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Ramazan Almakhan

10 minutes ago

COVID-19

Наурызбай Мерейлі

3 7 months ago

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

Ph_Covid19SDM_AngelKateCacayan

angel kate cacayan

9

COVID-19

ERMAKOV SEMEN

Tugas 3_Thamara Shaifa Anwar_0441174000035_Pemodelan Transportasi Laut

Dosen Pengampu : Dr-Ing Ir. Setyo Nugroho

Pemodelan COVID-19 di Indonesia

Thamara Shaifa Anwar

4

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

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

SEIR Infectious Disease Model for COVID-19

Steven D'Alessandro

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.

Burnie COVID-19 outbreak demo model version 2

Steven D'Alessandro

39

S-I-R covid-19 model

KRISTIAN SABATINO

Tugas Kelompok Teknik Pemodelan dan Simulasi

SIR Model Covid-19

Danurrizad Ryan Darmoko

11

Agent based Modeling Simulation for Pandemic COVID-19 Disease

Covid-19(ABM)_VHK

Venkata Habiram Koppaka

5

Covid-19 sim

Alexander Shaw

Covid-19-rise

Carrick M. Eggleston

Simulasi persebaran Covid-19 di Provinsi Bali tahun 2020.

Asumsi:

1. Belum ada vaksin karena pada tahun 2020 vaksin belum tersedia.

TA Pemsim - SEIR Covid-19 Model

Luthfi Izzuddin Hanif

6

AGENT-BASED MODEL OF COVID-19

AL JOHN D. CENA

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

Pia Mae Palay

13

COVID-19 SEIR Model for Indonesia

Daffa Muhammad Romero

Tugas mata kuliah pemodelan modifikasi model Covid -19 an. Faqih, Aji, dan Wahyu

Tugas Modifikasi Model Covid-19

Faqih Zulfikar

4

The System Dynamics Model presents the the COVID-19 status in Сhina

Сим Covid-19 in China

Сим Максим

20 hours ago

COVID-19 S&F PT1

Morgan Kull

25

Model ini dirancang untuk membuat model tentang penyebaran Covid-19 dan vaksinasi di Kabupaten Sleman pada November 2022

Model ini dibuat untuk memenuhi tugas kelompok dari matakuliah Metode Penyelesaian Masalah dan Pemodelan, atas nama :

Sabilla Halimatus Mahmud

Nurul Widyastuti
Muhammad Najib

Edit Model Penyebaran Covid-19 di Kabupaten Sleman

Najib Muhammad

7

This insight began as a March 22nd Clone of "Italian COVID 19 outbreak control"; thanks to Gabo HN for the original insight. The following links are theirs:

Initial data from:

Italian data [link] (Mar 4)

Incubation estimation [link]

Andy Long

Northern Kentucky University

May 2nd, 2020

This is an update of our model from April 9th, 2020. As we prepare for our final exam, I read a story in The Guardian about Italy's struggle to return to normalcy. The final paragraphs:

During the debate in the Senate on Thursday, the opposition parties grilled Conte. Ex-prime minister Matteo Renzi, who has called for less restraint in the reopening, remarked, “The people in Bergamo and Brescia who are gone, those who died of the virus, if they could speak, they’d tell us to relaunch the country for them, in their honour.”

Renzi’s controversial statement was harshly criticised by doctors who warned that the spread of the disease, which, as of Thursday, had killed almost 30,000 people in the country and infected more than 205,000 [ael: my emphasis], was not over and that a misstep could take the entire country back to mid-March coronavirus levels.

“We risk a new wave of infections and outbreaks if we’re not careful,” said Tullio Prestileo, an infectious diseases specialist at Palermo’s Benefratelli Hospital. “If we don’t realise this, we could easily find ourselves back where we started. In that case, we may not have the strength to get back up again.”

I have since updated the dataset, to include total cases from February 24th to May 2nd. I went to Harvard's Covid-19 website for Italy and and then to their daily updates, available at github. I downloaded the regional csv file for May 2nd, which had regional totals (21 regions); I grabbed the column "totale_casi" and did some processing to get the daily totals from the 24th of February to the 2nd of May.

The cases I obtained in this way matched those used by Gabo HN.

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

You can get a good fit to the data through April 9th 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.

The question is: how well did we do at modeling this epidemic through May 2nd (day 60)? And how can we change the model to do a better job of capturing the outbreak from March 3rd until May 2nd?

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.)