This model describes the whole process about government response and economic impact when the covid-19 outbreak in Burnie, Tasmania. When the reported cases increase to a certain level, the government realizes its high risk, then publishes a series of policies to protect the public, such as travel restriction, social distance and quarantine. The economic damage is also severe, especially for tourism and hostility industry and retail industry.

Clearly, in the beginning, the number of infected people and death cases increase sharply, but due to government policies and vaccination, it effectively reduces covid-19 cases. For economy, on one hand, the government health policies slow down the pace of growth, on the other hand, the government build vaccine confidence, which leads to more people getting vaccinated, and help the economy back to normal.

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 different paramenters.

The initial parametrization is based on the suggested current data. The initial population is set for Hong Kong.

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.

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

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.