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
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.

 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 System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
8 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 

 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
 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 

 Aquí tenemos un modelo SEIR básico e investigaremos qué cambios serían apropiados para modelar el Coronavirus 2019

Aquí tenemos un modelo SEIR básico e investigaremos qué cambios serían apropiados para modelar el Coronavirus 2019

 This model visualizes the Covid-19 hypothetical transmission at Barangay Busybees, Taytay, Palawan.   Hypothetical   Situation:  Barangay Busybees:  - Has a total population of 500 individual.  - Is a rural area.   - Strictly implemented health protocols.   Conclusion:  Given the population and sit
This model visualizes the Covid-19 hypothetical transmission at Barangay Busybees, Taytay, Palawan.
Hypothetical Situation: Barangay Busybees:
- Has a total population of 500 individual.
- Is a rural area.
- Strictly implemented health protocols.
Conclusion: Given the population and situation, transmission of Covid-19 is observed to be less in earlier stage but once transmission start it became rampant. Despite the rampant transmission, the strict implementation of protocols made recovery of people from Covid 19 possible with 92% recovered from the covid.

 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:
 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 

 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
     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/


 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
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 Na
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



 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
  INTRODUCTION
  

  COVID-19  

 Coronavirus which was named COVID-19 is a
respiratory disease which affects the lungs of the infected person and thus
making such people vulnerable to other diseases such as pneumonia. It was first
discovered in Wuhan China in December 2019 and since then has spread

INTRODUCTION

COVID-19

Coronavirus which was named COVID-19 is a respiratory disease which affects the lungs of the infected person and thus making such people vulnerable to other diseases such as pneumonia. It was first discovered in Wuhan China in December 2019 and since then has spread across the world affecting more than 40 million people from which over one million have died.

In the early discovery of the COVID-19, there were measures that were put in place with the help World Health Organization (WHO). They recommended a social distance of 1.5 meters to 2 meters to curb the spread since the scientist warned that COVID-19 can be carried in the droplets when someone breathes or cough. Another measure which was advised by WHO was wearing of mask, especially when people are in group. Wearing of mask would ensure that someone’s droplets do not leave their mouth or nose when they breathe or cough. It also help one from breathing in the virus which believed to be contagious and airborne.

The World Health Organization also advised on washing of the hand and avoiding frequent touching of the face. People mostly use their hand to touch surfaces which mad their hand the greatest harbor of the disease. Therefore, washing hands with soap will kill and wash away the virus from the hands. Avoiding touching of face also will prevent people from contracting the disease since the virus is believed to enter the body through openings such as eye, nose and mouth.

Another measure as a precaution from contracting the disease was to avoid hand shaking, hugging, kissing and any other thing which would bring people together. These were measures put to ensure that COVID-19 do not move from one person to another because of its airborne nature and the fact that it can be carried from the mouth or nose droplets.

Healthcare workers, in most of the countries, were provided with Personal Protective Equipment (PPEs) which helped them to protect themselves from contracting the virus. Healthcare workers were at the forefront in combating the disease since they were the people receiving the sick, including the ones with the virus. This exposed them to COVID-19 more than anyone hence more care was needed for them. Their PPEs comprised of white overall covering the whole body from head to toes. It also includes face mask and googles worn to prevent anything getting in their eyes. Their hands also were covered with gloves which were removed occasionally to avoid concentration of the virus on one glove.

COVID-19 affected many economies across the world as it greatly affected the human economic activities across the world. Due to the nature and how it spread, COVID-19 lead many countries to lockdown the country as we know it. Travelling was stopped as many countries feared the surge of the virus due to many people travelling form the countries which are already greatly affected. Another reason which travelling was hampered was due to the fact that the virus could spread among the travelers in an airplane. There were no proper measures to ensure social distance in the airplane and many people feared travelling from fear of contracting the disease.

This greatly affected the economy of many countries including great economies like USA. Tourism industry was the one affected the most as many country mostly depend on foreign travelers as their tourist. Many countries do not have proper domestic tourism structure and therefore depend on visitors who travels from foreign countries. Such countries have their economies greatly affected since the earnings from tourism either gone down or was not there at all.

Apart from locking down the country from foreigners, many major cities across the world were under lockdown. This means that even the citizens of the country were neither allowed in or out of the city. This restricted movement of people affecting greatly the human economic activities as many businesses were closed down especially transport businesses. The movement of goods from one places to another was affected making business difficult to carry out. Many people who dealt in perishable agricultural products count losses as their farm produced were destroyed because of lack of wider market. Some countries banned some businesses such as importing second hand clothes since it was believed that they could harbor the virus. Most of the meeting places such as sporting events and pubs were closed down affecting greatly the people who were involved in such businesses.

Across the world, schools were closed. Schools contain students in large numbers which could affect many students across the world. Learning was temporary stopped as different countries were finding ways of curbing the virus.

Scientist are busy like bees across the world to find the vaccine for the diseases that have ravage many countries and above all, they are trying to find the cure. Many countries have carried out their trial of vaccines with the hope to find an effective vaccine for the virus.

Meanwhile it is necessary to find ways by which the virus can be controlled so that it doesn’t spread to a point where it come out of control. Some of the measures put by the WHO has been highlighted above, but these measures need to be studied to ensure that measures which are more effective are affected at great heights. I therefore, have created a model in Insight Maker to check how these measures prove their effectiveness over time.

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.
 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 

 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:
 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:
 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