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Evolving Novel and Effective Treatment Plans in the Context of Infection Dynamics Models: Illustrated with HIV and HAART Therapy

  • Rebecca Haines
  • David Corne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

Several diseases involve complex interplay between an infection and the body’s defences. Concerning AIDS, for example, this corresponds to developments in the immune system’s responses and the HIV virus’ counter-responses. Treatment for such diseases involves, at specific times, delivery of an agent that inhibits the infection. We hypothesise that: given a credible model of the combined dynamics of infection and response, the timing and quantities involved in treatment can be valuably investigated using that model. In particular, we investigate searching for optimised treatment plans with an evolutionary algorithm (EA). Our test case is a cellular automaton (CA) model of HIV dynamics, extended to incorporate HAART therapy (a favoured HIV treatment).An EA is wrapped around this model, and searches for treatments that maximally delay onset of AIDS, given certain constraints. We find that significant improvements over default HAART strategy are readily discovered in this way.

Keywords

Human Immunodeficiency Virus Human Immunodeficiency Virus Infection Cellular Automaton Human Immunodeficiency Virus Treatment Human Immunodeficiency Virus Virus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rebecca Haines
    • 1
  • David Corne
    • 2
  1. 1.SECaMUniversity of ExeterExeterUK
  2. 2.MACSHeriot-Watt UniversityEdinburghUK

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