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There's a large literature on networks and disease transmission.  No
single network summary paramter captures everything, in part b/c
the properties of the pathogen matter too.

Martina

On Wed, 4 Jun 2003, Guy Hagen wrote:

> *****  To join INSNA, visit http://www.sfu.ca/~insna/  *****
>
> A few people have emailed me to learn what responses were sent regarding my
> query on transmission / attenuation (e.g., for disease, information flow,
> etc.)
>
> It appears some early work (primarily by Linton Freeman et al) used
> shortest-paths (geodesics) to estimate transmission, but this suffered from
> some of the shortcomings of centrality measures (e.g., the shortest path is
> not necessarily the most relevant one).
>
> However, the development of Information Centrality appears to address that
> by incorporating all paths, according to two responses.  See
> Stephenson-Zelen (1989) paper on information centrality and Noha Friedkin's
> measures of centrality.  For that matter, I think there's a presentation of
> the algorithm for info centrality in Wasserman and Faust.
>
> Of course, the algorithm will require more complex matrix algebra (e.g.,
> matrix inversion) than shortest path measures, I believe.
>
> I also found these articles on info centrality:
>
> Social networks in Disease Transmission: the colorado springs study -
> Rothenberg, Woodhouse, Potterat, Muth, Darrow and Klovdahl - NIDA research
> Monograph #151 on social networks, drug abuse, and HIV transmission
>
> And  the following, which has a nice intro on various centrality algorithms:
> Centrality measures and Information Flows in venture Capital Syndication
> networks, by Mikko Jaaskelainen (2001).
>
> I found both of those articles on a google search.
>
> G
> --
> Guy Hagen
> President, Innovation Insight
> 27810 Sky Lake Circle - Wesley Chapel - FL 33543
> 813.997.2111 - innovationinsight.com
>
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