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 Werner Ulrich's JORS Articles Operational research and critical systems thinking – an integrated perspective. Part 1: OR as applied systems thinking. Journal of the Operational Research Society advance online publication (14 December 2011). and Part 2 :OR as argumentative practice.

Evolution of eHEalth focus from Documenting, to Workflow to Assisting Clinical Judgement. Conceptual confusion among data, information and knowledge models and management. 

Linked concept version of Tanner's Clinical Judgment Model used as a scaffolding to start modeling managing the deteriorating patient.

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.

An overview of Thomas A Goudge's Book on The Thought of CS Peirce Dover NY 1950 and Thomas Knight's Book Charles Peirce NY 1965. See also causality insight

Attempting to outdo an opponent leads to escalation. A weaker response leads to De-escalation. A slightly more complex  form of Insight 972.  ​Z508 p36-38 System Zoo 3 by Hartmut Bossel.

Model for Rehab 2139 Board Game by Mark Heffernan and Lynette Lee

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

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.

From the Plausibility of life book esp p 220-227

Based on G.P. Cimellaro et al. Framework for analytical quantification of disaster resilience Engineering Structures 32 (2010) 3639–3649 paper

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

Hospital Flood Response Map WIP FBE