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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
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Ozindik zhymys
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Part 2 Systems Dynamics- COVID-19
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COVID-19 in India
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ABM of COVID-19 cases in PUERTO PRINCESA CITY
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Агентное моделирование COVID-19 в Китае
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Modelling COVID-19 Waste Generation using System Dynamics
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Model di samping adalah model SEIR yang telah dimodifikasi sehingga dapat digunakan untuk menyimulasikan perkembangan penyebaran COVID-19.
Covid-19: SEIR Model for COVID-19 in Indonesia
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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)
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Description:

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.

Assumptions:

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

The government policies will take a negative effect on Covid-19 outbreaks and the financial system.

Parameters:

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameter: Background disease.

Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be changed 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

An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.

BMA708_Assignment 3_Nguyen Dang Khoa Vo_520272_COVID-19 outbreak and Burnie economy
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Agent based Modeling Simulation for Pandemic COVID-19 Disease
Агентская модель
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A Model for COVID-19 outbreak
AT3
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COVID-19 in Jakarta
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Covid-19 Modell
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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
SARS-CoV-19 model
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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 

ECM-Training - SEIR Infectious Disease Model for COVID-19
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Systems Project Stock and Flow
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Dinâmica epidemiológica do Covid-19 (SD)
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This is the third in a series of models that explore the dynamics of infectious diseases. This model looks at the impact of two types of suppression policies. 

Press the simulate button to run the model with no policy.  Then explore what happens when you set up a lockdown and quarantining policy by changing the settings below.  First explore changing the start date with a policy duration of 60 days.
SIRD Epidemic Model with Suppression Policies
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Системная динамика COVID-19 в Китае
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Өзіндік жұмыс 1 германия Covid-19 2022ж