Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

Health Care Infection Epidemics 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

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

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0. Compare with Bass Diffusion Model IM-610

Health Care Infection

Kermack–McKendrick Epidemic SIR Infectious Disease Model - Metrics by Guy Lakeman

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0.

Health Care Infection Ebola

Upgrade of Kermack–McKendrick Epidemic SIR Infectious Disease Model (circa 2015) - Metrics by Guy Lakeman

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0.

Addition of a slider for susceptibles is equivalent to accumulated total cases

SARS, MERS AND COVID are similar virus types only differing in their sub genus

The COVID outbreak has reached 150,000 infected people

This simulation allows an attempt at predicting how long the virus will persist and its longevity dependence on its high speed massive infection numbers that have reached pandemic proportions

SARS reached 8,000 infected total and ran for 9 months before stopping

MERS 2012 is still killing 8 years later with patients dying even after using interferon to try and cure them

Health Care Infection Ebola Epidemic SARS MERS COVID

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

Health Care Infection Epidemics

Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus.

We add simple containment meassures that affect two paramenters, the Susceptible population and the rate to become infected.

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

The questions that we want to answer in this kind of models are not the shape of the curves, that are almost known from the beginning, but, when this happens, and the amplitude of the shapes. This is crucial, since in the current circumstance implies the collapse of certain resources, not only healthcare.

The validation process hence becomes critical, and allows to estimate the different parameters of the model from the data we obtain. This simulation approach allows to obtain somethings that is crucial to make decisions, the causality. We can infer this from the assumptions that are implicit on the model, and from it we can make decisions to improve the system behavior.

Yes, simulation works with causality and Flows diagrams is one of the techniques we have to draw it graphically, but is not the only one. On https://sdlps.com/projects/documentation/1009 you can review soon the same model but represented in Specification and Description Language.

Health Care Infection Epidemics

WIP map of ebola in west Africa based on African Affairs Article and SEIR framework

Health Care Infection Social

Here we have modified the SIR model of Insight 584 by adding an additional stock of Exposed people, who become Infective after an incubation period.

Health Care Infection

Upgrade of Kermack–McKendrick Epidemic SIR Infectious Disease Model (circa 2015) - Metrics by Guy Lakeman

This is a simple SIR infectious diseases 3 stock model with Susceptibles, Infectives and Recovereds stocks. In the initial description the R signified Removed and could include Deaths, Recovered with immunity to infection (Resistant) or those who had fled the epidemic. Note the need to initiate the epidemic by adding a pulse of a single infected person at time 0.

Addition of a slider for susceptibles is equivalent to accumulated total cases

SARS, MERS AND COVID are similar virus types only differing in their sub genus

The COVID outbreak has reached 150,000 infected people

This simulation allows an attempt at predicting how long the virus will persist and its longevity dependence on its high speed massive infection numbers that have reached pandemic proportions

SARS reached 8,000 infected total and ran for 9 months before stopping

MERS 2012 is still killing 8 years later with patients dying even after using interferon to try and cure them

updated 16/3/2020 from 5 years ago

Health Care Infection Ebola Epidemic SARS MERS COVID Pandemic

From Cultural Worldview and Preference for Childhood Vaccination Policy PSJ Article  Nov 2014

Health Care Infection Culture

Paprastas 3 regionų užkrėstumo modelis. Irena Gustytė.

Health Care Infection Bamba

A simple Susceptible - Infected - Recovered disease model.

Disease 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: 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

Multilevel context mechanisms and outcomes for hospital infection control

Health Care Hospital Infection Policy Multiscale Realist

This insight shows the relation between a healthy set of people and an infected set using the primitive infection rate variable as the "rate" that defines how many people get into the infected set at which rate. The second part of the insight shows how many people get recovered and move into Immune set by the rate defined through Recovery Rate variable.

Disease Dynamics Health Infection Immunity Rate Of Recovery.

This model shows the relationship between placement to Bourke Hospital and Infection Rate, Recovery rate and release from Bourke Hospital.  
AssumptionsThis model assumes that:upper value for Sensitive to get infected is 50 peopleupper value for Placed into Bourke hospital is 50 peopleupper value for Released from Bourke hospital is 50 people
VariablesInfection Rate - can be adjusted upwards or downwards to stimulate infection rate.Infection Factor - can be adjusted upwards or downwards to stimulate infection rate.Recovery Rate - can be adjusted upwards or downwards to stimulate infection rate.

Health People Infection

simple epidemic model Susceptibles > Infected > Recovered (SIR)

Health Care Infection

Health Care Infection

Zombie Infection

BME 1300 Project

Health Care Infection

If you include zombie disease and zombie cannibalism, how long would a zombie apocalypse last?

I'm adding a few things to the standard model:* zombies are carnivores* zombies, once the number of uninfected have significantly dropped, will start eating each other* zombies are in the throes of a fatal disease.  In enough time, the disease (and secondary diseases) will kill them* if there's no one left to eat, the zombies will starve

Health Care Infection

感染係数: ウイルスの感染しやすさ致死係数: 重症患者の死にやすさ初期潜伏感染者: 初めの潜伏感染者数政策閾値: 緊急事態宣言を出す判断を行う陽性判定数政策日数: 政策の長さ政策病床数: 政策によって追加されるホテル病床数政策外出率: 政策期間の外出率
バグ:病床使用率が1を超える陽性判定数が発症者の係数倍政策による効果の粒度が粗い

Health Care Infection

BME 1300 Project

Health Care Infection