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.

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.

Данная модель отражает распространение COVID-19 в России на основе статистики за 2020 год. Модель построена в среде Insight Maker по типу SEIRD (Susceptible–Exposed–Infected–Recovered–Dead), с упрощённой динамикой.

Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.

This Model described the outbreak simulation under government policy and impacts on Economics.

Model based on several references:
1. https://insightmaker.com/insight/4iVOp2JcrDSTBvqjER7pxM/TA-Pemsim-SEIR-Covid-19-Model
2. https://insightmaker.com/insight/5GiU0WZLpKCLGOoe6xeIhT/SEIR-COVID-19-New-Kl-1
3. https://insightmaker.com/insight/DaOeZ0N9RcgU1Q87ofIj8/COVID-19-SEIR-Model-for-Indonesia

Locus set on Indonesia, during 2021

This model shows an SIR model of COVID-19 infection in the Philippines. The data used in this model are recent data from COVID-19 statistics reports this 2022. The format of this Philippine COVID-19 model is guided by an Infection Model developed by martin.

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy.