H1N1

''- Project Name:Influenza A virus subtype H1N1

- Class: 4IT496 Simulation of Systems (WS 2017/2018)

- Author: Wahida Al-Rawahi

- Model Type: Agent-Based Simulation

- Software Used: NetLogo ''

Section 1: Problem Definition
H1N1 flu is also known as swine flu. It's called swine flu because in the past, people who caught it had direct contact with pigs. That changed several years ago, when a new virus emerged that spread among people who have not been near pigs. The World Health Organization proved that H1N1 virus can spread around the same way as the seasonal flu. When people who are already infected with the virus cough or sneeze, they spray tiny bits of the virus into the air. Therefore, when a healthy person comes in contact with these bits by touching a surface of where the bits have landed, or touching something an infected person has already touched, the H1N1 virus has be transmitted easily.

1.1 Goal
To simulate the transmission and perpetuation of the H1N1 virus in a human population.

Section 2: Method
The environment used to build the simulation is NetLogo 6.0.2. It was chosen due to the nature of the problem and the ease of visualizing how the virus is moving from one to person to another. Another reason for picking this software is to get quick results and have a flat learning curve.

Section 3: Interface


The model is initialized with number of people, of which a certain amount are infected. These people move randomly in the model with one of three conditions:

1. Healthy but vulnerable to be infected with the H1N1 virus and they are visualized in the color green.

2. Infected and infectious are visualized in the color red.

3. Healthy and immune are visualized in the color gray.

Moreover, people may die of infection or old age. This is clearly illustrated when the population slopes below the environment’s “carrying capacity” and healthy people may produce healthy, but prone children.

3.1.1 Sliders
1. “pick-number-of-people” slider: To determine the number of initial population.

2. “Infections” slider: To control how high or low the chance of the H1N1 virus transmission will occur when an infected person and vulnerable person occupy the same area.

3. “chance-of-recovery” slider: It controls the likelihood that an infection will end in recovery or immunity.

4. “duration” slider: It determines the number of weeks before an infected person either dies or recovers.

3.1.2 Buttons
5. “setup” button: It resets the display, monitors, and plots and randomly distributes “pick-number-of-people” in the display. All but 10 of the people are set to be green vulnerable people and 10 red infected people, all of randomly distributed ages.

6. “go” button: Starts running the simulation.

3.1.3 Choosers
7. “turtle-shape” chooser: User can pick whether individuals are visualized as persons or circles. The purpose of this is to easily distinguish the colors and to see individuals who are health, immune, or infected.

3.1.4 Monitors and Plots
The first two monitors (percentage-of-infected-individuals, percentage-of-immuned-individuals) show the percent of the population that is infected and the percent that is immune. The third monitor (number-of-years) shows the number of years that have passed. The plot shows the number of vulnerable, infected, and immune individuals. It also shows the number of individuals in the total population.

Section 4: How the Simulation Model Works
This section describes how the simulation runs when the execution button is pressed. The factors of the above section is explained below on how each person is treated in the simulation model:

1. Population Density: It affects how often infected, immune and vulnerable individuals come into contact with each other. The size of the initial population can be changed through the “pick-number-of-people” slider.

2. Population Turnover: When some of those people die, some of them will also die infected with the H1N1 virus, as well as vulnerable or immune. Those who die are replaced with the new born who are also vulnerable to the virus. People may die from the virus, the chances of which are determined by the slider “chance-of-recovery”, or they may die of old age. In this simulation model, people can die of old age at the age of 50 years and reproduction rate is constant. If the carrying capacity has not been reached, every healthy person has a 1% chance to reproduce.

3. Immunity Degree: If the individual has been infected with the H1N1 virus and has successfully recovered from it, then he/she is immune. In this simulation model, the when the person is immune it lasts for a year.

4. Infections: This is determined by the “infections” slider, where a user can pick how high or low the virus spread.

5. Infection Duration: This can be determined by the “duration” slider by picking how long is a person infected before he/she either recovers from the virus or dies. The time length is the virus’s opportunity for transmitting to a new host.

4.1 Inputs Embedded into Source Code
1. Lifespan = 50 years 2. Carrying Capacity = 300

3. Immunity Duration = 52 weeks 4. Birth Rate = 1 in 100 of reproducing when the number of individuals is less than the carrying capacity

Section 5: Results and Interpretations
The aspects selected on the sliders interrelate to influence the likeliness of the H1N1 virus to spread in the population. However, the aspects selected must create a balance in which a sufficient number of potential hosts remain available to the virus to access. What makes the simulation interesting is the fact that there will frequently be an explosion of infection since no individual in the population will be immune. At first, the virus is very powerful and can successfully infect everyone. However, it may not survive in the long-term. And since every person who is infected generally dies or becomes immune, as a result, the potential number of hosts is every so often limited. The exception to the above is when the “duration” slider is set so high that reproduction can provide new hosts.

Section 6: Conclusion
It is concluded that when there are no immune individuals in the population, it can lead to approximating of the plague of a viral infection in the population, one that has devastating consequences for the humans concerned. Before long, however, the H1N1 virus becomes less common as the population dynamics change. What ultimately happens to the virus is determined by the factors controlled by the sliders (and can also be applied in real life situations).