# Time Discounted Infection and Susceptibility

#### November 11, 2015

In Myers (2000), susceptibility and infection is defined for a given time period and as a constant throughout the network–so only varies on $$t$$. In order to include effects from previous/coming time periods, it adds up through the of the rioting, which in our case would be strength of tie, hence a dichotomous variable, whenever the event occurred a week within $$t$$, furthermore, he then introduces a discount factor in order to account for decay of the influence of the event. Finally, he obtains

$V_{(t)} = \sum_{a\in \mathbf{A}(t)} \frac{S_{(a)}m_{T(a), T\leq t-T(a)}}{t- T(a)}$

where $$\mathbf{A}(t)$$ is the set of all riots that occurred by time $$t$$, $$S_{(a)}$$ is the severity of the riot $$a$$, $$T(a)$$ is the time period by when the riot $$a$$ accurred and $$m$$ is an indicator function.

In order to include this notion in our equations, I modify these by also adding whether a link existed between $$i$$ and $$j$$ at the corresponding time period. Furthermore, in a more general way, the time windown is now a function of the number of time periods to include, $$K$$, this way, instead of looking at time periods $$t$$ and $$t+1$$ for infection, we look at the time range between $$t$$ and $$t + K$$.

# Infectiousness

Following the paper’s notation, a more generalized formula for infectiousness is

$\label{eq:infect-dec} \left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ji(t+k-1)}z_{j(t+k)}}{k} \right)\left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ji(t+k-1)}z_{j([t+k;T])}}{k} \right)^{-1}$

Where $$\frac{1}{k}$$ would be the equivalent of $$\frac{1}{t - T(a)}$$ in mayers. Alternatively, we can include a discount factor as follows

$\label{eq:infect-exp} \left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ji(t+k-1)}z_{j(t+k)}}{(1+r)^{k-1}} \right)\left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ji(t+k-1)}z_{j([t+k;T])}}{(1+r)^{k-1}} \right)^{-1}$

Observe that when $$K=1$$, this formula turns out to be the same as the paper.

# Susceptibility

Likewise, a more generalized formula of susceptibility is

$\label{eq:suscept-dec} \left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ij(t-k+1)}z_{j(t-k)}}{k} \right)\left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ij(t-k+1)}z_{j([1;t-k])}}{k} \right)^{-1}$

Which can also may include an alternative discount factor

$\label{eq:suscept-exp} \left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ij(t-k+1)}z_{j(t-k)}}{(1+r)^{k-1}} \right)\left( \sum_{k=1}^K\sum_{j\neq i} \frac{x_{ij(t-k+1)}z_{j([1;t-k])}}{(1+r)^{k-1}} \right)^{-1}$

Also equal to the original equation when $$K=1$$. Furthermore, the resulting statistic will lie between 0 and 1, been the later whenever $$i$$ acquired the innovation lastly and right after $$j$$ acquired it, been $$j$$ its only alter.

(PENDING: Normalization of the stats)