A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

  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.

​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
 A Susceptible-Infected-Recovered (SIR) disease model with herd immunity and isolation policies.

A Susceptible-Infected-Recovered (SIR) disease model with herd immunity and isolation policies.

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.
 FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION     BEWARE pushing increased growth blows the system!    (governments are trying to push growth on already unstable systems !)  The existing global capitalistic growth paradigm is totally flawed  The chaotic turbulence is the result of th
FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

   THE 2020 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION.  WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER R

THE 2020 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION.
WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
 A Susceptible-Infected-Recovered (SIR) disease model with herd immunity

A Susceptible-Infected-Recovered (SIR) disease model with herd immunity

Health Services System Map of Mildura region
Health Services System Map of Mildura region
 This is a simple implementation of the SIR epidemiological model. See  Wikipedia  for a description.        The number of new infections is proportional to the total number of infected people, the fraction of the population that remains susceptible, and the ratio of the total number of infections p
This is a simple implementation of the SIR epidemiological model. See Wikipedia for a description. 

The number of new infections is proportional to the total number of infected people, the fraction of the population that remains susceptible, and the ratio of the total number of infections per case and the typical time to recover from an infection.
How do drugs affect us on individual and popular levels? Let's take a look at drug addiction as a system and pick it apart based on its biological, financial, mental, and communal effects.
How do drugs affect us on individual and popular levels? Let's take a look at drug addiction as a system and pick it apart based on its biological, financial, mental, and communal effects.
Dosage per day, Doses per day, Every ? hours, Medicine in Intestines, Drug absorption, Plasma level, Blood volume, Plasma concentration, ​Toxic level, Medicinal level, Drug excretion, Excretion rate, Half-Life
Dosage per day, Doses per day, Every ? hours, Medicine in Intestines, Drug absorption, Plasma level, Blood volume, Plasma concentration, ​Toxic level, Medicinal level, Drug excretion, Excretion rate, Half-Life
 A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

Simulation of MTBF with controls   F(t) = 1 - e ^ -λt   Where    • F(t) is the probability of failure    • λ is the failure rate in 1/time unit (1/h, for example)   • t is the observed service life (h, for example)  The inverse curve is the trust time On the right the increase in failures brings its
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
Dosage per day, Doses per day, Every ? hours, Medicine in Intestines, Drug absorption, Plasma level, Blood volume, Plasma concentration, ​Toxic level, Medicinal level, Drug excretion, Excretion rate, Half-Life
Dosage per day, Doses per day, Every ? hours, Medicine in Intestines, Drug absorption, Plasma level, Blood volume, Plasma concentration, ​Toxic level, Medicinal level, Drug excretion, Excretion rate, Half-Life
​This model has been constructed from the model published in the following article:  Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control".    System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society. 
​This model has been constructed from the model published in the following article:
Jack B. Homer, "Worker burnout: a dynamic model with implications for prevention and control". 
System Dynamics Review 1 (no. 1, Summer 1985): 42-62. ISSN 0883-7066. 0 1985 by the Svstem Dynamics Society.