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

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

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

DIabetes incidence inflow, Prevalence stock and deaths outflow

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.

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.

Rich picture CLD of Tradeoffs in Responses to Work Pressure in the Service Industry by Rogelio Oliva California Mgt Review 2001 43(4) 26-43 paper

Jack Homer's model via ithink and Mark Heffernan.  From Jack's  paper Worker burnout: a dynamic model with implications  for prevention and control System Dynamics Review 1985 1(1)42-62. See IM-641 for the Causal loop diagram

This map is a WIP derived from the MIT D-memo 4641 presentation by Nelson Repenning 1996 and the paper "Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement" by Nelson P. Repenning and John D Sterman. http://bit.ly/jCXGKL See Insight 9781 for a simulation of this model. This map adds additional features mentioned in the article to the bare bones simulation in IM-9781

Patients on Treatment represented as people in a swimming pool. Fast Switchers change costumes. Lapsed Users sit by the pool. Misdiagnosed people are in the wrong pool. Extended from IM-305

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

We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.

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

The questions that we want to answer in this kind of models are not the shape of the curves, that are almost known from the beginning, but, when this happens, and the amplitude of the shapes. This is crucial, since in the current circumstance implies the collapse of certain resources, not only healthcare.

The validation process hence becomes critical, and allows to estimate the different parameters of the model from the data we obtain. This simulation approach allows to obtain somethings that is crucial to make decisions, the causality. We can infer this from the assumptions that are implicit on the model, and from it we can make decisions to improve the system behavior.

Yes, simulation works with causality and Flows diagrams is one of the techniques we have to draw it graphically, but is not the only one. On https://sdlps.com/projects/documentation/1009 you can review soon the same model but represented in Specification and Description Language.