This is an unmitigated model showing the potential spread of COVID-19 across the healthcare system. This effectively shows the worst case scenario without any actions to reduce the spread of the virus.
This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents.
Assumptions:
Government policies will be triggered when reported COVID-19 case are 10 or less;
Government policies reduces the infection and economic growth at the same time.
Interesting Insights:
In the first two weeks, the infected people showed an exponential growth, in another word, that’s the most important period to control the number of people who got affected.
Clone of Model of COVID-19 Outbreak in Burnie, Tasmania
Pada Tugas 3 mata kuliah Pemodelan Transportasi Laut, ditugaskan untuk membuat pemodelan penyebaran COVID-19 di negara yang dipilih, dan pada simulasi ini merupakan negara Indonesia
Dosen Pengampu : Dr.-Ing Ir Setyo Nugroho
Simulasi Pemodelan Penyebaran COVID-19 di Indonesia
An SD model formulated to present the trend of COVID-19 infection and death rate at Puerto Princesa City, PALAWAN using the CESU-PPC file last June 3, 2021.
COVID-19 SD Model of Puerto Princesa City, PALAWAN
This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.
This model is upgraded on the basis of the SIR model and adds more variables.
A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy
Government policy will help control the growth of COVID-19 cases by getting people tested.
BMA708 Model of COVID-19 Outbreak in Burnie island. Ming Liu 501335
Here we have a basic 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.
