1. Networks in Health
Mark Temple

2. What am I hoping to show?
If health is the product of many interactions.
and these interactions are multi-faceted
and they may be very powerful
Hadn’t we better start recording them
And start discussing them
Then empirical ideas may form

3. Why Networks They are elegant, mysterious and beautiful.
They are very resilient
They can be very strong and light
but they are difficult to describe.
We tend to describe simple ones
and ignore real ones.

4. A simple network

5. Chart of log degree frequency

6. A more complicated one

7. And its log degree frequency

8. A small world network

9. And its distribution

10. A scale free network

11. You guessed it

12. So what do we see from this
The characteristics of the networks are different.
The transmission features are different
We have some difficulty /unfamiliarity in describing networks
None of these are what is assumed in epidemiology

13. The Epidemiological view
All individuals act like a Brownian particle
Movement and contact random
Equal chance of linking to any other
Poisson distribution of connections
Lambda about 100 or more

14. And this is its contact shape

15. What is the real world experience
Drawn from CD control contact networks
Not all the same basis
Needs care with generalising

16. A Network diagram

17. The degree plot

18. Looking at all my network data

19. Does it matter In Imm & Vac – certainly
no herd immunity in scale free networks
no target level to achieve disease free state
It prevents epidemiologists seeing the wood for the trees

20. How networks affect rate of spread
Lattice
Random
Scale Free
Small World
Pictures provided by Ken Eames

21. Contagion of obesity The critique of Christakis and Fowler’s paper
claims network effects are contextual ones
common social groupings
homophily
Misses point
Personal action is secondary to social effects,
Network position is marker.

22. Where does that leave us?
We need to consider
The network in which we all work & think
How to
describe networks simply
collect good data
develop analytical skills
develop models to gain understanding

23. Future opportunities Lay foundations Let the data talk Get good data
Develop descriptive language
Stop using linear models
Let the data talk
Stop forcing it to behave to our thinking