1. The role of Networks within Public Health Helen McAneney School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast

2. Outline Background – Historical setting and recent research Some theory – Centrality, centralisation, block-modelling, A few simple examples – Star, circle and line networks Networks within Public Health – Results and discussion Questions for the future

3. Early beginnings for Social Network Analysis Stanley Milgram and six degrees of separation – the Erdös number and the Kevin Bacon game Granovetter (1973): –“The strength of weak ties” Watts and Strogatz (1998): –“Collective dynamics of small-world networks” Euler’s Konigsberg's Bridges Problem (1736)

4. Source: art-sciencefactory.com

5. The shape of the US purely from the flight paths. Collectively a pattern emerges.

6. It’s a small world: New Scientist 20 April 2009 Time to travel to nearest city of 50K+ by land or water Less than 10% of the world's land is more than 48 hours of ground-based travel from the nearest city.

7. It’s a small world: New Scientist 20 April 2009 The planet's navigable rivers The network of rivers produced by nature.

8. It’s a small world: New Scientist 20 April 2009 Keeping track of trains Railway lines are mainly confined to the richer nations. The railway networks in India, Argentina and parts of Africa give clues to their colonial heritage.

9. Applications Knowledge transfer Disease transfer –STDs –Avian flu (hub airports) Drugs/smoking/obesity Web, Google Citations of articles Neighbourhood effects Friendship sites

10. Friendship as a Health Factor Science 23 January 2009:Vol. 323. no. 5913, pp. 454 - 457

11. How your friends' friends can affect your mood New Scientist, 30 December 2008 by Michael Bond

12. The Spread of Obesity in a Large Social Network Over 32 Years N. Christakis, J. Fowler N Engl J Med (2007): 357: 370-9 The Collective Dynamics of Smoking in a Large Social Network N. Christakis, J. Fowler N Engl J Med (2008): 358: 2249-58 Dynamic Spread of Happiness in a Large Social Network: Longitudinal Analysis Over 20 Years in the Framingham Heart Study J. Fowler, N. Christakis BMJ (2008) 337: a2338 Model of Genetic Variation in Human Social Networks J. Fowler, C. Dawes, N. Christakis PNAS (2009) 106: 1720-1724

13. Networks Nodes (actors) and edges (ties) Mark Newman, The physics of networks. Physics Today, November 2008, 33-38. –“In its simplest form, a network is a collection of points, or nodes, joined by lines, or edges.” –“Statistical analysis of interconnected groups—of computers, animals, or people—yields important clues about how they function and even offers predictions of their future behaviour.”

14. SNA Theory Adjacency matrix A, ( nxn) SNA measures –Centrality, centralisation, block-modelling Freeman Degree Centrality –No. of edges attached to it –Normalised Degree –Popularity, advice

15. SNA Theory The degree distribution is the probability distribution of these degrees over the whole network The distribution of the degrees of nodes on the internet. As indicated, the distribution roughly follows a straight line on a logarithmic plot; that is, it obeys a power law. MEJ Newman. The physics of networks. Physics Today, November 2008, 33-38

16. SNA Theory Bonacich Eigenvector Centrality –Edges weighted by influence of node connected to – is largest e-value, x is e-vector of A –By Perron-Frobenius Theorem, e-vector of dominant e-value has non-negative entries. Betweenness Centrality –Fraction of geodesic paths that a given node lies on –Control a node has over flow of information

17. A few examples: Star network Star network Adjacency matrix of

18. A few examples: Star network Centrality measures –Freeman Degree –Bonacich Eigenvector –Betweenness Centralisation 100%, node1 dominates

19. A few examples: Circle network Circle network Adjacency matrix of

20. A few examples: Circle network Centrality measures –Freeman Degree –Bonacich Eigenvector –Betweenness Centralisation 0%, all nodes equal

21. A few examples: Line network Line network (‘broken circle’) Adjacency matrix of

22. A few examples: Line network Centrality measures Centralisation –6.67% (degree) –39% (e-vector) –31% (betweenness)

23. CoE Network in Public Health Launch of UKCRC CoE in Public Health (NI) June 2008 Questionnaire to provide baseline data Create a map of PH community in NI 98 participants from 44 organisations & research clusters 193 nodes (organisations) nominated

24. How personal goals related to those of CoE

25. CoE Network in Public Health 193 organisations and research clusters

26. Centrality measures Centralisation –16% (out-degree) & 5% (in-degree) –51% (eigenvector) –4% (betweenness)

27. Block-model of Network

28. Root mean sum of square of impact (x) and strength (y), Scale of 1 (high) – 3 (low) Strongest if  2 (1 2 +1 2 ), weakest if  18 (3 2 +3 2 ) Entry (i; j) from row i and column j, gives the RMSS from block i to block j.

29. Questions for the future Identified difference in attitudes/goals of academics & non- academics. Sectors with little or no interaction Influential organisation –good or bad? ‘Value’ of trans-disciplinary interaction CoE’s translational message, –improving cross collaboration –improving effectiveness for clinical or PH outcomes Health reforms in NI - new PH Agency, HSCB

30. Acknowledgement Dr Jim McCann –School of Mathematics and Physics Prof. Lindsay Prior –School of Sociology, Social Policy and Social Work, Jane Wilde CBE –The Institute of Public Health in Ireland Prof. Frank Kee –Director UKCRC Centre of Excellence for Public Health –www.qub.ac.uk/coe