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Tugas Metodologi Penyelesaian Masalah dan Pemodelan:
Wahyu Abdillah
Ribut Aji Kasmiadi
Faqih Zulfikar
Revised of COVID-19 S&F PT1 Model
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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.)

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
Final Version of Italian COVID-19 outbreak
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  • tingkat interaksi fisik : 1-10 orang per hari
  • penerapan prokes 0%-100%
  • penanganan pemerintah : minim (<40%) cukup (40%-70%) baik (>70%)


covid-19
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COVID-19 in Japan 2020 самостоятельная работа
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This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City, PALAWAN using the June 3, 2021 data of the CESU-PPC.
COVID-19 ABM (SIR) Model of Puerto Princesa City, PALAWAN
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This Systemigram illustrates how the world fought against COVID-19.
COVID-19 Systemigram
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Covid-19 in Italy
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​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
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COVID-19 в США
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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
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COVID-19 Model
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Cameroon COVID-19
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Explanation:
Explanation:
This model presents the COVID-19 outbreak in Burnie and how the government reacts to it. Moreover, the model also illustrates how the economy in Burnie is impacted by the pandemic. The possible stages of residents when the infectious disease spreads in Burnie can be concluded as Susceptible, Infection and Recovery, which are used as the main data in this model. However, the improvement of decreasing of reported infection rates of this infectious disease and increasing of recovery rates are contributed by the implementation of the Government Health Policy. 

Assumption
The decrease of both infection rate and economic growth are all influenced by the Government Health Policy simultaneously. The Government Health Policy is only triggered when there are 10 cases reported. However, the increase in reporting COVID-19 cases affects economic growth negatively. 

Interesting Insights:
There are two interesting insights that have been revealed from the simulation. First, the death rate continuously increased even though the infection rate goes down. However, the increase in testing rates contributed to the stability of the death rate towards the end of the week. Moreover, higher testing rates also trigger faster government intervention, which can reduce infectious cases.  Second, as the Government Health Policy limited the chance of going out and shopping, the economic growth is negative due to the higher cases. 

BMA708, Assessment 3: Complex system, Burnie Covid-19 outbreak
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Here we have a basic SEIR model and we will investigate what changes would be appropriate for modelling the 2019 Coronavirus 

MscT CSE - SEIR Infectious Disease Model for COVID-19
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Based on the SIR (Susceptible, Infected, Recovered) model of disease, this is an upgraded model with more specifc vaeriables.
Insights:
When the growth rate and the number of the recovered is much larger than deaths, the economic activity remain steady growing.
Model of COVID-19 outbreak in Burnie Tasmania
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Өздік жұмыс(жүйелік модельдеу)
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Турциядағы COVID-19 Жүйелік динамика
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​Modelo Epidemiológico para os Casos de Covid-19

Insigh Authors:
Luis Felipe (UFSM)
Carlos Heitor (UFSM)
Paulo Vilella (UFJF)
Modelo UFSM - COVID-19
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Турциядағы COVID-19 Агенттік модель
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This model is comparing healthy and sick residents in Burnie, Tasmania after the Covid-19 Outbreak in 2020. It will also show how the Burnie economy is effected by the disease, how the Government Health Policies are implemented and how they are enforced.

This model is based on the SIR, Susceptible, Infection, Recovery (or Removed) These are the three possible states related to the members of the Burnie population when a contagious decease spreads.

The Government/Government Health Policy, played a big part in the successful decrease in Covid-19 infections. The Government enforced the following.
- No travel (interstate or international)
- Isolation within the residents homes
- Social distancing by 1.5m
- Quarantine
- Non essential companies to be temporarily closed
- Limitations on public gatherings
- And limits on time and kilometers aloud to travel from ones home within a local community

This resulted in lower reported infection rates of Covid-19 and higher recovery rates.

In my opinion:
When the first case was reported the Government could have been even faster to enforce these rules to decrease the fatality rates further for the Burnie, population.  

Assumption: Government policies were only triggered when 10 cases were recorded.
Also, more cases that had been recorded effected the economic growth during this time.

Interesting Findings: In the simulation it shows as the death rates increases towards the end of the week, the rate of testing goes down. You would think that the government would have enforced a higher testing rate over the duration of this time to decrease the number of infections, exposed which would increase the recovery rates faster and more efficiently.  

Figures have been determined by the population of Burnie being 19,380 at the time of assignment.

Complex Systems How Burnie Tasmania dealt with Covid-19 Outbreak BMA708
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Simple SIR System Model for COVID-19_Group 4
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АҚШтағы COVID-19 Агенттік модель
5 4 months ago
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This model is to show the status of numbers of infected people, recovered people and deaths during COVID-19 in Burnie Australia. It also shows impact on the growth of economy. 

Variables
The infection rate and the percentage of people washing their hands are influencing the infected number of people. Also, there are death rate and recovery rate and immunity lost rate determining the numbers of deaths, recovered and infected-again people.  
for the economy growth, there are several factors, including unemployment rate, infection rate, economic growth rate and government health policy. 

Perspective
After some time, people will recovered, also the economic activities. 
A model of Burnie during COVID-19