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.
![Initial data from: Italian data [ link ], as of Mar 28 Incubation estimation [ link ] Model focuses on outbreak dynamics and control, this version ignores symptom onset to hospital admission and the rest of recovery dynamics.](https://insightmakercloud-files.storage.googleapis.com/a945023c-0f59-409e-b0a3-460d30361ee8/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20251124%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20251124T085418Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
