Evolution of Covid-19 in Brazil:  
  A System Dynamics Approach  
 Villela, Paulo (2020) paulo.villela@engenharia.ufjf.br  This model is based on  Crokidakis, Nuno . (2020).  Data analysis and modeling of the evolution of COVID-19 in Brazil . For more details see full paper  here .
Evolution of Covid-19 in Brazil:
A System Dynamics Approach

Villela, Paulo (2020)
paulo.villela@engenharia.ufjf.br

This model is based on Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. For more details see full paper here.

  Overview:   Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.      Variables   The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): D

Overview:

Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.


Variables

The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc. 


Assumptions:

From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.


In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.

Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.


Step 1: Try to set all value variables at their lowest point and then stimulate. 

 

Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.


Step 2: Try to increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate


Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).


With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.


Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate


Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.


In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.


Step 4: Try to change the GDP Growth Rate to 0.14, then Tourism Growth Rate to 0.02, others keep the same as step 3, and then simulate the model


Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632. 


This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.

The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
The System Dynamics Model presents the the COVID-19 status in Puerto Princesa City
A simple Susceptible - Infected - Recovered disease model.
A simple Susceptible - Infected - Recovered disease model.
 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.

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.

The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.
The model represents the interaction between influenza and SARS-CoV-2. The data used is for Catalonia region.
 Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.      With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.     We start with an SIR model, such as that featured in the MAA model featured
Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:
  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!?       PR

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.