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Model Penyebaran Hoaks "Golongan Darah O Kebal COVID-19"_500359_492989

Anantadhirya

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

Systemigram Covid-19

Diana A.

COVID-19 в Бразилии за 2020-2024 года (динамика заболеваний)

Kamal Lisa

2 months ago

SIR Modeling of Covid-19 in Cameroon

Briana Soriano

системное Америка

Shirin

9 months ago

COVID-19

Аяулым Халыктай

An SIR model for Covid-19

This is a simple example of an SIR model for my Mathematics for Liberal Arts classes at Northern Kentucky University, Spring of 2022.

Let's think about things on the scale of a week. What happens over a week?

With an Ro of 2 (2 people infected for each infected individual, over the course of a week); recovery rate of 1 (every infected person loses their infectiousness after a week), and resusceptible rate of .05 (meaning .05, or a twentieth of the recovered lose their immunity each week), the disease peaks -- does the wave, then waves again before the year is out, then ultimately becomes

"endemic" (that is, it's never going away, which is clear after two years -- that is, a time of 104 weeks). This is like our seasonal flu (only the disease in this simulation doesn't illustrate seasonality -- that requires a more complicated model).

With an Ro of .9, recovery rate of 1, and resusceptible rate of .05, the disease is eliminated.

Masking, social distancing (including quarantining following contact), and quarantines all serve to reduce infectivity. And if we can drive infectivity down far enough, the disease can be eliminated. Other things that help is slowing down the resusceptibility, by vaccinating. Vaccines (in general) impart an immune response that reduces -- or even eliminates -- your susceptibility. We are still learning the extent to which these vaccines impart long-term immunity.

Other tools at our disposal include Covid-19 treatments, which increase the recovery rate, and vaccinations, which reduce the resusceptible rate. These can also serve to help us eradicate a disease, so that it doesn't become endemic (and so plague us forever).

Andy Long

Mathematics and Statistics

Some resources:

Wear a good mask: https://www.cdc.gov/coronavirus/2019-ncov/your-health/effective-masks.html
Gotta catch those sneezes: https://www.dailymail.co.uk/sciencetech/article-8221773/Video-shows-26-foot-trajectory-coronavirus-infected-sneeze.html

MAT115 Covid Simulation

Andrew E Long

Агент модель

Өзіндік жұмыс 2

Nurai Shamsha

4 8 months ago

[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

SEIR Model for COVID-19 in Italy

Artyom Zainulin

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

School project for data modell of the COVID-19 Virus

Corona - DE

Jan Moritz Meyer

11

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

2 өзіндік жұмыс

Minu

3

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

Examen - Covid-19 3ra ola

Stalin Celis

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