This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

This is the base stock and flow diagram I will use to develop a larger system of influencing factors, from health, agri-food systems, and environmental models. Data was taken from UNICEF and UNFPA. Time = 0 starts at 1987.

This is the base stock and flow diagram I will use to develop a larger system of influencing factors, from health, agri-food systems, and environmental models. Data was taken from UNICEF and UNFPA. Time = 0 starts at 1987.

From NAP Toward Quality Measures for Population Health and the Leading Health Indicators Report with detailed Maternal  Infant and Child Health Example Fig.3-5

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

Moving from Disease Progression to Prevention Modelling - In this module, we add interventions and output indicators to create a ‘prevention’ model.

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.

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

SARS Modelling with SEIR Model.

This systems model will help students understand the different systems that make up our body and how choices we make can impact how those systems work.
Factors are based on daily choices.

SARS Modelling with SEIR Model.

This model calculates and demonstrates the possible spread of COVID-19 through an agent-based map. It shows the timeline of a healthy individual being infected to recovery.

This insight is about infection propagation and  population migration influence on this propagation. For this, we defined a world population size and a percentage of it who’s infected. Then, we created an agent where we simulated possible states of an individual. So, he can be healthy, infected (with an infection rate) or immunized ( with a certain rate of immunization). If the individual is infected, he can be alive or dead. Then, we simulated different continents (North-America, Asia and Europe) with a migration between these with a certain rate of migration (we tried to approach reality). Then, thanks to our move action which represents a circular permutation between the different continents with a random probability, the agent will be applied to every individual of the world population.

 How does the program work ?

In order to use this insight, we need to define a size of world population and a probability of every individual to reproduce himself. Every individual of this population can have three different state (healthy, infected or immunized) and infected people can be alive or dead. We need to define a percentage of infection for healthy people and a percentage of death for infected people and also a percentage of immunization.
Finally, there is Migration Part of the program, in this one, we need to define three different continents, states or whatever you want. We also need to define a migration probability between each continent to move these person. With this moving people, we can study the influence of migration on the propagation of a disease.

Vincent Cochet, Julien Platel, Jordan Béguet

SARS Modelling with SEIR Model.

SIR model with waning immunity - Metrics by Guy Lakeman

A Susceptible-Infected-Recovered (SIR) disease model with waning immunity

Our Economy is all about making air filters using factories that make the air worse, causing more people to buy air filters.

This is the base stock and flow diagram I will use to develop a larger system of influencing factors, from health, agri-food systems, and environmental models. Data was taken from UNICEF and UNFPA. Time = 0 starts at 1987.

A basic SIR model.

How to "infect" a surf spot