Virus on a Network§

In the academic literature such a model is sometimes referred to as an SIR model for epidemics.

With this model we want to simulate how a virus (or a warm) can be spread through a network. It's an abstraction of several dynamics:

• Network is based on a graph, where:
  • nodes represent computers (or any other device);
  • edges represent communication link into the network (edges are undirected);
• A node can be in 1 of 3 possible states:
  • Susceptible: a standard node and a potential victim;
  • Infected: an infected node. This kind of nodes can be unaware of the infection;
  • Resistant: after virus detection, this node became resistent to this kind of infection (this state simulates an antivirus update);
• At each step, communication between all nodes is performed, and infected nodes spread virus to their neighbours (for examples, as attachment of an email);
• Periodically nodes start a scan with their antivirus to detect virus; if they detect something, a recovery will be done. If the recovery is performed, there is a possibility for that node to became Resistant;

The network is created using preferential attachment algorithm; it's provided by a krABMaga macro: given a list of nodes, it adds nodes one by one, and it creates ages based on node degree.

preferential_attachment_BA!(nodes_set, state.network, NetNode, String, INIT_EDGE);

Node struct is simple: an id to identify nodes into the network, its status and if some problem is detected. Real2D parameter is necessary for visualization

#[derive(Clone, Copy, Debug)]
pub enum NodeStatus {
    Susceptible,
    Infected,
    Resistent,
}

#[derive(Clone, Copy)]
pub struct NetNode {
    pub id: u32,
    pub pos: Real2D,
    pub status: NodeStatus,
    pub virus_detected: bool,
}

EpidemicNetworkState has two main fields:

• Field2D for visualization;
• Network<NetNode, String> to manage nodes connection and comunication;