SIR model with deaths by disease. We are working on the
speficication of this model for it to represent the global development
of the COVID-19 pandemic. This project is ongoing under the
responsibility of PPGEA Pandemics Task Force Team, from Universidade
Federal de Viçosa - UFV.
Dieses Causal
Loop Diagramm (CLD) versucht in vereinfachter Weisse die Wesentliche Dynamik des
Mars-CoV-2 zu veranschaulichen. Der Motor hinter den Infektionen ist offensichtlich
eine selbstverstärkende Rückkopplungsschleife, und ausschlaggebend in diesem
Bezug ist der R-Wert. Wenn der R-Wert unter 1 liegt, dann heisst das, dass eine
infizierte Person während des Zeitraums, in dem sie infektiös ist, weniger als
eine andere Person infiziert. Liegt der
Wert über 1, dann steckt die Infizierte mehr als eine andere Person an, und das
Virus verbreitet sich exponentiell. Die Schleifen, die blaue Pfeile enthalten, sind negative Rückkopplungsschleifen – sie bremsen die Verbreitung des Virus. Das
Diagramm suggeriert, dass der R-Wert als Schlüssel zur Kontrolle der Verbreitung
des Virus dienen könnte. Sollte der Wert über 1 steigen, so müssten Schutzmassnahem eingeführt werden. Ist der
Wert unter 1, dann sind die negativen Schleifen dominierend und einige Massnahmen könnten gelockert werden.
This is the first in a series of models that explore the dynamics of and policy impacts on infectious diseases. This basic model divides the population into three categories -- Susceptible (S), Infectious (I) and Recovered (R).
Press the simulate button to run the model and see what happens at different values of the Reproduction Number (R0).
The second model that includes a simple test and isolate policy can be found here.
Variant of the model "COVID-19 spread" made by Anxo-Lois Pereira and Miquel Martínez de Morentin, including reinfection, permanent immunity and Vaccines. Made for the subject of TAED.
COVID-19 spread with reinfeccion, permanent immunity and vaccines
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.
Clone of Clone of Clone of SEIRD 02: COVID-19 spread with containment measures
