A simple Susceptible - Infected - Recovered disease model.
SIR Model
A simple Susceptible - Infected - Aids Patient disease model.
HIV model with Agents - Vinicius Carius and Cesar Conopoima
A simple Susceptible - Infected - Aids Patient disease model.
HIV model - Vinicius Carius and Cesar Conopoima
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 model
A model of an infectious disease and control
Disease Dynamics (Agent Based Modeling) Guy Lakeman
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 Week-12-Practice
A simple Susceptible - Infected - Aids Patient disease model.
ВИЧ в китае на миллион человек
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.
Reservoir Disease Spread
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 Data
A simple Susceptible - Infected - Aids Patient disease model.
Clone of HIV model with Agents - Vinicius Carius and Cesar Conopoima
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
A model of an infectious disease and control
Clone of Disease Dynamics (Agent Based Modeling)
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
Spatially Aware SIR Disease Model
A simple Susceptible - Infected - Aids Patient disease model.
Clone of HIV model with Agents - Vinicius Carius and Cesar Conopoima
The dual pathogen model from "Competition-colonization dynamics in an RNA virus" Ojosnegros et al 2010
Dual Pathogen Model
Zombie Pathogen Transmission Influence Diagram
A Susceptible-Infected-Recovered (SIR) disease model with waning immunity
Clone of SIR Model with waning immunity
Disease Dynamics Tutorial
Disease Dynamics Tutorial
A simple Susceptible - Infected - Aids Patient disease model.
Самостотельная работа Короновирус в Казакстане
A simple Susceptible - Infected - Aids Patient disease model.
ЖИТС Ауруы Жансая
A Susceptible-Infected-Recovered (SIR) disease model with herd immunity
SIR model with herd immunity
