1. Brian Lafferty
Virus on a Network

2. Purpose of this Model
This model demonstrates the spread of a virus through a network.
Each node in this model will represent a computer in a network.

3. Nodes Each node can be in one of three states. Susceptible - green
Infected - red
Resistant - grey

4. Running the Program
With each step, the red infected nodes will attempt to infect all of the neighboring green nodes.
Resistant nodes cannot be infected during the process.

5. Relations to real-world situations
The red nodes could represent an virus
The probability of a green node becoming infected may represent someone actually opening an infected attachment.
Resistant nodes may correspond to an up-to-date antivirus software on the given computer.

6. Infected Nodes
Infected nodes are not immediately aware that they are infected.
They check every so often determined by the VIRUS-CHECK-FREQUENCY slider.
This might correspond to a regularly scheduled virus-scan procedure, or simply a human noticing something different about how the computer is behaving.

7. Recovery
If the node recovers from the virus, then it has a probability of becoming resistant to the virus in the future.
When a node becomes resistant, the links between it and its neighbors are darkened, since they are no longer possible vectors for spreading the virus.

8. Working the model
The network that is created is based on proximity (Euclidean distance) between nodes.
A node is randomly chosen and connected to the nearest node that it is not already connected to.
This process is repeated until the network has the correct number of links to give the specified average node degree.

9. Sliders NUMBER-OF-NODES –size of the network
AVERAGE-NODE-DEGREE -average number of links coming out of each node
INITIAL-OUTBREAK-SIZE - determines how many of the nodes will start the simulation infected with the virus.

10. Sliders
VIRUS-SPREAD-CHANCE- determines the likelihood of the virus to be spread to the surrounding nodes.
VIRUS-CHECK-FREQUENCY – determins how many ticks will occur before the node checks if it has been infected
Increases the likelihood of the virus spreading

11. Sliders
RECOVERY-CHANCE – determines the chance of an infected node to recover and become either susceptible again or resistant to the virus.
GAIN-RESISTANT-CHANCE – determines the probability that the node will become resistant to the virus in the future

12. The NETWORK STATUS
shows the number of nodes in each state (S, I, R) over time.

13. End of Run
After the virus has died out, some nodes are still susceptible, while others have become immune.

14. conclusions A lot of factors come into play with this model.
Some key areas that cause changes are:
Average-node-degree
Virus-check-frequency

15. Real Word Example
The real computer networks on which viruses spread are generally not based on spatial proximity, like the networks found in this model.
Real computer networks are more often found to exhibit a "scale-free" link-degree distribution, somewhat similar to networks created using the Preferential Attachment model.

16. Citations
- Stonedahl, F. and Wilensky, U. (2008). NetLogo Virus on a Network model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. - Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.