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Demo_Group3_COVID-19
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Covid-19 model
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This model aims to show that how Tasmania government's Covid-19 policy can address the spread of the pandemic and in what way these policy can damage the economy.

This model assumes that if the COVID-19 cases are more than 10, the government will take action such as quarantine and lockdown at the area. These policy can indirectly affect the local economy in many different way. At the same time, strict policy may be essential for combating Covid-19.

From the simulation of the model, we can clearly see that the economy of Burine will be steady increase when government successfully reduces the COVID-19 cased and make it spreading slower.

Interesting finding: In this pandemic, the testing rate and the recovery rate are important to stop Covid-19 spreading. Once the cases of Covid-19 less than 10, the government might stop intervention and the economy of Burnie will back to normal.

Model of Covid-19 outbreaks at Burnie (Yingchao Yang,503757)
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Systems Project Stock and Flow
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Modelo SEIR in Covid-19
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Clone of SEIR COVID-19 New Kl. 1
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SEIR Model for COVID-19 in Italy
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A simple Susceptible - Infected - Recovered disease model.
Covid-19 in USA
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HW5 Version 1: Spread of COVID-19 in Cameroon
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Өзіндік жұмысы 1 (жүйелік)
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HW 6 - COVID-19 Simulation
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This model bases on the SIR model aims to indicate the relationship between the lockdown policy of the government for combating with COVID-19 and the economic activity in Burnie Tasmania during the pandemic. 

This model assumes that more COVID-19 cases will lead to the more serious lockdown policy of the local government, which indirectly affect the economic activities and economic growth. The primary reason is that the lockdown policy force people to stay at home and reduce the chance to work and consume.

The simulation trend of the model is that the economy will keep a steady increase when the serious government policy reduces the COVID-19 spreading speed rate.

COVID-19 outbreak in Burnie model by LUJIN 517217
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This Model was developed from the SEIR Model (Susceptible, Exposed, Infected, Recovered) and it predicts the COVID-19 outbreak in Burnie, Tasmania. This pandemic outbreak contributes to diverse rates including infection rate, death rates and recovery rate, government policies and its economic impacts.    

Assumptions:

 This model is driven by its determined rates, e.g., incubation rate, morality rate, test rate and immunity loss rate and its recovery rate.

Government policies are involved in fully vaccination rate, social distance, national border closure, travel, and business restriction which effect Burnie’s economy.

There are three economic entities dimensions in Burnie Island, we can tell that the pandemic has negative impact on Brick-and-Mortar enterprises and tourism business to some extent, whereas, e commercial business plays a crucial role to stimulate the regional economic activities during the COVID-19 period.

 

Interesting Insights:

 The figure of susceptible changes significantly during the initial 3 weeks because of low recovery rate and high infection rate. On the other hand, the implementation and interventions of government policies is effective, because the number of patients who tested negative is increased and the majority of them release and go back home after medical follow-up. 

Xueli Huang 501514, BMA708 Model of COVID-19 Outbreak in Burnie, Tasmania
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Tugas Metodologi Penyelesaian Masalah dan Pemodelan:
Wahyu Abdillah
Ribut Aji Kasmiadi
Faqih Zulfikar
Clone of Revised of COVID-19 S&F PT1 Model
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New SEIR COVID-19
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[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS]

A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply. 

It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases are 10 or less. 

Interesting insights
The infection rate will decline if the government increase the testing ranges, meanwhile,  the more confirmed cases will increase the pressure on hospital capacity and generate more demand for medical resources, which will promote government policy intervention to narrow the demand gap and  affect economic performance by increasing hospital construction with financial investment.

The Model of COVID-19 Pandemic Outbreak in Burnie, TAS
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Covid-19 TAED
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The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.
Influenza and SARS-CoV-2 interaction v1
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Muertes por COVID-19
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Spring, 2020:

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-6, we recover their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:
  * http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb

  * http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA-with-Flattening.nb

Key for Lab SIR 2 -- Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) Model for Coronavirus
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This model estimates the deaths due to COVID19 in Bangalore City. 
Assumptions:
City has a population = 8 Million
Initial infected population = 10
Probability of infection = 8%
Contact rate in population = 6
Average duration of recovery = 10 days
Death rate = 1%
Quarantine rate = 80%
Delay in quarantine = 5 days
COVID-19_SIR_MODEL_No_Quarantine
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COVID-19 Model Indonesia