SIR model with herd immunity - Metrics by Guy Laekman

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

This common archetype of systems that include relapse or recidivism allows exploration of the unintended effects of increasing upstream capacity and swamping downstream capacity. The increase in the relapse rate eventually returns to swamp upstream capacity as well. A social welfare example, based on a TANF case study, from How Small System Dynamics Models Can Help the Policy Process. N. Ghaffarzadegan, J. Lyneis, GP Richardson. System Dynamics Review 27,1 (2011) 22-44 Conference version at http://bit.ly/HlxtZj

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.

From Rudolph JW Morrison JB and Carroll JS (2009) The Dynamics of Action-Oriented Problem Solving: Linking Interpretation and Choice. Academy of Management Review 34 (4) p733-756 Available online . This paper won the Forrester Award for 2012 and the presentation talk video is available. The presentation  paper abstract is available in System Dynamics Review 2013 here

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.

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

The dynamics of methadone treatment for intravenous opioid users. The major flows in this study were people cycling between being on methadone and off treatment. Monograph pdf

From MIT ESD work (de Weck Ilities ) esp on Survivability (Medecki) http://bit.ly/HVsceb and Unarticulated Value (Ross) 2006 PhD pdf

From Rudolph JW Morrison JB and Carroll JS (2009) The Dynamics of Action-Oriented Problem Solving: Linking Interpretation and Choice. Academy of Management Review 34 (4) p733-756 Available online . This paper won the Forrester Award for 2012 and the presentation talk video is available. The presentation  paper abstract is available in System Dynamics Review 2013 here

WIP for Continuity of care ISO From Wikipedia Initial Insight Representation from ContSys

Nurse workforce ageing chain showing effects of parenting, quits and enrollments on nurse numbers by age and average age.

Interacting nested fast and slow adaptive cycles from Panarchy Book  ,Resilience thinking Book Brian Walker and David Salt Island Press 2006 and the http://www.resalliance.org/ Website, See also What is Panarchy at http://bit.ly/H9RFkL