Insight diagram
Assignment 3 Norway Covid-19
C. T
Insight diagram
Germany COVID-19
sanzhgod
8 months ago
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
Coronavirus BMA708 UTAS Tasmania Burnie Economy Government SIR Model COVID-19
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)
Yue Xiang
5
Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 
COVID-19 Infection Coronavirus Epidemics Health Care

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

SEIR Infectious Disease Model for COVID-19
Steven D'Alessandro
This Model described the outbreak simulation under government policy and impacts on Economics. Assumptions  The social distance policy can reduce 80% of infection. Interesting Insights The story tell the difference when social distance applied or not Click on View story to star
Burnie Tasmania COVID-19
This Model described the outbreak simulation under government policy and impacts on Economics.

Assumptions 
The social distance policy can reduce 80% of infection.

Interesting Insights
The story tell the difference when social distance applied or not

Click on View story to start simulations

BMA708 Task 3 Zijing Zeng 520737
Zijing Zeng
Insight diagram
COVID -19 ABM MODEL
Aaditya
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 t
Tasmania Health COVID-19 Burnie Economy BMA 708

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
NGUYEN DANG KHOA VO
Somulacion clase 2, retroalimentación + y - , primer versión
Simulacion Salud
Somulacion clase 2, retroalimentación + y - , primer versión
Modelo Covid-19 Co
brow
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.
COVID-19 Seir Coronavirus
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.
SEIR ABM MODEL
Remigiusz Kinas
20
Insight diagram
Covid-19 Pandemic
Sneha Tirchy Shekar
Insight diagram
SYSTEMS DYNAMICS OF COVID-19
AL JOHN D. CENA
Insight diagram
Pemodelan Covid-19 di Indonesia
Jihan Widya Candra
Insight diagram
Өзіндік жұмыс 2
Аружан Серік
13
Insight diagram
COVID-19
Аяулым Халыктай
12 months ago
Insight diagram
System dynamics covid-19 in India
Janelle
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
COVID-19 Burnie Economy
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)
Mika yang
The Covid-19 pandemic has introduced a variety of novel and intense difficulties, from dealing with the production network for individual defensive gear (PPE) to changing labor force ability to adapting to monetary misfortune. Amidst these difficulties lies a chance for medical services pioneers to
Health Care
The Covid-19 pandemic has introduced a variety of novel and intense difficulties, from dealing with the production network for individual defensive gear (PPE) to changing labor force ability to adapting to monetary misfortune. Amidst these difficulties lies a chance for medical services pioneers to more readily position and change their associations for an eventual fate of unusual amazement. To oversee limit, monetary misfortune, and care overhaul, medical services associations have settled on the basic choice to deliver or lessen labor force or to move numerous representatives to far off work, incorporating clinicians working with telehealth advances. (www.catalyst.nejm.org)


Reference:
Begun, J.W. PhD, Jiang, J.H, PhD,. (2020, October 9). NEJM Catalyst/Innovations in Care Delivery. Health Care Management During Covid-19: Insights from Complexity Science. Retrieved from https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0505

Covid-19 Health Care Complexities and Variables
Taleisha Y. Alcorn
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover Assumptions The government has reduced both the e
COVID-19
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
The government has reduced both the epidemic and economic development by controlling immigration.




Yuhao c, BMA708_Marketing insights into Big Data.
Yuhao Chen
Insight diagram
Epidemic Health
COVID-19 in Jakarta
Rizky Alif Ramadhan
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 2023.
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 2023.
Covid -19 Model
Cyndy Loraine Palay
Insight diagram
COVID-19 Systemigram Model Building Exercise
Josiah Best
Insight diagram
Group_3_7BUIS021W_MODELS-Impact of COVID-19 on civil aviation_CausalLoop
Dharani Goli
SARS-CoV-19 spread  in different countries - please  adjust variables accordingly Italy elderly population (>65): 0.228 estimated undetected cases factor: 4-11 starting population size: 60 000 000 high blood pressure: 0.32 (gbe-bund) heart disease: 0.04 (statista) free intensive
Disease Coronavirus Infection Corona Virus COVID-19 SARS-CoV-19 Pandemic
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
SARS-CoV-19 model
Lucia Vega Resto
76
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 pol
UTAS Model BMA708 COVID-19
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
LU JIN