COVID19 Models

These models and simulations have been tagged “COVID19”.

        Model description:     This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania. It also tell us the impact of economic policies on outbreak models and economic growth.       Variables:    The simulation takes into account the following variables and its adjusting ra

Model description:

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania. It also tell us the impact of economic policies on outbreak models and economic growth.

 

Variables:

The simulation takes into account the following variables and its adjusting range: 

 

On the left of the model, the variables are: infection rate( from 0 to 0.25), recovery rate( from 0 to 1), death rate( from 0 to 1), immunity loss rate( from 0 to 1), test rate ( from 0 to 1), which are related to Covid-19.

 

In the middle of the model, the variables are: social distancing( from 0 to 0.018), lock down( from 0 to 0.015), quarantine( from 0 to 0.015), vaccination promotion( from 0 to 0.019), border restriction( from 0 to 0.03), which are related to governmental policies.

 

On the right of the model, the variables are: economic growth rate( from 0 to 0.3), which are related to economic growth.

 

Assumptions:

(1) The model is influenced by various variables and can produce different results. The following values based on the estimation, which differ from actual values in reality.

 

(2) Here are just five government policies that have had an impact on infection rates in epidemic models. On the other hand, these policies will also have an impact on economic growth, which may be positive or negative.

 

(3) Governmental policy will only be applied when reported cases are 10 or more. 

 

(4) This model lists two typical economic activities, namely e-commerce and physical stores. Government policies affect these two types of economic activity separately. They together with economic growth rate have an impact on economic growth.

 

Enlightening insights:

(1) In the first two weeks, the number of susceptible people will be significantly reduced due to the high infection rate, and low recovery rate as well as government policies. The number of susceptible people fall slightly two weeks later. Almost all declines have a fluctuating downward trend.

 

(2) Government policies have clearly controlled the number of deaths, suspected cases and COVID-19 cases.

 

(3) The government's restrictive policies had a negative impact on economic growth, but e-commerce economy, physical stores and economic growth rate all played a positive role in economic growth, which enabled the economy to stay in a relatively stable state during the epidemic.

10 months ago
 The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune. 

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.   
10 months ago
This model estimates the deaths due to COVID19 in Bangalore City.  Assumptions:  City has a population = 8 Million  Initial infected population = 10  Probability of infection = 8%  Contact rate in population = 6  Average duration of recovery = 10 days  Death rate = 1%  Quarantine rate = 80%  Delay i
This model estimates the deaths due to COVID19 in Bangalore City. 
Assumptions:
City has a population = 8 Million
Initial infected population = 10
Probability of infection = 8%
Contact rate in population = 6
Average duration of recovery = 10 days
Death rate = 1%
Quarantine rate = 80%
Delay in quarantine = 5 days
This model estimates the deaths due to COVID19 in Bangalore City.  Assumptions:  City has a population = 80 Million  Initial infected population = 10  Probability of infection = 8%  Contact rate in population = 6  Average duration of recovery = 10 days  Death rate = 1%  Quarantine rate = 80%  Delay
This model estimates the deaths due to COVID19 in Bangalore City. 
Assumptions:
City has a population = 80 Million
Initial infected population = 10
Probability of infection = 8%
Contact rate in population = 6
Average duration of recovery = 10 days
Death rate = 1%
Quarantine rate = 80%
Delay in quarantine = 5 days
Summary: This model shows the situation of Burnie in COVID 19. The assumed number of people death and recovered can be find in the model. It also shows how is the government policy influence the susceptible people and what factors will be affected by the government policy.      Assumption:  Most peo
Summary:
This model shows the situation of Burnie in COVID 19. The assumed number of people death and recovered can be find in the model. It also shows how is the government policy influence the susceptible people and what factors will be affected by the government policy. 

Assumption:
Most people follow the social distancing rule and a few people need quarantine. Economic growth rate is composed by industrial production, service industry and online economy. 

Interesting insight:
Most infection happened in the group of people who do not follow the government policy. The online economy has a chance to growth fast during the COVID 19 period. 
  
10 months ago
Dieses SEIR Modell soll dazu dienen, den Ausbruch von Covid - 19 simulieren zu können.
Dieses SEIR Modell soll dazu dienen, den Ausbruch von Covid - 19 simulieren zu können.
 A spatially aware, agent based model of Covid19. There are three classes of people: susceptible (healthy), infected (sick and infectious) and recovered (healthy, temporarily immune).
A spatially aware, agent based model of Covid19. There are three classes of people: susceptible (healthy), infected (sick and infectious) and recovered (healthy, temporarily immune).
2 months ago
 The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 
The System Dynamic Model represents the Covid19 cases in Brgy. Sicsican, Puerto Princesa City as of May 27,2022. 
2 months ago
Agent based model of Covid19. There are three classes in this model, the susceptible, infected and recovered. This three classes is interconnected to the population
Agent based model of Covid19. There are three classes in this model, the susceptible, infected and recovered. This three classes is interconnected to the population
2 months ago
 A spatially aware, agent based model of Covid19. There are three classes of people: susceptible, infected and recovered. 
A spatially aware, agent based model of Covid19. There are three classes of people: susceptible, infected and recovered. 
2 months ago
The system dynamics model represents the Covid19 cases as of May 27 2022 in Brgy. San Jose Puerto Princesa City.
The system dynamics model represents the Covid19 cases as of May 27 2022 in Brgy. San Jose Puerto Princesa City.
2 months ago
We can observe the Covid19 flows or transitions and linkages from healthy to infected and immune in this system dynamics model, or SDM. It conducts a flow known as infection from healthy to infected. The diseased then initiates a flow known as recovery to immunity. It means that the covid19 infects
We can observe the Covid19 flows or transitions and linkages from healthy to infected and immune in this system dynamics model, or SDM. It conducts a flow known as infection from healthy to infected. The diseased then initiates a flow known as recovery to immunity. It means that the covid19 infects healthy people first, and then they become immune once they recover from covid19 infections. We can conduct a simulation to observe how they interact to get a more useful analysis.
2 months ago
This System Dynamics Model represents the Covid19 cases as of May 27, 2022 in Brgy. San Jose Puerto Princesa City.
This System Dynamics Model represents the Covid19 cases as of May 27, 2022 in Brgy. San Jose Puerto Princesa City.
2 months ago
 The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune. 

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.   
Agent based modelling of Covid29 cases in Brgy. San Jose . Classified into 3 classes such as Susceptible, Infected and Recovered.
Agent based modelling of Covid29 cases in Brgy. San Jose . Classified into 3 classes such as Susceptible, Infected and Recovered.
2 months ago
An agent based model of Covid19 cases in Brgy. San Jose Puerto Princesa City as of May 27, 2022. Susceptible, Infected and Recovered are presented in this model which represents the type of persons involved in the Covid19 cases. To effectively replicate the model, this class is linked to the populat
An agent based model of Covid19 cases in Brgy. San Jose Puerto Princesa City as of May 27, 2022. Susceptible, Infected and Recovered are presented in this model which represents the type of persons involved in the Covid19 cases. To effectively replicate the model, this class is linked to the population and to percent of infected persons.
2 months ago