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COVID-19 в Бразилии (агентное моделирование)

Kamal Lisa

3 months ago

covid-19

Hannah Kakisina

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

Yini Song

3

Tugas Metodologi Penyelesaian Masalah dan Pemodelan:

Wahyu Abdillah

Ribut Aji Kasmiadi

Faqih Zulfikar

Revised of COVID-19 S&F PT1 Model

Faqih Zulfikar

6

Covid -19 Nurym

Нурым Бекназым

3

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

Hanyong Li

Story Telling COVID19

Guillermo Garza

Өзіңдік жұмыс дұрысы

Таир Аубакиров

12

Жүйелік динамика SIR ауру үлгісі

Covid-19 in USA(2021).

Kairken Nazym

9 months ago

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

Andrew E Long

16

A simple SI (Susceptible-Infectious) model that captures the dynamics of COVID-19.

SI Model

Ali Ozdagli

69 3 months ago

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

самостоятельная

Ксения

9 months ago

Simple SIR System Model for COVID-19_Group 4

Bryan Kovalick

SEIR COVID-19 New Kl. 1

Kelompok1

29

S-I-R covid-19 model

KRISTIAN SABATINO

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

INTRODUCTION

This is a balanced loop model that demonstrates how COVID 19 outbreak in Burnie and the response of the government (e.g. by enforcing health policies: Lockdown; quarantine, non-necessary business closure; border closure) affect the local economy. This model has 13 positive loops and seven negative loops. Government response is dependent on the number of reported COVID-19 cases which in turn thought to be dependent on the testing rates less those who recovered from COVID 19 and dead. Economic activity is dependent on the economic growth rate, increased in online shopping, increased in unemployment, number of people who do not obey the rules, COVID 19 cases and health policies.

ASSUMPTIONS

· Both infection and economic growth is reduced by enforcing government policies

· However, the negative effect of government policies is reduced by the number of people who do not obey government health policies

· Govt policies are enforced when the reported COVID-19 case are 10 or greater.

· Number of COVID cases reported is dependent on the testing rates less those who recovered and dead.

· The higher number of COVID-19 cases have a negative effect on local economy. This phenomena is known as negative signalling.

· Government policies have a negative effect on economic activity because health policies limit both social and economic activities which directly or indirectly affect the economy in Burnie .

· This negative effect is somewhat reduced by the increase in online shopping and the number of people who do not obey heath rules.

INTERESTING INSIGHTS

The test ratings seem to play a vital role in controlling COVID-19 outbreak. Higher Rates of COVID testings decrease the number of COVID 19 deaths and number of infected. This is because higher rates of testing accelerate the government involvement (as the government intervention is triggered earlier, 10 COVID cases mark is reached earlier). Delaying the government intervention by reducing the COVID testing rates increases the death rates and number of infected.

Increased testing rates allow the figures (deaths, susceptible, infected) to reach a plateau quickly.

BMA708- Shakila Bethmage- 548351 - COVID 19 Outbreak in Burnie

Shakila Piyumi Perera Bethmage

The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy.

Susceptible can be exposed by go out. resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune.

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.

COVID-19 outbreak in Burnie, TAS. BMA708 Assignment 3

Yifei MAO

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

Covid-19 in China

Канатбек Молдир

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

Kate Elizabeth Craddock

COVID-19 pandemic

Taylor Gendel

Simple epidemiological model for Burnie, Tasmania

SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).

Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)

Yue Xiang

5

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

SEIR - COVID-19 (v.1)

Remigiusz Kinas

64

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