COVID-19 | Insight Maker

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

Clone of Clone of SEIR Infectious Disease Model for COVID-19

Muhammad Grandiv Lava Putra (Grandiv)

Самостоятельная работа

Dauren07

9 months ago

1 тапсырма

Arsen Tulegenov

5 months ago

Covid 19

Нұрдәулет Тюленбеков

9 months ago

This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay

Clone of System Dynamic Model of COVID 19 in Puerto Princesa City

Pi Cho

The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.

Clone of SEIR - COVID-19 (v.1)

M Rifat Bagus A 24_16

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.

Clone of Burnie COVID-19 outbreak demo model version 2

Lin Ling

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 4.0)

Wilson Otitonaiye

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

Clone of SEIR Infectious Disease Model for COVID-19

810400027 TKU

The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.

Clone of SEIR - COVID-19 (v.1)

Takafumi Ogura

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 Clone of Week-12-Practice

арайлым мырзахмет

5 months ago

This System Model presents the cases of COVID-19 in Puerto Princesa City as of June 3, 2021

Insight Author: Pia Mae M. Palay

Clone of System Dynamic Model of COVID 19 in Puerto Princesa City

Jolina Magbanua

Dieses Causal Loop Diagramm (CLD) versucht in vereinfachter Weisse die Wesentliche Dynamik des Mars-CoV-2 zu veranschaulichen. Der Motor hinter den Infektionen ist offensichtlich eine selbstverstärkende Rückkopplungsschleife, und ausschlaggebend in diesem Bezug ist der R-Wert. Wenn der R-Wert unter 1 liegt, dann heisst das, dass eine infizierte Person während des Zeitraums, in dem sie infektiös ist, weniger als eine andere Person infiziert. Liegt der Wert über 1, dann steckt die Infizierte mehr als eine andere Person an, und das Virus verbreitet sich exponentiell. Die Schleifen, die blaue Pfeile enthalten, sind negative Rückkopplungsschleifen – sie bremsen die Verbreitung des Virus. Das Diagramm suggeriert, dass der R-Wert als Schlüssel zur Kontrolle der Verbreitung des Virus dienen könnte. Sollte der Wert über 1 steigen, so müssten Schutzmassnahem eingeführt werden. Ist der Wert unter 1, dann sind die negativen Schleifen dominierend und einige Massnahmen könnten gelockert werden.

Eine Systemische Sicht auf Covid-19

Hanns-Jürgen Hodann

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

Carlos Amezquita

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death

Jordan

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

Lohitha Meduri

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions

Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.

Clone of Burnie COVID-19 outbreak demo model

LU JIN

Украина короновирус, 2021 жыл

Мирас Өмірзақов

5 months ago

Test

이민

9 months ago

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

Clone of SEIR Infectious Disease Model for COVID-19

Daffa Muhammad Romero

Abdisattarova

Nurdidara

The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.

Clone of Influenza and SARS-CoV-2 interaction v1

Pau Prat Moreno

Simple epidemiological model for Burnie, Tasmania

SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).

Clone of Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)

Lin Ling