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Tugas Pemodelan Transportasi Laut
Memodelkan persebaran pandemik covid-19 di Indonesia menggunakan insightmaker

Dosen : Dr.-Ing Ir Setyo Nugroho

Pemodelan Corona_Tugas 3_Yustika Athiya Hasna_04411740000023

Yustika Athiya Hasna

9

My Insight 2 covid-19

Кенжебекова Асем

7 months ago

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from SIR model(susceptible, infected, revovered) to find out the effect of covid-19 outbreak into economic outcomes via government policy.

Assumptions

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

The government policies will take negative effect into Covid-19 outbreaks and financial system

Parameters

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameters: Infection rate, Background disease, recovery rate.

Adjusted parameter: Immunity loss rate can be change 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

Increase vaccination rate and testing rate will decrease the number amount of infected case and a little bit more negative effect to economic system. However economic system still need a long time to recover in both cases.

BMA708_Assignment 3_ndkvo_520272_COVID-19 outbreak and Burnie economy

KV

COVID-19 Systemigram

Bianca Rodriguez

Introduction:

This simulation model demonstrates the outbreak of Covid-19 in Burnie, Tasmania and how the corresponding government’s responses affect the spreading of Covid-19. Meanwhile, this model also shows how the economy in Burnie is influenced by the impacts of both Covid-19 and government policies.

Variables:

This simulation contains some relevant variables as follow:

Variables in Covid-19 outbreaks: (1) Infection rate, (2) Recovery rate, (3) Death rate, (4) Immunity loss rate

Variables in Government policies: (1) Vaccination rate, (2) Lockdown, (3) Travel ban, (4)Quarantine

Variables in Economy: (1) E-commerce business, (2) Unemployment rate, (3) Economic growth rate.

Assumption:

Government responses would be triggered when reported Covid-19 cases are at least 10.

The government policies reduce the spreading of Covid-19, but they would also limit economic development at the same time due to the negative impact of the policies on the economy is greater than the positive impact.

The increase in reported Covid-19 cases would negatively affect economic growth.

Interesting Insights:

The first finding is that the death number would keep increasing even though the infection rate has decreased, but with stronger government policies (such as implementing a coefficient over 25%), no more death numbers will occur caused by Covid-19.

The second finding is that as government policies limit business activities, with the increasing number of reported Covid-19 cases, economic growth will suffer a severe blow even if e-commerce grows, it can’t make up for this economic loss.

BMA 708 assignment 3 - simulation model of Covid-19 Outbreak in Burnie, Tasmania

Xuanze Liang

COVID-19 Section

Manu Hegde

Modelling of the SARS-Cov-2 viral outbreak using an SEIR model plus specific extensions to model demand for health and care resources.

The model includes biths and deaths, and migration to accommodate import and export of infected individuals from other areas.

Healthcare resources identifies need for hospital beds and critical care.

The model is uses arrays to reflect the different impacts of modelled parameters by age and sex.

Infectious Disease Model (Covid)

David Plummer

7

Sike Liu_Assignment

Sike Liu

COVID-19 in Turkey

Асел Сабитқызы

11 months ago

Contágio da COVID-19

Luiza Freire

Modèle simple de causalité entre mesures et impact

COVID-19

Philippe Vallat

COVID-19 FLOW

Sitti Mashita J. Kilis

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

The initial parametrization is based on the suggested current data. The initial population is set for Catalonia.

Clone of SEIRD 01: COVID-19 spread

Albert Campos Gisbert

COVID-19 Crisis Management

Marian Antonela Castillo Román

Contagios COVID-19

Estefania Salazar

Pada Tugas mata kuliah Metode Penyelesaian Masalah dan Pemodelan , ditugaskan untuk membuat pemodelan penyebaran COVID-19 di negara yang dipilih, dan pada simulasi ini merupakan negara Indonesia

Dosen Pengampu :

Simulasi Pemodelan Penyebaran COVID-19 di Indonesia di Indonesia Dari Data Vaksinasi

Ngaliman Abu Ibrahim

Modelo Epidemiológico para Casos de COVId-19

Insigh: Luis Felipe Dias Lopes - UFSM

Carlos HeitorMoreira - UFSM

Paulo Villela - ITA

Simulação Santa Maria Covid-19

Luis Felipe Dias Lopes

Tugas 3 Pemodelan Transportasi Laut

STUDI KASUS : Simulasi Penyebaran Virus Corona atau COVID-19 di Indonesia dengan aplikasi Insight Maker

TUGAS 3_IGedeBagusIndraDanendra_04411740000036_Pemodelan Transportasi Laut

I Gede Bagus Indra Danendra

Rashid_COVID-19 crisis

Md Mamunur Rashid

The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.

Clone of SEIR - COVID-19 (v.1)

Muhammad Iqbal M

36

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.)