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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
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COVID-19 Model Indonesia
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2 өзіндік
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COVID-19 in Japan 2020 самостоятельная работа
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Simple epidemiological model for Burnie, Tasmania
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts  

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model,  will stop for next 52 weeks).
Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)
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Model ini dirancang untuk membuat model tentang penyebaran Covid-19 dan vaksinasi di Kabupaten Sleman pada November 2022

Model ini dibuat untuk memenuhi tugas kelompok dari matakuliah Metode Penyelesaian Masalah dan Pemodelan, atas nama :
Sabilla Halimatus Mahmud
Nurul Widyastuti
Muhammad Najib



Edit Model Penyebaran Covid-19 di Kabupaten Sleman
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Италиядағы COVID-19 экосистемасы
Жаңа идея
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This Systemigram illustrates how the world fought against COVID-19.
COVID-19 Systemigram
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Evolution of Covid-19 in Brazil:
A System Dynamics Approach

Villela, Paulo (2020)
paulo.villela@engenharia.ufjf.br

This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.

Evolução da Covid-19 no Brasil
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This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City, PALAWAN using the June 3, 2021 data of the CESU-PPC.
COVID-19 ABM (SIR) Model of Puerto Princesa City, PALAWAN
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

SEIRD 01: COVID-19 spread
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This insight began as a March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the original insight. The following links are theirs:

Initial data from:
Italian data [link] (Mar 4)
Incubation estimation [link]

Andy Long
Northern Kentucky University
May 2nd, 2020

This is an update of our model from April 9th, 2020. As we prepare for our final exam, I read a story in The Guardian about Italy's struggle to return to normalcy. The final paragraphs:

During the debate in the Senate on Thursday, the opposition parties grilled Conte. Ex-prime minister Matteo Renzi, who has called for less restraint in the reopening, remarked, “The people in Bergamo and Brescia who are gone, those who died of the virus, if they could speak, they’d tell us to relaunch the country for them, in their honour.”

Renzi’s controversial statement was harshly criticised by doctors who warned that the spread of the disease, which, as of Thursday, had killed almost 30,000 people in the country and infected more than 205,000 [ael: my emphasis], was not over and that a misstep could take the entire country back to mid-March coronavirus levels.

“We risk a new wave of infections and outbreaks if we’re not careful,” said Tullio Prestileo, an infectious diseases specialist at Palermo’s Benefratelli Hospital. “If we don’t realise this, we could easily find ourselves back where we started. In that case, we may not have the strength to get back up again.”

I have since updated the dataset, to include total cases from February 24th to May 2nd. I went to Harvard's Covid-19 website for Italy  and and then to their daily updates, available at github. I downloaded the regional csv file for May 2nd,  which had regional totals (21 regions); I grabbed the column "totale_casi" and did some processing to get the daily totals from the 24th of February to the 2nd of May.

The cases I obtained in this way matched those used by Gabo HN.

The initial data they used started on March 3rd (that's the 0 point in this Insight).

You can get a good fit to the data through April 9th by choosing the following (and notice that I've short-circuited the process from the Infectious to the Dead and Recovered). I've also added the Infectious to the Total cases.

The question is: how well did we do at modeling this epidemic through May 2nd (day 60)? And how can we change the model to do a better job of capturing the outbreak from March 3rd until May 2nd?

Incubation Rate:  .025
R0: 3
First Lockdown: IfThenElse(Days() == 5, 16000000, 0)
Total Lockdown: IfThenElse(Days() >= 7, 0.7,0)

(I didn't want to assume that the "Total Lockdown" wasn't leaky! So it gets successively tighter, but people are sloppy, so it simply goes to 0 exponentially, rather than completely all at once.)

deathrate: .01
recoveryrate: .03

"Death flow": [deathrate]*[Infectious]
"Recovery flow": [recoveryrate]*[Infectious]

Total Reported Cases: [Dead]+[Surviving / Survived]+[Infectious]



Resources:
  * https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported
Final Version of Italian COVID-19 outbreak
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Агентное моделирование по COVID-19
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Pemodelan Covid-19 di Indonesia
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COVID-19 Week 7
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Tugas 3_Thamara Shaifa Anwar_0441174000035_Pemodelan Transportasi Laut

Dosen Pengampu : Dr-Ing Ir. Setyo Nugroho
Pemodelan COVID-19 di Indonesia
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SEIR Model_John
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Introduction:
This model demonstrates the COVID-19 outbreak in Burnie, Tasmania. It shows how the government policy tries to reduce the spread of COVID-19 whilst also impacting the local economy.

Assumptions:
This model has four variables that influence the number of COVID-19 cases: infection rate, immunity loss rate, recovery rate and death rate.

In order to reduce the pandemic spread, in this model, assume the government released six policies when Burnie COVID-19 cases are equal or over 10 cases. Policies are vaccination promotion, travel restriction to Burnie, quarantine, social distance, lockdown and testing rate.

Government policies would reduce the pandemic. However, it decreases economic growth at the same time. In this model, only list three variable that influence local economic activities. 
Travel restrictions and quarantine will reduce Burnie tourism and decrease the local economy. On the other hand, quarantine, social distance, lockdown allow people to stay at home, increasing E-commerce business.
As a result, policies that cause fewer COVID-19 cases also cause more considerable negative damage to the economy.

Interesting insights:
One of the interesting findings is that the government policy would reduce the COVID-19 spread significantly if I adjust the total government policies are over 20% (vaccine promotion, travel restriction, quarantine, social distance, lockdown), 3560 people will die, then no more people get COVID-19.
However, if I change the total government policy to less than 5%, the whole Burnie people will die according to the model. Therefore, we need to follow the polices, which saves our lives.
BMA708 assignment3 - Model of COVID-19 outbreak in Burnie
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Самостоятельная работа COVID-19 2023г.
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Өзіңдік жұмыс дұрысы
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Story Telling COVID19
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Muertes por COVID-19