- Motivation
- relatives sick
- graph theory stuff is neato
- Politicians and religious people got sick first
- Transmission through networks
- Percolation threshold
- On random graphs and on graphs with a specific structure
- Tells us whether an “outbreak” will become an “epidemic” and spread to an infinite number of nodes

- Percolation threshold
- Not looking at:
- the time path of how a disease spreads
- (Sort of time invariant approach)

- The mortality or costs involved.

- the time path of how a disease spreads

- Model
– Tranmission
- always happens at rate r
- If you have n outneighbors, its expected that your case will cause an additional n
*r new cases - If there is a total population *I*of your type, then together you’ll create Inr new cases. - If nr > 1, then on average cases will grow.- population structure
- Split into types indexed by
*i* - types are defined by the number of connections is has to each other type.
- infinite population of each type, so I’m not worrying about susceptibility and the like
- parameters n_ij tell us the number of outdegrees from a type i node to a type j node
- Suppose that each of these groups is very large and that the connections are chosen at random, save for the fact about which group they connect.

- Split into types indexed by
- Obvious consequences
- let \Phi_{i} be the portion of infectious people who are of type i. So \Phi_{i}\equiv\frac{I_{i}}{\sum_{k}I_{k}}.
- expected number of new transmission events is \sum_{k}\left[I_{k}\sum_{j}rn_{kj}\right]
- refer to latex notes for info about number of newly infected of each type

- population structure
- Proposition 0: what happens with identity mapping among distribution
- Any point is a fixed point
- will technically be minor variations
- but will stick around the initial outbreak point
- epidemic will depend purely on whether n_ii > 1/r?

- Proposition 1: fixed point and convergence
- Note that, holding the parameters \eta_{..} constant, the system of equations describing each \Phi_{i}^{\prime} is a mapping from a probability distribution to itself, which doesn’t depend on the transmission rate r.
- In other words, I’m splitting up two questions about how the contagion spreads: how does the distribution of types amongst the infected evolve?, and does the incidence of the infection grow or shrink?
- Because it’s a continous mapping from a simplex to the same simplex, there must be at least one fixed point.
- This fixed point is not necessarily unique.
- If the connecton parameters \left{ \eta\right} are chosen such that \eta_{ij}\neq0 for any i\neq j , then the fixed point must be in the interior of the probability. simplex.
- In the latter case, subsequent generations of infectious will evolve to have a distribution of types which converges to the fixed distribution.
- Then the question of whether the contagion can spread to a large portion of the network depends on the average number of new tranmissions per person among this distribution of infectious people.
- Then after the distribution evolves in a way determined by the structure of connections between types, the number of new transmissions per infectious person will be a weighted sum of the average number of transmissions per type of person \sum_{k}\left[\Phi_{k}\sum_{j}rn_{kj}\right]

-Continuous example with two types is in pdf

-exmaple with only two people

-note about endogenizing connections )pdf

-point about eigenvectors

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