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
Health Care Biogeochemistry Biomedical Health Food Disease
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
Clone of Pharmacokinetics
Btari Fatma R
A simple Susceptible - Infected - Recovered disease model.
Disease KeLE ABM
A simple Susceptible - Infected - Recovered disease model.
Clone of CS558 System Dynamics Disease Model
Rhys Almario
Insight diagram
SIR Disease Agent-Based Model
Clone of SIR Model
Evandro Dias Gaio
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. Factors are based on daily choices.
Health Disease Human Body Systems
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.
Factors are based on daily choices.
Clone of Human Body Systems Efficiency
Marc-André Beaulieu
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: 4-11     Ausgangsgröße der Bevölkerung: 60 000 000     hoher Blut
Coronavirus Pandemic SARS-CoV-19 Virus Disease COVID-19 Corona Infection
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: 4-11
    Ausgangsgröße der Bevölkerung: 60 000 000
    hoher Blutdruck: 0,32 (gbe-bund)
    Herzkrankheit: 0,04 (statista)
    kostenlose Intensivstationen: 3 100


Deutschland

    ältere Bevölkerung (>65): 0,195 (bpb)
    geschätzte unentdeckte Fälle Faktor: 2-3 (deutschlandfunk)
    Ausgangsgröße der Bevölkerung: 83 000 000
    hoher Blutdruck: 0,26 (gbe-bund)
    Herzkrankheit: 0,2-0,28 (Herzstiftung)
    kostenlose Intensivstationen: 5 880


Frankreich

    ältere Bevölkerung (>65): 0,183 (statista)
    Faktor der geschätzten unentdeckten Fälle: 3-5
    Ausgangsgröße der Bevölkerung: 67 000 000
    Bluthochdruck: 0,3 (fondation-recherche-cardio-vasculaire)
    Herzkrankheit: 0,1-0,2 (oecd)
    kostenlose Intensivstationen: 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
Clone of SARS-CoV-19 Modell von Lucia Vega Resto
Antonius Warmeling
A simple Susceptible - Infected - Recovered disease model.
KeLE ABM Disease
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Alina
A simple Susceptible - Infected - Recovered disease model.
KeLE ABM Disease
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Micajah McGarity
A simple Susceptible - Infected - Recovered disease model.
Disease KeLE ABM
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Victor Angers
Insight diagram
Disease
disease tutorial
Ezra B Zubrow
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 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).

Clone of CS558 Agent-Based Spatially Aware Disease Model
Evan Johansson
A simple Susceptible - Infected - Recovered disease model.
KeLE ABM Disease
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Nicholas J Cooper
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).
Disease ABM

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).

Clone of Spatially Aware SIR Diseasse Model
Sergei Titov
Insight diagram
Agent Base Modeling Disease
Clone of Agent Based Disease
TomG
Insight diagram
Disease
Spread of SARS
Arina Putri Tanca
A simple Susceptible - Infected - Recovered disease model.
Disease KeLE ABM
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Sasha Chang
SIR Model - Metrics by Guy Lakeman A simple Susceptible - Infected - Recovered disease model.
Disease
SIR Model - Metrics by Guy Lakeman

A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model - Metrics by Guy Lakeman
bora
This is Lab3 for a Dynamic Systems class. This lab requires us to follow the Disease Dynamics Tutorial.
Disease
This is Lab3 for a Dynamic Systems class. This lab requires us to follow the Disease Dynamics Tutorial.
DiseaseDynamics
Whitney Meulink
Insight diagram
Disease LAB1
Disease Simulation
Russell Dunsford
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). @ LinkedIn ,  Twitter ,  YouTube
ABM SIR Roam Disease

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).

@LinkedIn, Twitter, YouTube

Clone of Spatially Aware SIR Disease Model
Yong Joo Park
Tutorial model of disease dynamics using ABM
ABM Disease
Tutorial model of disease dynamics using ABM
Clone of Agent-Based Disease Dynamics
Bechara Assouad
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
Infection SARS-CoV-19 COVID-19 Corona Coronavirus Virus Disease Pandemic
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
Daniel Afriyie
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
Disease Virus Pandemic Infection Corona SARS-CoV-19 Coronavirus COVID-19
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
Jingyi Qiu
A simple Susceptible - Infected - Recovered disease model.
KeLE ABM Disease AISR
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model AISR
Bilal Koussayer
A Susceptible-Infected-Recovered (SIR) disease model with herd immunity and isolation policies.
Health Care Health Disease

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

Clone of SIR model with herd immunity and isolation
Tong's Song