This is a mitigated model showing the potential spread of COVID-19 across the healthcare system.
COVID phased community model
Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus
ECM-Training - SEIR Infectious Disease Model for 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 model
Explanation:
Explanation:
This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be concluded as Susceptible, Infection and Recovery, which are used as the main data in this model. However, the improvement of decreasing of reported infection rates of this infectious disease and increasing of recovery rates are contributed by the implementation of the Government Health Policy.
Assumption
The decrease of both infection rate and economic growth are all influenced by the Government Health Policy simultaneously. The Government Health Policy is only triggered when there are 10 cases reported. However, the increase in reporting COVID-19 cases affects economic growth negatively.
Interesting Insights:
There are two interesting insights that have been revealed from the simulation. First, the death rate continuously increased even though the infection rate goes down. However, the increase in testing rates contributed to the stability of the death rate towards the end of the week. Moreover, higher testing rates also trigger faster government intervention, which can reduce infectious cases. Second, as the Government Health Policy limited the chance of going out and shopping, the economic growth is negative due to the higher cases.
Clone of BMA708, Assessment 3: Complex system, Burnie Covid-19 outbreak
Very basic LV model, looking at the relationship between COVID-19 mitigation behavior and COVID-19 cases
Lotka Volterra COVID Model
Tugas Kelompok Teknik Pemodelan dan Simulasi
Самостоятельная работа Covid-19
During the Covid-19 Outbreak Model
After the Covid-19 Outbreak Model
This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021
Insight Author: Pia Mae M. Palay
System Dynamic Model of COVID 19 in Puerto Princesa City
Model Penyebaran Hoaks "Golongan Darah O Kebal COVID-19"_500359_492989
Exemple de modélisation
https://youtu.be/Kas0tIxDvrgLes chiffres
https://www.worldometers.info/coronavirus/coronavirus-symptoms/
Modélisation Covid-19 aka Coronavirus
The System Dynamics Of Covid-19 Pandemic at Puerto Princesa City Palawan
COVID-19 Vaccination of indigenous West Australians
COVID-19 in Iran 2 - ө. ж.
This Model described the outbreak simulation under government policy and impacts on Economics.
Assumptions
The social distance policy can reduce 80% of infection.
Interesting Insights
The story tell the difference when social distance applied or not
Click on View story to start simulations
BMA708 Task 3 Zijing Zeng 520737
Whitney_6550_Covid-19_GlobalCrisis
A systems model of the relationships amongst economic situation, health situations and Covid-19 in Burnie, Tasmania.
Health situation
According to exposed and go out population decreases, the population of infected decreases after a stable high cases period.
Economic situation
When the infected population decreases, the population economic recovery increases over time, then become stable after a period of time.
BMA708 Assessment 3 Complex system
Modelling of the SARS-Cov-2 viral outbreak using an SEIR model plus specific extensions to model demand for health and care resources.
The model includes biths and deaths, and migration to accommodate import and export of infected individuals from other areas.
Healthcare resources identifies need for hospital beds and critical care.
The model is uses arrays to reflect the different impacts of modelled parameters by age and sex.
Infectious Disease Model (Covid)
