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The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
Clone of SEIRS MODEL
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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.
Clone of Modelo SIR simples - Covid 19
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Clone of SEIR Model_John
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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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
Clone of SEIR Model for COVID-19 in Indonesia
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram

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

Clone of Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.
Clone of SEIR ABM MODEL
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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.
Clone of Clone of Week-12-Practice
11 months ago
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Description:

This is a system dynamics model of COVID-19 outbreak in Burnie which shows the process of infections and how  government responses, impact on the local economy.  

First part is outbreak model, we can know that when people is infected, there are two situations. One is that he recovers from  treatment, but even if he recovered, the immunity loss rate increase, makes him to become infected again. The other situation is death. In this outbreak, the government's health policies (ban on non-essential trips, closure of non-essential retailers, limits on public gatherings and quarantine )  help to reduce the spread of the COVID-19 new cases. Moreover,  government legislation is dependent on  number of COVID-19 cases and testing rates. 

 Second part: the model of Govt legislation and economic impact. Gov policy can help to reduce infection rate and local economy at same way. The increase of number of COVID-19 cases has a negative impact on local Tourism industry and economic growth rate. On the other hand, Govt legislation also can be change when reported COVID-19 case are less or equal to 10.






Model of COVID-19 outbreak in Burnie(Yafei Shi 489576)
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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. 





BMA708- Shakila Bethmage- 548351 - COVID 19 Outbreak in Burnie
Insight diagram
Data provided by: PHE and Worldometers

Clone of UK COVID 19 Simulator
Insight diagram
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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
Data provided by: PHE and Worldometers

Clone of UK COVID 19 Simulator
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
Model of Covid-19 outbreak in Burnie, Tasmania

Balancing Health and Economy factor
Vaccination rate will help to recovered more people and decrease the immunity loss rate.


Additionally. The lack of food during the covid-19 pandemic still an obstacle for economic development.

In someway, Health balancing in every people will help to shut down covid-19 and help economic development even grow up faster.


Model of Covid-19 outbreak in Burnie, Tasmania
Insight diagram
Data provided by: PHE and Worldometers

Clone of UK COVID 19 Simulator
Insight diagram
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
Clone of SARS-CoV-19 model
Insight diagram
This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay
Clone of System Dynamic Model of COVID 19 in Puerto Princesa City
Insight diagram
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
Zoes clone the pretty the smart and the science
Insight diagram
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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
SD India
Insight diagram

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

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram

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

Clone of SEIR Infectious Disease Model for COVID-19
Insight diagram
Data provided by: PHE and Worldometers

Clone of UK COVID 19 Simulator