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
Tutorial 1.1 simulating a simple disease
Tutorial 1.1.0 - Disease
Diagram adapted from Chaix2009 review article with multiscale effects of geography and neighbourhoods on individuals developing coronary heart disease
Geographic living environments and coronary heart disease
A Susceptible-Infected-Recovered (SIR) disease model with waning immunity
SIR model with waning immunity
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
Tutorial 1.1 simulating a simple disease, with reduced infection rate
Tutorial 1.1.2 - Disease - Reduced Infection Rate
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
当处在春节时期，疫情来临时，外来人口较多的S市的疫情传染仿真模型。
人群的状态可分为S/E/I/R/D的五个状态，S为易感染者（即S市所在人群），E为潜伏期患者（人群不会对他远离，但是会传染他人），I为感染者（为医院确诊人群，他人会远离该患者），R为康复人群，D为死亡人群。
SEIR
A simple Susceptible - Infected - Recovered disease model.
SIR Model
Semestrální projekt na operační a systémovou analýzu
Operacni a systemova analyza
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
Simple Basic Model as suggested in the manual (in German)
Modellierung einfache Infektionskrankheit
Tutorial 1.1 simulating a simple disease, with increased recovery rate
Tutorial 1.1.1 - Disease - Improved Recovery
Clone of Agent Based Disease
SIR model with waning immunity - Metrics by Guy Lakeman
A Susceptible-Infected-Recovered (SIR) disease model with waning immunity
Clone of SIR model with waning immunity - Metrics by Guy Lakeman
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 Clone of Spatially Aware SIR Diseasse Model
SIR model with herd immunity - Metrics by Suresh Vunnam
A Susceptible-Infected-Recovered (SIR) disease model with herd immunity
SIR model with herd immunity - Metrics by suresh vunnam
A simple Susceptible - Infected - Recovered disease model.
Clone of SIR Model
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).
Agent-Based Spatially Aware Disease Model
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
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
