​Modelo epidemiológico simples   SIR: Susceptíveis - Infectados - Recuperados         Clique aqui  para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.          Dados diários sobre  in
​Modelo epidemiológico simples
SIR: Susceptíveis - Infectados - Recuperados

Clique aqui para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.


Dados diários sobre infectadosrecuperados e óbitos para diversos países (incluindo o Brasil) podem ser obtidos aqui neste site
Dados diários para o município de Juiz de Fora podem ser obtidos no site da Prefeitura.
This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.  This model is based on the SIR, Susceptible,
This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.

This model is based on the SIR, Susceptible, Infection, Recovery (or Removed) These are the three possible states related to the members of the Burnie population when a contagious decease spreads.

The Government/Government Health Policy, played a big part in the successful decrease in Covid-19 infections. The Government enforced the following.
- No travel (interstate or international)
- Isolation within the residents homes
- Social distancing by 1.5m
- Quarantine
- Non essential companies to be temporarily closed
- Limitations on public gatherings
- And limits on time and kilometers aloud to travel from ones home within a local community

This resulted in lower reported infection rates of Covid-19 and higher recovery rates.

In my opinion:
When the first case was reported the Government could have been even faster to enforce these rules to decrease the fatality rates further for the Burnie, population.  

Assumption: Government policies were only triggered when 10 cases were recorded.
Also, more cases that had been recorded effected the economic growth during this time.

Interesting Findings: In the simulation it shows as the death rates increases towards the end of the week, the rate of testing goes down. You would think that the government would have enforced a higher testing rate over the duration of this time to decrease the number of infections, exposed which would increase the recovery rates faster and more efficiently.  

Figures have been determined by the population of Burnie being 19,380 at the time of assignment.

 Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

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

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

  COVID-19 outbreak in Burnie Tasmania Simulation Model         Introduction        This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community.  Government responses are based on the reported COVID-19 cases amount, whcih is considered to be
COVID-19 outbreak in Burnie Tasmania Simulation Model

Introduction

This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community.  Government responses are based on the reported COVID-19 cases amount, whcih is considered to be based on testing rate times number of people who are infected minus those recovered from COVID-19 and dead.
Government interventions include the implement of healthy polcy, border surveillance, quarantine and travel restriction. After outbreak, economic activities are positively affected by the ecommerce channel development and normal economic grwoth, while the unemployement rate unfortunately increases as well. 

Assumption
  • Enforcing government policies reduce both infection and economica growth.                        
  • When there are 10 or greater COVID-19 cases reported, the governmwnt policies are triggered.                                                          
  • Greater COVID-19 cases have negatively influenced the economic activities.                    
  • Government policies restict people's activities socially and economically, leading to negative effects on economy.                                          
  • Opportunities for jobs are cut down too, making umemployment rate increased.           
  • During the outbreak period, ecommerce has increased accordingly because people are restricted from going out.                                  
Interesting insights

An increase in vaccination rate will make difference on reduing the infection. People who get vaccinated are seen to have higher immunity index to fight with COVID-19. Further research is needed.

Testing rate is considered as critical issue to reflect the necessity of government intervention. Higher testing rate seems to boost immediate intervention. Reinforced policies can then reduce the spread of coronvirus but absoluately have negative impacts on economy too.
  ABOUT THE MODEL   This is a dynamic model that shows the correlation between the
health-related policies implemented by the Government in response to COVID-19 outbreak
in Burnie, Tasmania, and the policies’ impact on the Economic activity of the
area.   

   ASSUMPTIONS  

 The increase in the num

ABOUT THE MODEL

This is a dynamic model that shows the correlation between the health-related policies implemented by the Government in response to COVID-19 outbreak in Burnie, Tasmania, and the policies’ impact on the Economic activity of the area.

 ASSUMPTIONS

The increase in the number of COVID-19 cases is directly proportional to the increase in the Government policies in the infected region. The Government policies negatively impact the economy of Burnie, Tasmania.

INTERESTING INSIGHTS

1. When the borders are closed by the government, the economy is severely affected by the decrease of revenue generated by the Civil aviation/Migration rate. As the number of COVID-19 cases increase, the number of people allowed to enter Australian borders will also decrease by the government. 

2. The Economic activity sharply increases and stays in uniformity. 

3. The death rate drastically decreased as we increased test rate by 90%.


 If no attempt is made to eradicate SARS-CoV-2 it will eventually
become endemic, ineradicable, at a high never-ending cost to world in terms of economic
growth, human health and lives. The current strategy adopted by most
governments is to impose  restrictive
measures when the virus threatens to ov

If no attempt is made to eradicate SARS-CoV-2 it will eventually become endemic, ineradicable, at a high never-ending cost to world in terms of economic growth, human health and lives. The current strategy adopted by most governments is to impose  restrictive measures when the virus threatens to overwhelm hospital services and to relax these restrictions as this danger recedes. This is short-sighted. It cannot eliminate the highly infectious delta variant, which has an estimated R0-value of between 6 & 9. Periodic lockdowns will be hard to avoid in the future.

However, eradication is possible, herd immunity can be achieved quickly worldwide, reducing the R0 permanently to below 1, which will lead to the disappearance of the virus. Critical in achieving this is Ivermectin, a medicine that is cheap,  readily available and can be manufactured by most countries. A recent meta study has shown that Ivermectin, prophylactically employed, can prevent infection with the virus  by 86 % on average – very similar to the efficacy of vaccines. Eradication will require employment of all the instruments shown in the graph: future generations do not have to live with this plague. 

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.
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.
  Overview:
  

 The
COVID-19 Outbreak in Burnie Tasmania shows the process of COVID-19 outbreak,
the impacts of government policy on both the COVID-19 outbreak and the GDP
growth in Burnie.  

  Assumptions:  

 We set some
variables at fix rates, including the immunity loss rate, recovery rate, de

Overview:

The COVID-19 Outbreak in Burnie Tasmania shows the process of COVID-19 outbreak, the impacts of government policy on both the COVID-19 outbreak and the GDP growth in Burnie.

Assumptions:

We set some variables at fix rates, including the immunity loss rate, recovery rate, death rate, infection rate and case impact rate, as they usually depend on the individual health conditions and social activities.

It should be noticed that we set the rate of recovery, which is 0.7, is higher than that of immunity loss rate, which is 0.5, so, the number of susceptible could be reduced over time.

Adjustments: (please compare the numbers at week 52)

Step 1: Set all the variables at minimum values and simulate

results: Number of Infected – 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.

Step 2: Increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate

results: Number of Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).

So, the increase of health policy, quarantine and travel restriction will help increase recovery, decrease confirmed cases, decrease death, but also decrease GDP.

Step 3: Increase the variables of Testing Rate to 0.4, others keep the same as step 2, and simulate

results: Number of Infected – 152 (down); Recovered – 243 (down); Cases – 1022 (up); Death – 17,625 (down); GDP – 824 (same).

So, the increase of testing rate will help to increase the confirmed cases.

Step 4: Change GDP Growth Rate to 0.14, Tourism Growth Rate to 0.02, others keep the same as step 3, and simulate

results: Number of Infected – 152 (same); Recovered – 243 (same); Cases – 1022 (same); Death – 17,625 (same); GDP – 6,632 (up).

So, the increase of GDP growth rate and tourism growth rate will helps to improve the GDP in Burnie.

  LEIA ANTES DE COMEÇAR   Milhões de pessoas ao redor do mundo estão em QUARENTENA em função da pandemia COVID-19. Se adaptar à quarentena pode ser um PROBLEMA para muitas pessoas.   Nosso DESAFIO é construir um DIAGRAMA CAUSAL que analise este PROBLEMA que é ficar em quarentena. Vamos lá!?       PR

LEIA ANTES DE COMEÇAR

Milhões de pessoas ao redor do mundo estão em QUARENTENA em função da pandemia COVID-19. Se adaptar à quarentena pode ser um PROBLEMA para muitas pessoas.

Nosso DESAFIO é construir um DIAGRAMA CAUSAL que analise este PROBLEMA que é ficar em quarentena. Vamos lá!?


PRIMEIRA TAREFA (até dia 13 de maio)

1) Qual a variável CHAVE que você acha que pode definir o problema? Crie uma VARIÁVEL dentro do folder CHAVE.

2) Quais as outras variáveis SECUNDÁRIAS que estão relacionadas com este problema? Crie variáveis secundárias dentro dos FOLDER que melhor identifica o tipo da variável.


SEGUNDA TAREFA

No dia 15 de maio discutiremos virtualmente no Zoom, as variáveis propostas e faremos um DIAGRAMA CAUSAL RASCUNHO.


TERCEIRA TAREFA

No dia 22 de maio discutiremos virtualmente Zoom, o DIAGRAMA CAUSAL RASCUNHO objetivando construir o DIAGRAMA CAUSAL DEFINITIVO.

Италиядағы COVID-19 экосистемасы
Италиядағы COVID-19 экосистемасы
 Model of Covid-19 outbreak in Burnie, Tasmania     This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.     Assumptions     The government policy is triggered when number of infected is mor
Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.

Assumptions

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

The government policies will take negative effect into Covid-19 outbreaks and financial system

Parameters

We set some fixed and adjusted variables.
Covid-19 outbreak's parameter
Fixed parameters: Infection rate, Background disease, recovery rate.
Adjusted parameter: Immunity loss rate can be change 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

Increase vaccination rate and testing rate will decrease the number amount of infected case and a little bit more negative effect to economic system. However economic system still need a long time to recover in both cases.
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.
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.
 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
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
This model demonstrates the relationship between the covid-19 outbreak, government policy, and economic impacts. This model was developed based on SIR model (Susceptible, Infection, Recovery). The model also outlines the policies been implemented by the government to cope with Covid-19 pandemic and
This model demonstrates the relationship between the covid-19 outbreak, government policy, and economic impacts. This model was developed based on SIR model (Susceptible, Infection, Recovery). The model also outlines the policies been implemented by the government to cope with Covid-19 pandemic and it also indicate its economic impact. 
Interesting insights
This model indicates the government policies have had positive influence on economic impact and it reduce its negative effects on the economy.
 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
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
 Modelo epidemiológico simples   SIR: Susceptíveis - Infectados - Recuperados        Dados iniciais do Brasil em 04 Abr 2020    Fonte:   https://www.worldometers.info/coronavirus/country/brazil/
Modelo epidemiológico simples
SIR: Susceptíveis - Infectados - Recuperados

Dados iniciais do Brasil em 04 Abr 2020
 Modelo epidemiológico simples   SIR: Susceptíveis - Infectados - Recuperados         Clique aqui  para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.          Dados iniciais de  infec
Modelo epidemiológico simples
SIR: Susceptíveis - Infectados - Recuperados

Clique aqui para ver um vídeo com a apresentação sobre a construção e uso deste modelo.  É recomendável ver o vídeo num computador de mesa para se poder ver os detalhes do modelo.


Dados iniciais de infectados, recuperados e óbitos para diversos países (incluindo o Brasil) podem ser obtidos aqui neste site.
This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.
 Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.