A simple Susceptible - Infected - Recovered disease model.

Disease KeLE ABM

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 COVID-19 Corona Coronavirus Virus Disease Infection Pandemic

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

Disease Health Care Health

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.<br>Factors are based on daily choices.

Health Disease Human Body Systems

This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.
Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.

Disease Agent Based Modeling

A simple Susceptible - Infected - Recovered disease as a stock and flow model.

Systems KeLE ABM Disease

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

Disease Health Care Health

A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (healthy and temporarily immune).

ABM Disease

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

Disease Health Care Health

SIR model with waning immunity - Metrics by Guy Lakeman

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

Disease Health Care Health Ebola

A model of an infectious disease and control

Environment Health Care Finance Disease Pandemic

A Susceptible-Infected-Recovered (SIR) disease model

Disease Health Care Health

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

Disease Health Care Health

SIR model with herd immunity - Metrics by Guy Laekman

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

Disease Health Care Health

SIR Model - Metrics by Guy Lakeman

A simple Susceptible - Infected - Recovered disease model.

Disease

Data from two rounds of using Disease Participatory Simulation in class. Participants + Androids = 39.  By adjusting Rate Constant, stocks and flows representation can be used to match data from either Trial 1 or Trial 2. An example of matching Trial 1 is shown when this simulation is run.  Graph of "Area" (Well * Sick) has the same shape as Rate Catching graph. The Rate Catching graph is much smaller because the Well * Sick values are multiplied by a small constant that is the Rate Constant.

Disease Participatory Simulation Logistic Curve Epidemic Curve

SIR Agent-Based Model Disease

Data provided by: PHE and Worldometers

COVID 19 Disease

A simple Susceptible - Infected - Recovered disease model.

Disease Infection

A simple Susceptible - Infected - Aids Patient disease model.

Disease Hiv AIDS

A Susceptible-Infected-Recovered (SIR) disease model for Rage

Disease Health Care Health SIR

Ausbreitung von SARS-CoV-19 in verschiedenen Ländern
- bitte passen Sie die Variablen über die Schieberegler weiter unten entsprechend an

Italien

    ältere Bevölkerung (>65): 0,228
    Faktor der geschätzten unentdeckten Fälle: 0,6
    Ausgangsgröße der Bevölkerung: 60 000 000
    hoher Blutdruck: 0,32 (gbe-bund)
    Herzkrankheit: 0,04 (statista)
    Anzahl der Intensivbetten: 3 100


Deutschland

    ältere Bevölkerung (>65): 0,195 (bpb)
    geschätzte unentdeckte Fälle Faktor: 0,2 (deutschlandfunk)
    Ausgangsgröße der Bevölkerung: 83 000 000
    hoher Blutdruck: 0,26 (gbe-bund)
    Herzkrankheit: 0,2-0,28 (Herzstiftung)
    Anzahl der Intensivbetten: 5 880


Frankreich

    ältere Bevölkerung (>65): 0,183 (statista)
    Faktor der geschätzten unentdeckten Fälle: 0,4
    Ausgangsgröße der Bevölkerung: 67 000 000
    Bluthochdruck: 0,3 (fondation-recherche-cardio-vasculaire)
    Herzkrankheit: 0,1-0,2 (oecd)
    Anzahl der Intensivbetten: 3 000


Je nach Bedarf:

    Anzahl der Begegnungen/Tag: 1 = Quarantäne, 2-3 = soziale Distanzierung , 4-6 = erschwertes soziales Leben, 7-9 = überhaupt keine Einschränkungen // Vorgabe 2
    Praktizierte Präventivmassnahmen (d.h. sich regelmässig die Hände waschen, das Gesicht nicht berühren usw.): 0.1 (niemand tut etwas) - 1 (sehr gründlich) // Vorgabe 0.8
    Aufklärung durch die Regierung: 0,1 (sehr schlecht) - 1 (sehr transparent und aufklärend) // Vorgabe 0,9
    Immunitätsrate (aufgrund fehlender Daten): 0 (man kann nicht immun werden) - 1 (wenn man es einmal hatte, wird man es nie wieder bekommen) // Vorgabe 0,4


Schlüssel

    Anfällige: Menschen sind nicht mit SARS-CoV-19 infiziert, könnten aber infiziert werden
    Infizierte: Menschen sind infiziert worden und haben die Krankheit COVID-19
    Geheilte: Die Menschen haben sich gerade von COVID-19 erholt und können es in diesem Stadium nicht mehr bekommen
    Tote: Menschen starben wegen COVID-19
    Immunisierte: Menschen wurden immun und können die Krankheit nicht mehr bekommen
    Kritischer Prozentsatz der Wiederherstellung: Überlebenschance ohne spezielle medizinische Behandlung

SARS-CoV-19 COVID-19 Corona Coronavirus Virus Disease Infection Pandemic

Agent Base Modeling Disease

Semestrální projekt na operační a systémovou analýzu

Disease