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.
 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
 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 

9 months ago
 This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.      This model is upgraded on the basis of the SIR model and adds more variables.      A large number of COVID-19 cases w
This model is to explain the COVID-19 outbreak in Brunie Island, Tasmania, Australia, and the relationship between it and the government policies , also with the local economy.

This model is upgraded on the basis of the SIR model and adds more variables.

A large number of COVID-19 cases will have a negative impact on the local economy. But if the number of cases is too small, it will have no impact on the macro economy

Government policy will help control the growth of COVID-19 cases by getting people tested.


 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)        Germany
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)

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)

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

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
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly)
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating)
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again)

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
 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:
 An SIR model for Covid-19      This is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.     Let's think about things on the scale of a week. What happens over a week?       With an Ro of 2 (2 people infected for each infec
An SIR model for Covid-19

This is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.

Let's think about things on the scale of a week. What happens over a week?

With an Ro of 2 (2 people infected for each infected individual, over the course of a week); recovery rate of 1 (every infected person loses their infectiousness after a week), and resusceptible rate of .05 (meaning .05, or a twentieth of the recovered lose their immunity each week), the disease peaks -- does the wave, then waves again before the year is out, then ultimately becomes
"endemic" (that is, it's never going away, which is clear after two years -- that is, a time of 104 weeks). This is like our seasonal flu (only the disease in this simulation doesn't illustrate seasonality -- that requires a more complicated model).

With an Ro of .9, recovery rate of 1, and resusceptible rate of .05, the disease is eliminated.

Masking, social distancing (including quarantining following contact), and quarantines all serve to reduce infectivity. And if we can drive infectivity down far enough, the disease can be eliminated. Other things that help is slowing down the resusceptibility, by vaccinating. Vaccines (in general) impart an immune response that reduces -- or even eliminates -- your susceptibility. We are still learning the extent to which these vaccines impart long-term immunity.

Other tools at our disposal include Covid-19 treatments, which increase the recovery rate, and vaccinations, which reduce the resusceptible rate. These can also serve to help us eradicate a disease, so that it doesn't become endemic (and so plague us forever).

Andy Long
Mathematics and Statistics

Some resources:
  1. Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
  2. Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

 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:
 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.
3 months ago
     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.

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.
 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 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.
 Introduction; 
 This model shows COVID-19 outbreak in Burnie have some impact for local economy situation and government policy. The main government policy is lockdown during the spreading period which can help reduce the infected rate, and also increase the test scale to help susceptible confirm t

Introduction;

This model shows COVID-19 outbreak in Burnie have some impact for local economy situation and government policy. The main government policy is lockdown during the spreading period which can help reduce the infected rate, and also increase the test scale to help susceptible confirm their situation.


Variables;

Infection rate, Death rate, Recovery rate, test rate, susceptible, immunity rate, economy growth rate

These variables are influenced by different situation.


When cases over 10, government will implement lockdown policy.


Conclusion;

When cases increase too much , they will influence the economic situation.


Interesting insights:

If the recover rate is higher, more people will recover from the disease. It seems to be a positive sign. However, it would lead to a higher number of recovered people and more susceptible. As a result, there would be more cases, and would have a negative impact on the economic growth. 

The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account. The susceptible people are the local Burnie residents. If residents were infected, they would either recovered or dead. However, even they do r
The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account.
The susceptible people are the local Burnie residents. If residents were infected, they would either recovered or dead. However, even they do recover, there is a chance that they will get infected again if immunity loss occurs.
From the simulation result we can see that with the implementation of local government policies including travel ban and social distancing,  the number of infected people will decrease. The number of recovered people will increase in the first 5 weeks but then experience a decrease.
In addition, with the implementation of local government policy, the economic environment in Burnie will be relatively stable when the number of COVID-19 cases is stable.
 This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents.       Assumptions:   Government policies w

This Model was first developed from the SIR model (Susceptible, Infected, Recovered). It was designed to explore relationship between the government policies regarding the COVID-19 and its influences on the economy as well as well-being of local residents. 

 

Assumptions:

Government policies will be triggered when reported COVID-19 case are 10 or less;

Government policies reduces the infection and economic growth at the same time.

 


Interesting Insights:

In the first two weeks, the infected people showed an exponential growth, in another word, that’s the most important period to control the number of people who got affected. 

 

  COVID-19 outbreak model brief description        The model stimulated the COVID-19 outbreak at Burnie in Tasmania. The pandemic spread was driven by infection rate, death rate, recovery rate, and government policy.     The government policy reduces the infection in some way, but it also decreases
COVID-19 outbreak model brief description

The model stimulated the COVID-19 outbreak at Burnie in Tasmania. The pandemic spread was driven by infection rate, death rate, recovery rate, and government policy.

The government policy reduces the infection in some way, but it also decreases the physical industry. Online industry plays a vital role during the pandemic and brings more opportunities to the world economy. 

The vaccination directly reduces the infection rate. The national border will open as long as residents have been fully vaccinated. 

Assumption: 
The model was created based on different rates, including infection rate, death rate, testing rate and recovered rate. There will be difference between the real cases and the model. 

The model only list five elements of government policies embracing vaccination rate, national border and state border restrictions, public health orders, and business restrictions. Public health order includes social distance and residents should wear masks in high spread regions. 

This model only consider two industries which are physical industry, like manufacturer, retailers, or hospitality industries, and online industry. During the pandemic, employees star to work from home and students can have online class. Therefore, the model consider the COVID-19 has positive impact on online industry. 

Interesting insights:
The susceptible will decrease dramatically in first two weeks due to high infection rate and low recovery rate and government policy. After that, the number of susceptible will have a slight decline. 

The death toll and recovery rate was increased significantly in the first two weeks due to insufficient healthy response. And the trend will become mild as government policy works. 



 This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021     Insight Author: Jolina Rosile Magbanua
This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Jolina Rosile Magbanua
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