Social Science

Social Interaction
James Holland Jones, Stanford University

Directly transmitted infectious diseases -- e.g., sexually transmitted infections like HIV, Gonorrhea, or Genital Herpes -- require social interaction to be transmitted. In this talk, I will introduce models of infectious disease as a way to build up to increasingly complex formal representations of social interaction. The most basic model of social interaction is that of "mass action" employed by the classic
Kermack-McKendrick general epidemic model. This simple model of social interaction -- that the amount of interaction is proportional to the product of abundances of infected and susceptible people -- can be expanded to account for increasingly complex modes of social structure.

This structure reaches its most detailed expression in explicit social network models for social contact. In order to facilitate a general understanding of how social interactions -- and the structure these interactions induce -- affects epidemics, I will focus on two key metrics relating to epidemic models: (1) the basic reproduction number and (2) the final size of the epidemic. How one calculates the basic reproduction number of the epidemic has great bearing on social interactions and how the epidemic mixes.

Using highly detailed sexual history survey data collected as part of the The Malawi Diffusion and Ideational Change Project, I will estimate models for sexual network structure for a relatively large network (ca. 1500 individuals). Using epidemic simulations on both the observed network structure and simulations based on the estimated model, I will show how social structures that arise from particular forms of social interaction either inhibit or enhance transmission of sexually transmitted infections. This work provides key insights into understanding the the infection risk of populations characterized by different types of social interaction. It also suggests interventions that may help to check the spread of diseases like AIDS that have already devastated the fabric of many African societies and threaten to reap a similar toll in other parts of the world this century.

Background Reading
Probably the best intro to the talk I will give can be found in my recent book chapter, the full text of which, remarkably, appears to be available on Google Books:

books.google.com/books/p/uwp?id=zHgGwdWfe0IC&printsec=frontcover&source=gbs_Vie
wAPI&hl=en#PPA117,M1

This is not very technical and deals with the basic concepts I will cover in my talk.

Some references:

Handcock, M. S., and J.H. Jones. 2004. Likelihood-based inference for stochastic models of sexual network evolution. Theoretical Population Biology 65:413-422.

Handcock, M. S., and J.H. Jones. 2006. Interval estimates for epidemic thresholds in two-sex network models. Theoretical Population Biology 70:125-134.

Hethcote, H. W. 2000. The mathematics of infectious diseases. Siam Review 42 (4):599-653. (a good overview of mathematical models of epidemics)

Jones, J.H. 2008. Culture for epidemic models and epidemic models for culture. In Brown, M., ed., Explaining Culture Scientifically, pp.
117-136. Seattle: University of Washington Press.

# vimeo.com/33252459 Uploaded 84 Plays / / 0 Comments Watch in Couch Mode

Social Science

Kavli Frontiers of Science Plus

This channel contains session presentations that cover social science topics from the Kavli Frontiers of Science symposium series of the National Academy of Sciences.

For additional symposium information, please visit our web site (nasonline.org/kfos).

Browse This Channel

Shout Box

Channels are a simple, beautiful way to showcase and watch videos. Browse more Channels. Channels