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Simulation of how a virus infects after entering the body, how it replicates inside living cells, and how the body's immune system responds towards the virus

System Dynamic Model 1b (Previously-infected individual)

Hazwan Hafiz

3

SIR Modeling of Covid-19 in Cameroon

Briana Soriano

Check how different times of recovery and deths in cases of covid-19 infulence 2 key mortality indicators:

Overall mortalityr ate (ratio of all deaths to all cases)

Resolved cases mortality rate (ratio of all deaths to recovered cases)

Assumed delays are:

5 weeks for recovery cases

2 weeks for death cases

Delays are built into conveyor stocks, so cannot be adjusted by slider

keep in mind Insigth uses similar but made-up numbers and linear flow of new cases (in opposition to exponential in real world)

Understanding Covid-19 mortality

Cezary Zając

Assignment 3 Norway Covid-19

C. T

Италиядағы COVID-19 экосистемасы

Жаңа идеялар

Таир Аубакиров

March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the insight.

Initial data from:

Italian data [link] (Mar 4)

Incubation estimation [link]

Andy Long

April 9th, 2020

I have since updated the dataset, to include total cases from February 24th to April 9th.

I went to
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KDFYZW
and downloaded the archive for April 9th:
https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/KDFYZW/C2HSTK&version=19.0

I dug through the files, and found the file dpc-covid19-ita-regioni.csv, which had regional totals (21 regions); I grabbed the column "totale_casi" and used some lsp code to get the daily totals from the 24th of February til the 9th of April.

The good news is that the cases I obtained in this way matched those used by Gabo HN.

The initial data started on March 3rd (that's 0 in this Insight).

You can get a good fit to the data by choosing the following (and notice that I've short-circuited the process from the Infectious to the Dead and Recovered). I've also added the Infectious to the Total cases.

Incubation Rate: .025

R0: 3

First Lockdown: IfThenElse(Days() == 5, 16000000, 0)

Total Lockdown: IfThenElse(Days() >= 7, 0.7,0)

(I didn't want to assume that the "Total Lockdown" wasn't leaky! So it gets successively tighter, but people are sloppy, so it simply goes to 0 exponentially, rather than completely all at once.)

deathrate: .01

recoveryrate: .03

"Death flow": [deathrate]*[Infectious]

"Recovery flow": [recoveryrate]*[Infectious]

Total Reported Cases: [Dead]+[Surviving / Survived]+[Infectious]

Resources:

* https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported

MAT375 Version of Italian COVID 19 outbreak control

Andrew E Long

32

системное Америка

Shirin

11 months ago

School project for data modell of the COVID-19 Virus

Corona - DE

Jan Moritz Meyer

11

Develop a basic Systemigram / Rich Picture to tell the story of covid 19 mitigation

Systemigram Covid-19

Diana A.

Агент модель

Өзіндік жұмыс 2

Nurai Shamsha

4 10 months ago

COVID-19

Аяулым Халыктай

[The Model of COVID-19 Pandemic Outbreak in Burnie, TAS]

A model of COVID-19 outbreaks and responses from the government with the impact on the local economy and medical supply.

It is assumed that the government policy is triggered and rely on reported COVID-19 cases when the confirmed cases are 10 or less.

Interesting insights

The infection rate will decline if the government increase the testing ranges, meanwhile, the more confirmed cases will increase the pressure on hospital capacity and generate more demand for medical resources, which will promote government policy intervention to narrow the demand gap and affect economic performance by increasing hospital construction with financial investment.

The Model of COVID-19 Pandemic Outbreak in Burnie, TAS

Yuxi Wang

Pemodelan COVID-19

Hisyam Awie Anugrah

Description:

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.

Assumptions:

The government policy is triggered when the number of infected is more than ten.

The government policies will take a negative effect on Covid-19 outbreaks and the financial system.

Parameters:

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameter: Background disease.

Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be changed from vaccination rate.

Government policy's parameters

Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)

Economic's parameters

Fixed parameter: Tourism

Adjusted parameter: Economic growth rate(from 0.3 to 0.5)

Interesting insight

An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.

BMA708_Assignment 3_Nguyen Dang Khoa Vo_520272_COVID-19 outbreak and Burnie economy

NGUYEN DANG KHOA VO

Systemigram Model Building Exercise (COVID-19)

Josiah Best

2 өзіндік жұмыс

Minu

3

Introduction:

This model demonstrates the COVID-19 outbreak in Bernie, Tasmania, and shows the relationship between coVID-19 outbreaks, government policy and the local economy. The spread of pandemics is influenced by many factors, such as infection rates, mortality rates, recovery rates and government policies. Although government policy has brought the Covid-19 outbreak under control, it has had a negative impact on the financial system, and the increase in COVID-19 cases has had a negative impact on economic growth.

Assumptions:

The model is based on different infection rates, including infection rate, mortality rate, detection rate and recovery rate. There is a difference between a real case and a model. Since the model setup will only be initiated when 10 cases are reported, the impact on infection rates and economic growth will be reduced.

Interesting insights:

Even as infection rates fall, mortality rates continue to rise. However, the rise in testing rates and government health policies contribute to the stability of mortality. The model thinks that COVID-19 has a negative impact on offline industry and has a positive impact on online industry.

Model of COVID-19 outbreak in Burnie, Tasmania

Zilin Wu

SEIR Model for COVID-19 in Italy

Artyom Zainulin

Examen - Covid-19 3ra ola

Stalin Celis

COVID-19 in USA

Марина Якимова

This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.

COVID Model

Justine Grace Jumawan

COVID-19 outbreak in Burnie Tasmania Simulation Model

Introduction

This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community. Government responses are based on the reported COVID-19 cases amount, whcih is considered to be based on testing rate times number of people who are infected minus those recovered from COVID-19 and dead.

Government interventions include the implement of healthy policy, border surveillance, quarantine and travel restriction. After outbreak, economic activities are positively affected by the ecommerce channel development and normal economic grwoth, while the unemployement rate unfortunately increases as well.

Assumption

Enforcing government policies reduce both infection and economica growth.
When there are 10 or greater COVID-19 cases reported, the governmwnt policies are triggered.
Greater COVID-19 cases have negatively influenced the economic activities.
Government policies restict people's activities socially and economically, leading to negative effects on economy.
Opportunities for jobs are cut down too, making umemployment rate increased.
During the outbreak period, ecommerce has increased accordingly because people are restricted from going out.

Interesting insights

An increase in vaccination rate will make difference on reduing the infection. People who get vaccinated are seen to have higher immunity index to fight with COVID-19. Further research is needed.

Testing rate is considered as critical issue to reflect the necessity of government intervention. Higher testing rate seems to boost immediate intervention. Reinforced policies can then reduce the spread of coronvirus but absoluately have negative impacts on economy too.

Mengling Xue 561743 BMA708_Marketing insights into Big Data

Mengling Xue

Systems Project Stock and Flow

Alexis Pelland-Champagne

covid-19

Маржан Абдураимова

3 months ago