Upgrade of Kermack–McKendrick Epidemic SIR Infectious Disease Model (circa 2015) - 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.

Addition of a slider for susceptibles is equivalent to accumulated total cases

SARS, MERS AND COVID are similar virus types only differing in their sub genus

The COVID outbreak has reached 150,000 infected people

This simulation allows an attempt at predicting how long the virus will persist and its longevity dependence on its high speed massive infection numbers that have reached pandemic proportions

SARS reached 8,000 infected total and ran for 9 months before stopping

MERS 2012 is still killing 8 years later with patients dying even after using interferon to try and cure them

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.

Rich picture version of causal loop diagram for medication errors, showing the importance of reporting, analyzing and fixing knowledge and process errors. Medication errors will tend to grow due to the use of more medications in more complex patients. This is exacerbated by the loss of staff knowledge by turnover and goal erosion in places with harmful errors.

A rich picture representation of the interactions described in Producing Health Consuming HEalth Care and usually referred to as the EBM Model or Field Theory of Health

Insight synthesis of exchanges to better understand the basis of this decision. Unstructured additions from GMcD

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Causal loop diagram based on Jack  Homer's  Worker burnout: a dynamic model with implications  for prevention and control See IM-333 for simulation model and IM-641 for Rich Picture CLD

System Dynamics Review 1985 1(1)42-62

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

Rich Picture Version of Insight 386 The Dynamics of Human Service Delivery General Theory from the Book by Levin, Roberts, Hirsch et al. Ballinger 1976 ISBN 0-88410-132-0

Addition of glucagon hormone action to control glucose homeostasis extended from IM-586

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.

Model of Buffer Margin from Robert Wears thesis WIP

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

Z212 from System Zoo 1 p142-148

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.