models can help the public policy process. System Dynamics Review 27: 22-44 (2011) Conference version at http://bit.ly/HlxtZj  and LA Alfeld and AK Graham's Introduction to Urban Dynamics 1974 p 195.

An element of Perspectives: The Foundation of Understanding and Insights for Effective Action. Register at http://www.systemswiki.org/

Overview of choice modelling as part of value, effectiveness and motivation series of insights about wants, needs and demands related to health care (regional) model See also IM-4043

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