Modelo epidemiológico simples   SIR: Susceptíveis -Infectados - Recuperados        Ajuste os PARÂMETROS abaixo.  Clique em SIMULATE no menu superior para simular.  Dados iniciais de 04 Abr 2020  Fonte:  https://www.worldometers.info/coronavirus/country/brazil/
Modelo epidemiológico simples
SIR: Susceptíveis -Infectados - Recuperados

Ajuste os PARÂMETROS abaixo.
Clique em SIMULATE no menu superior para simular.
Dados iniciais de 04 Abr 2020
 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
 Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.      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
Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

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

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce 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:
     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/

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/


 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
   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 .
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.

 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
     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

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.

Collapse of the economy, not just recession, is now very likely. To give just one possible cause,
in the U.S. the fracking industry is in deep trouble. It is not only that most
fracking companies have never achieved a   free cash flow   (made a profit)
since the fracking boom started in 2008, but th
Collapse of the economy, not just recession, is now very likely. To give just one possible cause, in the U.S. the fracking industry is in deep trouble. It is not only that most fracking companies have never achieved a free cash flow (made a profit) since the fracking boom started in 2008, but that  an already very weak  and unprofitable oil industry cannot cope with extremely low oil prices. The result will be the imminent collapse of the industry. However, when the fracking industry collapses in the US, so will the American economy – and by extension, probably, the rest of the world economy. To grasp a second and far more serious threat it is vital to understand the phenomenon of ‘Global Dimming’. Industrial activity not only produces greenhouse gases, but emits also sulphur dioxide which converts to reflective sulphate aerosols in the atmosphere. Sulphate aerosols act like little mirrors that reflect sunlight back into space, cooling the atmosphere. But when economic activity stops, these aerosols (unlike carbon dioxide) drop out of the atmosphere, adding perhaps as much as 1° C to global average temperatures. This can happen in a very short period time, and when it does mankind will be bereft of any means to mitigate the furious onslaught of an out-of-control and merciless climate. The data and the unrelenting dynamic of the viral pandemic paint bleak picture.  As events unfold in the next few months,  we may discover that it is too late to act,  that our reign on this planet has, indeed,  come to an abrupt end?  
     Model introduction    This is an SIR model that simulates the potential COVID outbreak that can happen in Burnie, Tasmania after the positive case reported on October 2nd 2021, which incorporates three parts: Susceptible – Infectious – Recovered Looping model, government’s health policy that wi

Model introduction 

This is an SIR model that simulates the potential COVID outbreak that can happen in Burnie, Tasmania after the positive case reported on October 2nd 2021, which incorporates three parts: Susceptible – Infectious – Recovered Looping model, government’s health policy that will affect each phase of the SIR process, and the potential economy that will affect people’s behaviours and thus influence the effectiveness of government’s public policy. 

 

For instance, the values of variables deciding the inflection rate are influenced by actions taken to control the situation, such as through the quarantine of those infected, social distancing, travel bans, and personal isolation and protection strategies. Conversely, the magnitude of the problem at various points in time will also influence the magnitude of the response to control the situation. 

 

Assumptions

1. The population is assumed to be homogeneous and well-mixed. And there is no significant change on the total population due to births and deaths.

2. Once lockdown is lifted, no further imported cases are assumed to occur.

3. Super spreader events are not explicitly considered. 

4. The interaction among states is assumed to be implicit. 

5. All confirmed cases would go to quarantine, and 90% of their contacts can be traced.

6. Contact tracing and testing capacity is sufficient.


Insights

Ideally, both one-way scenario analysis and two-way scenario analysis (amount change in one/two variables each time) will be conducted to find out the variable that has the greatest impact on getting new cases. Insights below can be gained:

 

1.What happens if people are more/less likely to pass on infection, through washing their hands and sneeze into their elbows (infection rate affected by people’s behaviours that will further induced by government’s policies)

2. How vaccination rate will affect the development of positive cases 

3. What if the structure of the contact network changes (extent to which school, workplace and restaurants is shut down) 

4. How growth rate is sensitive to the duration of illness and probability of infection

Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.    Modified by Rio dan Pras
Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.

Modified by Rio dan Pras
 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 

This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world. The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at w
This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.
The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.
  Overview:   Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.      Variables   The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): D

Overview:

Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.


Variables

The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc. 


Assumptions:

From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.


In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.

Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.


Step 1: Try to set all value variables at their lowest point and then stimulate. 

 

Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.


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


Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).


With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.


Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate


Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.


In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.


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


Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632. 


This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.

 ​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 shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.    It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10.      Government interventi
This model shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.

It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10. 

Government intervention - lock down the state, suppress the development of COVID-19 effectively. It is related to most of people stay at home to reduce the exposure in public area.
On the other hand, it also bring the economy of Burnie in the recession, as no tourists, no dining out activities and decrease in money spending in the city.
  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.
 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.
12 months ago
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.

 This is the first in a series of models that explore the dynamics of and policy impacts on infectious diseases. This basic  model divides the population into three categories -- Susceptible (S), Infectious (I) and Recovered (R).       Press the simulate button to run the model and see what happens
This is the first in a series of models that explore the dynamics of and policy impacts on infectious diseases. This basic  model divides the population into three categories -- Susceptible (S), Infectious (I) and Recovered (R).  

Press the simulate button to run the model and see what happens at different values of the Reproduction Number (R0).

The second model that includes a simple test and isolate policy can be found here.
11 months ago
 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.