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A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
Clone of SEIR ABM MODEL
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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 

Clone of Clone of Clone of Clone of Clone of SEIR Infectious Disease Model for COVID-19
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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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Description:

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.

Assumptions:

The government policy is triggered when the number of infected is more than ten.

The government policies will take a negative effect on Covid-19 outbreaks and the financial system.

Parameters:

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameter: Background disease.

Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be changed from vaccination rate.

Government policy's parameters

Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)

Economic's parameters

Fixed parameter: Tourism

Adjusted parameter: Economic growth rate(from 0.3 to 0.5)

Interesting insight

An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.

BMA708_Assignment 3_Nguyen Dang Khoa Vo_520272_COVID-19 outbreak and Burnie economy
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El Salvador
  • Tamaño población inicial: 6,400,000
  • Unidad de cuidados intensivos disponibles: 2000
  • Casos confirmados hasta 13/10/2020: 30,480
  • Casos fallecidos hasta 13/10/2020: 899
Fuente: https://covid19.gob.sv/


CORONAVIRUS EL SALVADOR
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This is the second in a series of models that explore the dynamics of and policy impacts on infectious diseases. This basic SIR model explores the impact of a simple test and isolate policy. The first model can be found here.

Future Learn Basic SIR Model with Sample Testing
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INTRODUCTION

This is a balanced loop model that demonstrates how COVID 19 outbreak in Burnie and the response of the government (e.g. by enforcing health policies: Lockdown; quarantine, non-necessary business closure; border closure) affect the local economy.  This model has 13 positive loops and seven negative loops.  Government response is dependent on the number of reported COVID-19 cases which in turn thought to be dependent on the testing rates less those who recovered from COVID 19 and dead. Economic activity is dependent on the economic growth rate, increased in online shopping, increased in unemployment, number of people who do not obey the rules, COVID 19 cases and health policies.

 ASSUMPTIONS

 · Both infection and economic growth is reduced by enforcing government policies

 · However, the negative effect of government policies is reduced by the number of people who do not obey government health policies

 · Govt policies are enforced when the reported COVID-19 case are 10 or greater.

 ·     Number of COVID cases reported is dependent on the testing rates less those who recovered and dead.

 ·   The higher number of COVID-19 cases have a negative effect on local economy. This phenomena is known as negative signalling. 

 ·   Government policies have a negative effect on economic activity because health policies limit both social and economic activities which directly or indirectly affect the economy in Burnie .  

 ·  This negative effect is somewhat reduced by the increase in online shopping and the number of people who do not obey heath rules.

 INTERESTING INSIGHTS

The test ratings seem to play a vital role in controlling COVID-19 outbreak. Higher Rates of COVID testings decrease the number of COVID 19 deaths and number of infected. This is because higher rates of testing accelerate the government involvement (as the government intervention is triggered earlier, 10 COVID cases mark is reached earlier). Delaying the government intervention by reducing the COVID testing rates increases the death rates and number of infected. 

Increased testing rates allow the figures (deaths, susceptible, infected) to reach a plateau quickly. 





BMA708- Shakila Bethmage- 548351 - COVID 19 Outbreak in Burnie
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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



Clone of Edit Model Penyebaran Covid-19 di Kabupaten Sleman
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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 

Clone of SEIR Infectious Disease Model for COVID-19
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
Clone of SEIR Model for COVID-19 in Indonesia
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Самостаятельная работа часть 1 Акилбеков Асет
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Clone of SEIRSD MODEL
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
Covid-19: SEIR Model for COVID-19 in Indonesia
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The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
Clone of Ph_Covid19SDM_AngelKateCacayan
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The System Dynamics Model presents the the COVID-19 status in Сhina
Clone of Сим Covid-19 in China
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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 

Clone of Clone of Clone of Clone of SEIR Infectious Disease Model for COVID-19
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Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 3.0)
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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 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
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



coba
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Model description: 

This model is designed to simulate the Covid-19 outbreak in Burnie, Tasmania by estimating several factors such as exposed population, infection rate, testing rate, recovery rate, death rate and immunity loss. The model also simulates the measures implemented by the government which will impact on the local infection and economy. 

 

Assumption:

Government policies will reduce the mobility of the population as well as the infection. In addition, economic activities in the tourism and hospitality industry will suffer negative influences from the government measures. However, essential businesses like supermarkets will benefit from the health policies on the contrary.

 

Variables:

Infection rate, recovery rate, death rate, testing rate are the variables to the cases of Covid-19. On the other hand, the number of cases is also a variable to the government policies, which directly influences the number of exposed. 

 

The GDP is dependent on the variables of economic activities. Nonetheless, the government’s lockdown measure has also become the variable to the economic activities. 

 

Interesting insights:

Government policies are effective to curb infection by reducing the number of exposed when the case number is greater than 10. The economy becomes stagnant when the case spikes up but it climbs up again when the number of cases is under control. 

Sample Model of COVID-19 outbreak in Burnie Tasmania by Yim Fong Ng (544885)
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Initial data from:
Italian data [link], as of Mar 28
Incubation estimation [link

Model focuses on outbreak dynamics and control, this version ignores symptom onset to hospital admission and the rest of recovery dynamics.
Italian COVID 19 outbreak control V2
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Proyecto final dinámica de sistemas

Proyecto dinamica
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This basic pandemic model explores the dynamics and healthcare burden associated with of a novel infection.
Clone of Pandemic: Exploring the Dynamics of a Novel Infection