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.

This model simulates a COVID outbreak occurring at Burnie, Tasmania. It links the extent to the pandemic with governments intervention policies aiming to limit the spread of the virus. The other part of the model illustrates how will the COVID statistics and the government enforcement jointly influence the economic environment in the community. A number of variables are taken into account, indicating positive or negative relationship in the infection and the economy model respectively.

Assumptions

·         Government takes responsive actions when the number of acquired cases exceeds 10.

·         Government’s prompt actions, involving closure of the state border, lockdown within the city, plans on mandatory vaccination and testing, effectively control the infection status.

·         Economic activities are reduced due to stagnation in statewide tourism, closure of brick-and-mortar businesses, and increased unemployment rate, as results of government restrictions.

Insights

Government’s rapid intervention can effectively reduce the infected cases. The national vaccination rollout campaign raises vaccination rate in Australians, and particularly influence the death rate in the infection model. Please drag the slider of vaccination to a higher rate and run the model to compare the outcomes.

Although local economy is negatively affected by government restriction policies, consumer demand in online shopping and government support payments neutralize the negative impact on economy and maintain the level of economic activities when infections get controlled. 

The majority of virus-infected individuals will experience a mild to severe respiratory disease and will recover without the need for special care. However, some people will get serious illnesses and need to see a doctor. Serious sickness is more likely to strike older persons and those with underlying medical illnesses including cancer, diabetes, cardiovascular disease, or chronic respiratory diseases. COVID-19 can cause anyone to become very ill or die at any age.

                COVID-19 primarily spreads through respiratory droplets that are produced when an infected person talks, coughs, or sneezes. If someone nearby inhales the droplets or touches surfaces that have been exposed to them and then touches their face (mouth, nose, or eyes), they could become infected.

                This model shows an infection's rapid growth reflects an exponential increase in the rate of transmission. On top of that, the majority of patients with minor symptoms will probably recover from the virus with the aid of medical care and isolation, boosting each person's chance of recovery.

Source: https://www.who.int/health-topics/coronavirus#tab=tab_1

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.