Adapted from Denis R. Towill, (1996) "Industrial dynamics modelling of supply chains", Logistics Information Management, Vol. 9 Iss: 4, pp.43 - 56 Diagram http://www.emeraldinsight.com/content_images/fig/0050260202006.png

Replaced by IM-752 Causal Loop Rich Picture unfolding from Repenning, N. and J. Sterman (2002). Capability Traps and Self-Confirming Attribution Errors in the Dynamics of Process Improvement. Administrative Science Quarterly, 47: 265 - 295. http://jsterman.scripts.mit.edu/docs/Repenning-2002-CapabilityTraps.pdf

balancing an integrated eye care training program with service needs and available resources

IM-1175 with computable arguments, based on ideas from Micropublications paper about Claims, Evidence, Representations and Context Networks

A Rich Picture Story representation of ED overcrowding based on Using system dynamics principles for conceptual modelling of publicly funded hospitals] by HJ Wong et al Journal of the Operational Research Society 63, 79-88 (January 2012) | doi:10.1057/jors.2010.164 Click the +view story on the bottom left

Major stocks and flows of Erythropoiesis and Erythropoiesis Stimulating Agents (ESA) Dosing in Anemia due to Renal Failure from Jim Rogers. See Simulation Insight

IPCC Extreme weather events combined context diagrams  FBE WIP

SIR model with herd immunity - Metrics by Guy Laekman

A Susceptible-Infected-Recovered (SIR) disease model with herd immunity

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.

Created in James Madison University's ISAT 341 Simulation and Modeling course by Joseph Straub and Andrew Funkhouser. Based on Mark Heffernan's Glucose-Insulin Insight Maker

Glucose Insulin Model Info:

Translated from Hormone.stm in Dynamic Modeling in the Health Sciences James L hargrove, Springer 1998, Ch 24 p255-261, by Mark Heffernan.

A work in progress conceptual model based on Using system dynamics principles for conceptual modelling of publicly funded hospitals by HJ Wong et al Journal of the Operational Research Society advance online publication, 27 April 2011 doi:10.1057/jors.2010.164. Linked concepts with annotated definitions.