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COVID-19 в США

Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.

SEIR Model for COVID-19 in Indonesia

Muhammad Iqbal M

37

Сovid 19 South Korea

Dauren07

7

Taller evaluativo #2

KEVIN HERNANDO BELTRAN MARTINEZ

Very basic LV model, looking at the relationship between COVID-19 mitigation behavior and COVID-19 cases

Lotka Volterra COVID Model

Christa

COVID problem

Yushan Li

АҚШтағы COVID-19 Агенттік модель

MERS Aizhan

5 5 months ago

Initial data from:

Italian data [link] (Mar 4)

Incubation estimation [link]

Italian COVID 19 outbreak control

Gabo HN

85

COVID-19

ERMAKOV SEMEN

Model description:

This model is designed to simulate the Covid-19 outbreak in Burnie, Tasmania by estimating several factors such as exposed population, infection rate, testing rate, recovery rate, death rate and immunity loss. The model also simulates the measures implemented by the government which will impact on the local infection and economy.

Assumption:

Government policies will reduce the mobility of the population as well as the infection. In addition, economic activities in the tourism and hospitality industry will suffer negative influences from the government measures. However, essential businesses like supermarkets will benefit from the health policies on the contrary.

Variables:

Infection rate, recovery rate, death rate, testing rate are the variables to the cases of Covid-19. On the other hand, the number of cases is also a variable to the government policies, which directly influences the number of exposed.

The GDP is dependent on the variables of economic activities. Nonetheless, the government’s lockdown measure has also become the variable to the economic activities.

Interesting insights:

Government policies are effective to curb infection by reducing the number of exposed when the case number is greater than 10. The economy becomes stagnant when the case spikes up but it climbs up again when the number of cases is under control.

Sample Model of COVID-19 outbreak in Burnie Tasmania by Yim Fong Ng (544885)

Chrissie Ng

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

SNM Model Penyebaran Covid-19 di Kabupaten Sleman

Najib Muhammad

A Susceptible - Infected - Recovered disease as a stock and flow model for COVID.

COVID SIR Disease Model

Gregory Fulkerson

3

A principio de 2020 se produjo en América la llegada de la enfermedad covid-19, una enfermedad transmitida en mayor medida mediante los contactos estrechos con personas contagiadas. Al crecer la preocupación científica se decidió implementar una fase de aislamiento social, cierre de comercios y fomentar medidas sanitarias contra la propagación, como el uso de tapaboca y distancia social.Con estas medidas el riesgo de contraer la enfermedad se vió reducido, pero el cierre de los comercios causó una caída económica.

TP Actividad extra - Simulación

Mauro Sergio Rodriguez

A principio de 2020 se produjo en América la llegada de la enfermedad covid-19, una enfermedad transmitida en mayor medida mediante los contactos estrechos con personas contagiadas. Al crecer la preocupación científica se decidió implementar una fase de aislamiento social, cierre de comercios y fomentar medidas sanitarias contra la propagación, como el uso de tapaboca y distancia social.

Con estas medidas el riesgo de contraer la enfermedad se vió reducido, pero el cierre de los comercios causó una caída económica.

TP Actividad de cierre de práctica - Covid

Paola Perez

3

Urban Public Health Issue - COVID

Koyel Das

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

Covid-19 TAED

Jose

COVID-19 cases in Barangay Candawaga, Municipality of Rizal

COVID-19 MODEL2 (OPERIANO, GLIANNE BETH O.)

Glianne Operiano

This insight model is designed to give insight into the stability of COVID 19 infection rates due to government policy and the delay between the time people are tested and the reaction of the Government to the testing results.

COVID Control Loop