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  Constraints that deconstrain

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

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

From Michael Hiete, Mirjam Merz and Frank Schultmann (2011) Scenario-based impact analysis of a power outage on healthcare facilities in Germany IJDRBE 2,3 p222-244

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 See IM 1769 for updated simpler version without serialization of the downstream stock

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

Adapted from Lane, David C. and Husemann, E. (2008) Steering without Circe: attending to reinforcing loops in social systems. System dynamics review, 24 (1). pp. 37-61. ISSN 0883-706 paper abstract  See also Conceptual hybrid mapping IM

Dynamics of Emergency Room Crowding a simple uncalibrated simulation which includes Wards and some simple internal ED feedback effects

DIabetes incidence inflow, Prevalence stock and deaths outflow

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