Brief of the model:

The model predicts the outbreak of COVID-19 in the Burnie, Tasmania area. It is imperative to clarify that this model was developed from the SEIR model (Susceptible, Infected, Infected, Recovered). The spread of this pandemic is driven by a combination of infection rates, mortality rates, and recovery rates from the virus itself, as well as government policies.

For COVID-19 itself, vaccination directly reduces the infection rate, thereby reducing the mortality rate of COVID-19 patients and the reduction of confirmed cases. In other words, if the local population is adequately vaccinated, everyday life, shopping, tourism, and even national borders will be open rather than in a closed border situation.

Assumption of the model:

The model simulated based on different rates, including Infecting rate, Death Rate, Test Rate, Immunity Loss Rate and Recovery Rate. And, this model lists six elements of government policy, which including border closure, travel ban, social distancing, business restriction, self-quarantine, and vaccination schedule.

Besides, the model considers three economic entities in the Burnie area, one in the brick-and-mortar industry and online business industry. Government policies have somewhat reduced COVID-19 infections. Still, they have also at the same time, online businesses played an essential role in stimulating local economic activity during the pandemic. At the same time, however, online businesses played an indispensable role in promoting regional economic activity during the pandemic.

The prediction model is for reference only, and there may be differences between the actual cases and the model.

Insights of the model:

Due to the high infection and low recovery rates and timely government policy interventions, the number of susceptible individuals changes dramatically in the first four weeks. However, the number of sensitive individuals continues to decline after this period, but the decline is not significant. Secondly, with the implementation of government policies, the number of suspected patients who tested negative for medical follow-up continued to rise, implying that government policy interventions directly affect COVID-19.

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. 

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