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.
  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%.


 Spring, 2020:       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   https://www.maa.org/press/periodicals/loci/joma/the-sir-mod
Spring, 2020:

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=1.27x10-6, we recover 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:
 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 can be used to investigate how government interventions affect transmission and mortality associated with COVID-19 during an outbreak, and how these interventions impact on the economic activities in Burnie, Tasmania.     Assumptions can be made that effective government intervention can
This model can be used to investigate how government interventions affect transmission and mortality associated with COVID-19 during an outbreak, and how these interventions impact on the economic activities in Burnie, Tasmania.

Assumptions can be made that effective government intervention can reduce the number of people infected, whereas the local economy is severely impacted.

Insights:
1. When COVID-19 case are more than 10, government policy will be triggered.

2. Testing rate is very crucial to understanding the spread of the pandemic and responding appropriately.


 Wenn kein Versuch unternommen wird, SARS-CoV-2 auszurotten, wird es
schließlich endemisch und unausrottbar werden, mit hohen, nicht endenden Kosten
für die Welt in Bezug auf Wirtschaftswachstum, menschliche Gesundheit und
Menschenleben. Die derzeitige Strategie der meisten Regierungen besteht darin

Wenn kein Versuch unternommen wird, SARS-CoV-2 auszurotten, wird es schließlich endemisch und unausrottbar werden, mit hohen, nicht endenden Kosten für die Welt in Bezug auf Wirtschaftswachstum, menschliche Gesundheit und Menschenleben. Die derzeitige Strategie der meisten Regierungen besteht darin, restriktive Maßnahmen zu ergreifen, wenn das Virus die Krankenhäuser zu überschwemmen droht und diese Beschränkungen wieder zu lockern, wenn diese Gefahr zurückgeht. Diese Strategie kann die hochinfektiöse Delta-Variante, die einen geschätzten R0-Wert von 6 bis 9 hat, nicht eliminieren. Regelmäßige Sperrungen werden sich in Zukunft kaum vermeiden lassen.

Eine Ausrottung ist jedoch möglich, ebenso wie eine weltweit schnell erreichte Herdenimmunität, die den R0-Wert dauerhaft auf unter 1 senkt und somit zum Verschwinden des Virus führen wird. Entscheidend dafür ist Ivermectin, ein Medikament, das billig und leicht verfügbar ist und in den meisten Ländern hergestellt werden kann. Eine kürzlich durchgeführte Metastudie hat gezeigt, dass die prophylaktische Anwendung von Ivermectin eine Infektion mit dem Virus im Durchschnitt um 86 % verhindern kann, was der Wirksamkeit von Impfstoffen sehr ähnlich ist. Die, die nicht geimpft wurden und nicht immun sind, weil sie Covid-19 überstanden haben, könnten sich mit Ivermectin sehr effektiv vor einer Infektion schützen.  Die Ausrottung erfordert den Einsatz aller in der Grafik gezeigten Instrumente: künftige Generationen könnte es erspart bleiben mit dieser Plage leben zu müssen.  

Check how different times of recovery and deths in cases of covid-19 infulence 2 key mortality indicators: Overall mortalityr ate (ratio of all deaths to all cases)  Resolved cases mortality rate (ratio of all deaths to recovered cases)     Assumed delays are:  5 weeks for recovery cases  2 weeks fo
Check how different times of recovery and deths in cases of covid-19 infulence 2 key mortality indicators:
Overall mortalityr ate (ratio of all deaths to all cases)
Resolved cases mortality rate (ratio of all deaths to recovered cases)

Assumed delays are:
5 weeks for recovery cases
2 weeks for death cases
Delays are built into conveyor stocks, so cannot be adjusted by slider

keep in mind Insigth uses similar but made-up numbers and linear flow of new cases (in opposition to exponential in real world)  
 This is the third in a series of models that explore the dynamics of infectious diseases. This model looks at the impact of two types of suppression policies.      Press the simulate button to run the model with no policy.  Then explore what happens when you set up a lockdown and quarantining polic
This is the third in a series of models that explore the dynamics of infectious diseases. This model looks at the impact of two types of suppression policies. 

Press the simulate button to run the model with no policy.  Then explore what happens when you set up a lockdown and quarantining policy by changing the settings below.  First explore changing the start date with a policy duration of 60 days.
 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.

 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.

 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
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).
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 description:   This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy.     More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death
Model description:
This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy. 

More importantly, the impact of governmental responses to both Covid-19 infection and to local economy, the impact of death cases to local economy are illustrated. 

The model is based on SIR (Susceptible, Infected and recovered) model. 

Variables:
The simulation takes into account the following variables: 

Variables related to Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate. 

Variables related to Governmental policies: (1): Vaccination mandate. (2): Travel restriction to Burnie. (3): Economic support. (4): Gathering restriction.

Variables related to economic growth: Economic growth rate. 

Adjustable variables are listed in the part below, together with the adjusting range.

Assumptions:
(1): Governmental policies are aimed to control(reduce) Covid-19 infections and affect (both reduce and increase) economic growth accordingly.

(2) Governmental policy will only be applied when reported cases are 10 or more. 

(3) The increasing cases will negatively influence Burnie economic growth.

Enlightening insights:
(1) Vaccination mandate, when changing from 80% to 100%, doesn't seem to affect the number of death cases.

(2) Governmental policies are effectively control the growing death cases and limit it to 195. 

 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 

 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:
 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
O presente  Insight  engloba diversos tipos de modelos compartimentais. Pra visualizar alguns deles, procure testar os seguintes valores: SI: S=995, I=5, β=0.1 SIS: S=980, I=20, β=0.1 e δ = 0.01 SIR: S=995, I=5, β=0.35 e γ=0.035 SIRS: S=995, I=5, β=0.4, γ=0.2 e μ=0.005 SEIR: S=995, I=5, β=0.5, ω=0.1
O presente Insight engloba diversos tipos de modelos compartimentais.
Pra visualizar alguns deles, procure testar os seguintes valores:
SI: S=995, I=5, β=0.1
SIS: S=980, I=20, β=0.1 e δ = 0.01
SIR: S=995, I=5, β=0.35 e γ=0.035
SIRS: S=995, I=5, β=0.4, γ=0.2 e μ=0.005
SEIR: S=995, I=5, β=0.5, ω=0.1 e γ=0.1
SEIRS: S=995, I=5, β=0.5, ω=0.1, γ=0.1 e μ=0.03.
SIRV:  S=995, I=5, β=0.35, γ=0.035 e ν=0.01

Note que este é um Insight que pode ser modificado para mostrar cada um desses modelos e o usuário deverá tornar alguns fluxo nulos afim de manter apenas as conexões essenciais para cada sistema.
    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 loo

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. 





 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 

[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS]   A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply.      It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases
[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS]

A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply. 

It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases are 10 or less. 

Interesting insights
The infection rate will decline if the government increase the testing ranges, meanwhile,  the more confirmed cases will increase the pressure on hospital capacity and generate more demand for medical resources, which will promote government policy intervention to narrow the demand gap and  affect economic performance by increasing hospital construction with financial investment.

 The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022.         Total population of Brgy. Sicsican - 22625    Total Covid19 cases as of May 27, 2022 - 250    Local transmission - 241    Imported transmission - 9    Recovery - 226    Death Due
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
6 months ago
 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