Sexually Transmitted Infections in a Brave New World

Organised by - Trystan Leng

Abstract - 

In his 1931 novel Brave New World, Aldous Huxley imagines a dystopian society where social norms are radically different from our own. The spread of infections, and in particular the spread of sexually transmitted infections (STIs), can be thought of as processes on networks - hence a change in social norms, by changing the structure of these networks, has the potential to alter both the spread and control of STIs drastically. This project will create appropriate models to explore the effect the differing social norms between our world and Huxley's Brave New World have on the spread and control of STIs. Doing so provides an insight into which aspects of sexual networks must be explicitly modelled, and more importantly, which can we neglect without affecting the results of our models.

The project may use pair-formation models or stochastic simulations of networks, depending upon preliminary discussions within the group. Pair-formation models have the advantage of being in general deterministic, and hence more tractable, but to assess the impact of some control measures (such as contact tracing) a simulation of the full model might be necessary. The project may be partitioned along lines of different modeling methods, different disease dynamics, or different control interventions - to be decided within the group at the retreat.

Aims and Objectives - 

Aim-to explore the impact differing social norms has on the spread and control of STIs, to better understand which features of networks should be explicitly modelled for the spread of infectious diseases.

Objectives -

  • Determine the ways in which social and sexual norms differ between our world and in a Brave New World, and decide which of these to focus on.
  • Construct appropriate models of STI spread under differing social norms.
  • Examine how the spread and control of STIs differ between the constructed models, in particular when parameters are adjusted to achieve the same population level quantities across models (such as prevalence).

Of Interest to - Those interested in epidemiology and processes on networks.

Resources Necessary - 

References -