COVID-19 | Insight Maker

An SD model formulated to present the trend of COVID-19 infection and death rate at Puerto Princesa City, PALAWAN using the CESU-PPC file last June 3, 2021.

COVID-19 SD Model of Puerto Princesa City, PALAWAN

Alonzo S. Peralta

This model shows the COVID-19 outbreaks in Burnie and the Government intervention to alleviate the crisis and also how is the intervention affect the economy.

It is assumed that the Government intervention is triggered when the COVID-19 case is equal to or more than 10.

Government intervention - lock down the state, suppress the development of COVID-19 effectively. It is related to most of people stay at home to reduce the exposure in public area.

On the other hand, it also bring the economy of Burnie in the recession, as no tourists, no dining out activities and decrease in money spending in the city.

Clone of Burnie COVID-19 outbreaks and economic impacts_Pui Chu Daisy Cheung 524767

Lin Ling

Clone of Coronavirus, COVID-19

Helmi Mahditia Adam

Model of Covid-19 outbreak in Burnie, Tasmania

Balancing Health and Economy factor

Vaccination rate will help to recovered more people and decrease the immunity loss rate.

Additionally. The lack of food during the covid-19 pandemic still an obstacle for economic development.

In someway, Health balancing in every people will help to shut down covid-19 and help economic development even grow up faster.

Model of Covid-19 outbreak in Burnie, Tasmania

KV

O presente Insight engloba diversos tipos de modelos compartimentais.
Pra visualizar alguns deles, procure testar os seguintes valores:

SI: S=995, I=5, β=0.1
SIS: S=980, I=20, β=0.1 e δ = 0.01
SIR: S=995, I=5, β=0.35 e γ=0.035
SIRS: S=995, I=5, β=0.4, γ=0.2 e μ=0.005
SEIR: S=995, I=5, β=0.5, ω=0.1 e γ=0.1
SEIRS: S=995, I=5, β=0.5, ω=0.1, γ=0.1 e μ=0.03.
SIRV: S=995, I=5, β=0.35, γ=0.035 e ν=0.01

Note que este é um Insight que pode ser modificado para mostrar cada um desses modelos e o usuário deverá tornar alguns fluxo nulos afim de manter apenas as conexões essenciais para cada sistema.

Compartmental models in epidemiology

Gabriel Rampeloti

A simple ABM example illustrating how the SEIR model works. It can be a basis for experimenting with learning the impact of human behavior on the spread of a virus, e.g. COVID-19.

Clone of SEIR ABM MODEL

Rakhimova Sabina

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Clone of Infectious Disease Model (Version 4.0)

LB

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Infectious Disease Model (Version 3.0 DHC)

David Plummer

A simple feedback loop of the COVID-19 pandemic.

Clone of COVID-19 feedback loops

Chloe Johnson

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)

Ashutosh Bansal

Coronavirus, COVID-19

Yusriyadi

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Infectious Disease Model (Version 4.0)

Eleanor Jeram

LEIA ANTES DE COMEÇAR

Milhões de pessoas ao redor do mundo estão em QUARENTENA em função da pandemia COVID-19. Se adaptar à quarentena pode ser um PROBLEMA para muitas pessoas.

Nosso DESAFIO é construir um DIAGRAMA CAUSAL que analise este PROBLEMA que é ficar em quarentena. Vamos lá!?

PRIMEIRA TAREFA (até dia 13 de maio)

1) Qual a variável CHAVE que você acha que pode definir o problema? Crie uma VARIÁVEL dentro do folder CHAVE.

2) Quais as outras variáveis SECUNDÁRIAS que estão relacionadas com este problema? Crie variáveis secundárias dentro dos FOLDER que melhor identifica o tipo da variável.

SEGUNDA TAREFA

No dia 15 de maio discutiremos virtualmente no Zoom, as variáveis propostas e faremos um DIAGRAMA CAUSAL RASCUNHO.

TERCEIRA TAREFA

No dia 22 de maio discutiremos virtualmente Zoom, o DIAGRAMA CAUSAL RASCUNHO objetivando construir o DIAGRAMA CAUSAL DEFINITIVO.

Diagrama Causal da Quarentena

Paulo Villela

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Clone of Infectious Disease Model (Version 4.0)

Dr. Dev Vencappa

COVID-19 outbreak in Burnie Tasmania Simulation Model

Introduction

This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community. Government responses are based on the reported COVID-19 cases amount, whcih is considered to be based on testing rate times number of people who are infected minus those recovered from COVID-19 and dead.

Government interventions include the implement of healthy policy, border surveillance, quarantine and travel restriction. After outbreak, economic activities are positively affected by the ecommerce channel development and normal economic grwoth, while the unemployement rate unfortunately increases as well.

Assumption

Enforcing government policies reduce both infection and economica growth.
When there are 10 or greater COVID-19 cases reported, the governmwnt policies are triggered.
Greater COVID-19 cases have negatively influenced the economic activities.
Government policies restict people's activities socially and economically, leading to negative effects on economy.
Opportunities for jobs are cut down too, making umemployment rate increased.
During the outbreak period, ecommerce has increased accordingly because people are restricted from going out.

Interesting insights

An increase in vaccination rate will make difference on reduing the infection. People who get vaccinated are seen to have higher immunity index to fight with COVID-19. Further research is needed.

Testing rate is considered as critical issue to reflect the necessity of government intervention. Higher testing rate seems to boost immediate intervention. Reinforced policies can then reduce the spread of coronvirus but absoluately have negative impacts on economy too.

Mengling Xue 561743 BMA708_Marketing insights into Big Data

Mengling Xue

Modelo epidemiológico simples

SIR: Susceptíveis - Infectados - Recuperados

Dados iniciais do Brasil em 04 Abr 2020

Fonte:

https://www.worldometers.info/coronavirus/country/brazil/

Clone of Clone of Modelo SIR simples - Covid-19

Daiana Cristina Wickert

SARS-CoV-19 spread in different countries

- please adjust variables accordingly

Italy

elderly population (>65): 0.228
estimated undetected cases factor: 4-11
starting population size: 60 000 000
high blood pressure: 0.32 (gbe-bund)
heart disease: 0.04 (statista)
free intensive care units: 3 100

Germany

elderly population (>65): 0.195 (bpb)
estimated undetected cases factor: 2-3 (deutschlandfunk)
starting population size: 83 000 000
high blood pressure: 0.26 (gbe-bund)
heart disease: 0.2-0.28 (herzstiftung)
free intensive care units: 5 880

France

elderly population (>65): 0.183 (statista)
estimated undetected cases factor: 3-5
starting population size: 67 000 000
high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
heart disease: 0.1-0.2 (oecd)
free intensive care units: 3 000

As you wish

numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key

Healthy: People are not infected with SARS-CoV-19 but could still get it
Infected: People have been infected and developed the disease COVID-19
Recovered: People just have recovered from COVID-19 and can't get it again in this stage
Dead: People died because of COVID-19
Immune: People got immune and can't get the disease again
Critical recovery percentage: Chance of survival with no special medical treatment

Clone of SARS-CoV-19 model

Muhammad Iqbal M

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 3.0)

k sha

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

Clone of Edit Model Penyebaran Covid-19 di Kabupaten Sleman

Nur Rochman

Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

Clone of Infectious Disease Model (Version 3.0)

Brandon Bule

Explanation of the Model

This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate.

Assumptions

Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.

Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.

Interesting Insight

There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death.

In contrast, lower testing rates increase the death rate and cases.

Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.

Clone of BMA 708, Assessment Tast 3: Complex System, Burnie COVID-19 outbreak, Diprina Shakya-519673

Marvellous

SARS-CoV-19 spread in different countries

- please adjust variables accordingly

Italy

elderly population (>65): 0.228
estimated undetected cases factor: 4-11
starting population size: 60 000 000
high blood pressure: 0.32 (gbe-bund)
heart disease: 0.04 (statista)
free intensive care units: 3 100

Germany

elderly population (>65): 0.195 (bpb)
estimated undetected cases factor: 2-3 (deutschlandfunk)
starting population size: 83 000 000
high blood pressure: 0.26 (gbe-bund)
heart disease: 0.2-0.28 (herzstiftung)
free intensive care units: 5 880

France

elderly population (>65): 0.183 (statista)
estimated undetected cases factor: 3-5
starting population size: 67 000 000
high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
heart disease: 0.1-0.2 (oecd)
free intensive care units: 3 000

As you wish

numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key

Healthy: People are not infected with SARS-CoV-19 but could still get it
Infected: People have been infected and developed the disease COVID-19
Recovered: People just have recovered from COVID-19 and can't get it again in this stage
Dead: People died because of COVID-19
Immune: People got immune and can't get the disease again
Critical recovery percentage: Chance of survival with no special medical treatment

Clone of SARS-CoV-19 model

Jim

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

Modified by Rio dan Pras

Clone of Clone of SEIR Model for COVID-19 in Indonesia - case study SLEMAN

Yusriyadi

COVID-19 outbreak in Burnie Tasmania Simulation Model

Introduction

This model simulates how COVID-19 outbreak in Burnie and how the government responses influence the economic community. Government responses are based on the reported COVID-19 cases amount, whcih is considered to be based on testing rate times number of people who are infected minus those recovered from COVID-19 and dead.

Government interventions include the implement of healthy policy, border surveillance, quarantine and travel restriction. After outbreak, economic activities are positively affected by the ecommerce channel development and normal economic grwoth, while the unemployement rate unfortunately increases as well.

Assumption

Enforcing government policies reduce both infection and economica growth.
When there are 10 or greater COVID-19 cases reported, the governmwnt policies are triggered.
Greater COVID-19 cases have negatively influenced the economic activities.
Government policies restict people's activities socially and economically, leading to negative effects on economy.
Opportunities for jobs are cut down too, making umemployment rate increased.
During the outbreak period, ecommerce has increased accordingly because people are restricted from going out.

Interesting insights

An increase in vaccination rate will make difference on reduing the infection. People who get vaccinated are seen to have higher immunity index to fight with COVID-19. Further research is needed.

Testing rate is considered as critical issue to reflect the necessity of government intervention. Higher testing rate seems to boost immediate intervention. Reinforced policies can then reduce the spread of coronvirus but absoluately have negative impacts on economy too.

Clone of Mengling Xue 561743 BMA708_Marketing insights into Big Data

Mengling Xue

COVID-19 Models