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This stock-flow simulation model is to show Covid-19 virus spread rate, sources of spreading and safety measures followed by all the countries affected around the world.
The simulation also aims at predicting for how much more period of time the virus will persist, how many people could recover at what kind of rate and also about the virus toughness dependence based on its excessive speed, giving rise to bigger numbers day-by-day.
covid-19 in USA
3 months ago
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Өзіндік жұмыс
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Propagación y recuperación Covid-19
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Covid-19 in Belarus
8 months ago
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This is a complex model of COVID-19 outbreak in Burnie Tasmania. It show the effect of government policy to local economic and the impact of Covid-19. 

Assumptions
Government policy can reduce the number of infected, however also would reduce the economic growth. 

Interesting insights
Based on changing the value of government policy, it show that the policy can help to reduce on the number of death and infection. 

Covid-19 Out break
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COVID-19 в Китае
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Tugas Pemodelan Transportasi Laut

Memodelkan persebaran pandemik covid-19 menggunakan insightmaker

Dosen pembimbing : Dr-Ing Ir. Setyo Nugroho
Pandemic Covid-19 Simulation
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Covid-19 SSM
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Variant of the model "COVID-19 spread" made by Anxo-Lois Pereira and Miquel Martínez de Morentin, including reinfection, permanent immunity and Vaccines. Made for the subject of TAED.
COVID-19 spread with reinfeccion, permanent immunity and vaccines
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Disease Dynamics
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Johanski_Studio2
6 months ago
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Өзіндік жұмыс агент
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COVID-19 Indonesia
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Динамикалық өзіндік жұмыс
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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.

BMA708_Model of COVID-19 in Burnie_Yuanyuan Liao
Insight diagram
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
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COVID-19 Systemigram
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COVID-19 Vaccination of indigenous West Australians
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Covid-19_Systemigram_BradD
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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 

Clone of SEIR Infectious Disease Model for COVID-19
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SYSTEMS DYNAMICS OF COVID-19