Modelo Epidemiológico para Casos de COVId-19      Insigh: Luis Felipe Dias Lopes - UFSM              Carlos HeitorMoreira - UFSM              Paulo Villela - ITA
Modelo Epidemiológico para Casos de COVId-19

Insigh: Luis Felipe Dias Lopes - UFSM
            Carlos HeitorMoreira - UFSM
            Paulo Villela - ITA


 Brief of the model: 

 The model predicts the outbreak of COVID-19 in the Burnie,
Tasmania area. It is imperative to clarify that this model was developed from
the SEIR model (Susceptible, Infected, Infected, Recovered). The spread of this
pandemic is driven by a combination of infection rates, m

Brief of the model:

The model predicts the outbreak of COVID-19 in the Burnie, Tasmania area. It is imperative to clarify that this model was developed from the SEIR model (Susceptible, Infected, Infected, Recovered). The spread of this pandemic is driven by a combination of infection rates, mortality rates, and recovery rates from the virus itself, as well as government policies.

For COVID-19 itself, vaccination directly reduces the infection rate, thereby reducing the mortality rate of COVID-19 patients and the reduction of confirmed cases. In other words, if the local population is adequately vaccinated, everyday life, shopping, tourism, and even national borders will be open rather than in a closed border situation.

 

Assumption of the model:

The model simulated based on different rates, including Infecting rate, Death Rate, Test Rate, Immunity Loss Rate and Recovery Rate. And, this model lists six elements of government policy, which including border closure, travel ban, social distancing, business restriction, self-quarantine, and vaccination schedule.

Besides, the model considers three economic entities in the Burnie area, one in the brick-and-mortar industry and online business industry. Government policies have somewhat reduced COVID-19 infections. Still, they have also at the same time, online businesses played an essential role in stimulating local economic activity during the pandemic. At the same time, however, online businesses played an indispensable role in promoting regional economic activity during the pandemic.

 

The prediction model is for reference only, and there may be differences between the actual cases and the model.

 

 

Insights of the model:

Due to the high infection and low recovery rates and timely government policy interventions, the number of susceptible individuals changes dramatically in the first four weeks. However, the number of sensitive individuals continues to decline after this period, but the decline is not significant. Secondly, with the implementation of government policies, the number of suspected patients who tested negative for medical follow-up continued to rise, implying that government policy interventions directly affect COVID-19.

 Recently, a new article published on <Science> explores the feasibility of living with the current Coronavirus in the long-term through mathematical modeling. Since either complete eradication or herd immunity is difficult to achieve in the short term, this work may provide useful and helpful

Recently, a new article published on <Science> explores the feasibility of living with the current Coronavirus in the long-term through mathematical modeling. Since either complete eradication or herd immunity is difficult to achieve in the short term, this work may provide useful and helpful public health policy implications in real environment.


Based on the model developed in the article, I translate it into a dynamic model here, so you may gain useful insights or check your own assumptions when simulating.

 The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022.         Total population of Brgy. Sicsican - 22625    Total Covid19 cases as of May 27, 2022 - 250    Local transmission - 241    Imported transmission - 9    Recovery - 226    Death Due
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 

Total population of Brgy. Sicsican - 22625
Total Covid19 cases as of May 27, 2022 - 250
Local transmission - 241
Imported transmission - 9
Recovery - 226
Death Due to Covid19 - 15
 Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.
Modelling the demand for health and care resources resulting from the Covid-19 outbreak using an SEIR model.

 ​ Curso de Dinâmica de Sistemas com Insight Maker    Módulo II - Modelos Diversos: Aula 7    Modelo Epidemiológico   Prof. Dr. Paulo Villela -  Email    Assista esta aula no Youtube     Veja esta contada no Storytelling       Este modelo está baseado no paper  Crokidakis, Nuno . (2020).  Data analy
Curso de Dinâmica de Sistemas
com Insight Maker
Módulo II - Modelos Diversos: Aula 7
Modelo Epidemiológico
Prof. Dr. Paulo Villela - Email

Este modelo está baseado no paper Crokidakis, Nuno. (2020). Data analysis and modeling of the evolution of COVID-19 in Brazil. Para mais detalhes veja o paper completo aqui.
Agent based Modeling Simulation for Pandemic COVID-19 Disease
Agent based Modeling Simulation for Pandemic COVID-19 Disease