DiseaseSpreadPWR: A disease spread function
In kelseyandersen/ugsweets:

This function allows you to simulate disease spread through a network that combines two types of data- observed transaction data and modeled spread data. Spread modeled using the inverse power law model.

1	DiseaseSpreadPWR(transmat, d, startnode, p, Beta)

`transmat`	your transaction matrix which is a full network matrix of 1 and 0s
`d`	matrix of geographic distances between all nodes, scale does not matter
`startnode`	single node number that introduces the pathogen into the network
`p`	probability of infection on a link in each timestep
`Beta`	spread parameter beta that will be used in the inverse power law model to model link formation between locations- higher number means fewer links