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

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

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

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

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

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

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

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

A simple Susceptible - Infected - Recovered disease model.

Disease Infection

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

Zombie 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

BME 1300 Project

Health Care Infection

Health Care Infection

BME 1300 Project

Health Care Infection

Slimības

Infection

This insight is about infection propagation and  population migration influence on this propagation. For this, we defined a world population size and a percentage of it who’s infected. Then, we created an agent where we simulated possible states of an individual. So, he can be healthy, infected (with an infection rate) or immunized ( with a certain rate of immunization). If the individual is infected, he can be alive or dead. Then, we simulated different continents (North-America, Asia and Europe) with a migration between these with a certain rate of migration (we tried to approach reality). Then, thanks to our move action which represents a circular permutation between the different continents with a random probability, the agent will be applied to every individual of the world population.

 How does the program work ?

In order to use this insight, we need to define a size of world population and a probability of every individual to reproduce himself. Every individual of this population can have three different state (healthy, infected or immunized) and infected people can be alive or dead. We need to define a percentage of infection for healthy people and a percentage of death for infected people and also a percentage of immunization.
Finally, there is Migration Part of the program, in this one, we need to define three different continents, states or whatever you want. We also need to define a migration probability between each continent to move these person. With this moving people, we can study the influence of migration on the propagation of a disease.

Vincent Cochet, Julien Platel, Jordan Béguet

Health Migration Infection

A basic infection model where the the infection and recovery rates depend on the fraction of the total population that is infected.

Infection Recovery

Infection