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.
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
This model visualizes the Covid-19 hypothetical transmission at Barangay Busybees, Taytay, Palawan.
Hypothetical Situation: Barangay Busybees:
- Has a total population of 500 individual.
- Is a rural area.
- Strictly implemented health protocols.
Conclusion: Given the population and situation, transmission of Covid-19 is observed to be less in earlier stage but once transmission start it became rampant. Despite the rampant transmission, the strict implementation of protocols made recovery of people from Covid 19 possible with 92% recovered from the covid.
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 COVID-19 spread with containment measures
This model shows the COVID-19 outbreak in Burnie and how the government policy impacts the economy. The possible phases when the infectious disease spreads in Burnie can be labelled as Susceptible, Infection and Recovery, which are main factors in the model. It is concluded that the government policy can reduce the infectious disease and also the impact in the overall economy.
Assumption
The Government Healthy Policy will affect the decrease in the infection and economy growth rate at the same time.
The Government Health Policy is only triggered when there are more than 10 cases
The increase in number of COVID-19 cases can affect negatively towards the economic growth.
Interesting Insights:
The Government's vaccination promote will reduce the possibility of spreading the infectious disease.
When vaccination rate increase, the dead, infected people and susceptible group will all decrease. This reveals that the crucial role in government's vaccination promote program.
When there is more than 10 confirmed cases, the government policies can effectively reduce the infections and the overall economic activities.
This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.
Assumptions
The government policy is triggered when number of infected is more than ten.
The government policies will take negative effect into Covid-19 outbreaks and financial system
Adjusted parameter: Immunity loss rate can be change from vaccination rate.
Government policy's parameters
Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)
Economic's parameters
Fixed parameter: Tourism
Adjusted parameter: Economic growth rate(from 0.3 to 0.5)
Interesting insight
Increase vaccination rate and testing rate will decrease the number amount of infected case and a little bit more negative effect to economic system. However economic system still need a long time to recover in both cases.
BMA708_Assignment 3_ndkvo_520272_COVID-19 outbreak and Burnie economy
This model demonstrates the relationship between the covid-19 outbreak, government policy, and economic impacts. This model was developed based on SIR model (Susceptible, Infection, Recovery). The model also outlines the policies been implemented by the government to cope with Covid-19 pandemic and it also indicate its economic impact.
Interesting insights
This model indicates the government policies have had positive influence on economic impact and it reduce its negative effects on the economy.
