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 SEIR Infectious Disease Model for COVID-19

Pol Rifé Mata

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

Alikhan Mairambayev

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

Никита Кравцов

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:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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

Нуршат Ахлас

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:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

SD India

Алина Чеботарь

A simple feedback loop of the COVID-19 pandemic.

Clone of COVID-19 feedback loops

Cindy Hong Lu

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

Alex Thomas

Modelo epidemiológico simples

SIR: Susceptíveis -Infectados - Recuperados

Ajuste os PARÂMETROS abaixo.

Clique em SIMULATE no menu superior para simular.

Dados iniciais de 04 Abr 2020

Fonte: https://www.worldometers.info/coronavirus/country/brazil/

Clone of Modelo SIR simples para Covid-19 - Brasil

Barbara Scoralick Villela

This is the third in a series of models that explore the dynamics of infectious diseases. This model looks at the impact of two types of suppression policies.

Press the simulate button to run the model with no policy. Then explore what happens when you set up a lockdown and quarantining policy by changing the settings below. First explore changing the start date with a policy duration of 60 days.

Clone of SIRD Epidemic Model with Suppression Policies

Jingting REN

This basic pandemic model explores the dynamics and healthcare burden associated with of a novel infection.

Clone of Pandemic: Exploring the Dynamics of a Novel Infection

Peter Ehlers

The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City

Самостоятельная 1 Индия

Едигеева Аружан

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

Indriana Hidayah

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

Zoes clone the pretty the smart and the science

Zoe

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:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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

Aisana Bakhytbek

2 өзіндік жұмыс

Minu

5

Explanation of the Model

This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate.

Assumptions

Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.

Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.

Interesting Insight

There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death.

In contrast, lower testing rates increase the death rate and cases.

Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.

BMA 708, Assessment Tast 3: Complex System, Burnie COVID-19 outbreak, Diprina Shakya-519673

Diprina Shakya

6

The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022.

Total population of Brgy. Sicsican - 22625

Total Covid19 cases as of May 27, 2022 - 250

Local transmission - 241

Imported transmission - 9

Recovery - 226

Death Due to Covid19 - 15

Clone of Ph_Covid19SDM_EvelynBacalla

Leslie Genevieve Ong

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

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

RakeshTigadi

Tugas Permo

Clone of Tugas Permo_Corona

Yopy Anjas

A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.

Clone of SEIR ABM MODEL

Rakhimova Sabina

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

M Ibarria

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:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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

Liv Hammarström

This model visualizes the Covid-19 hypothetical transmission at Barangay Busybees, Taytay, Palawan.

Hypothetical Situation: Barangay Busybees:

- Has a total population of 500 individual.

- Is a rural area.

- Strictly implemented health protocols.

Conclusion: Given the population and situation, transmission of Covid-19 is observed to be less in earlier stage but once transmission start it became rampant. Despite the rampant transmission, the strict implementation of protocols made recovery of people from Covid 19 possible with 92% recovered from the covid.

Model 2

Ruzzel Jan Domdom

COVID-19 Models