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Automatic Learning for Dynamic Markov Fields with Application to Epidemiology

Author

Listed:
  • S. Yakowitz

    (University of Arizona, Tucson, Arizona)

  • R. Hayes

    (Sun Microsystems, Mountain View, California)

  • J. Gani

    (University of California, Santa Barbara, California)

Abstract

Following an outline of dynamic Markov fields, we briefly describe some spatial models for contagious diseases and pose a prototype epidemic control problem. The notion of automatic learning is then introduced, and its relevance to epidemic control is described. In essence, once a contagion model is adopted and a domain of controls has been selected, learning can be used to obtain asymptotically optimal performance. (The learning algorithm is a synthesis of simulation and optimization, and is a suitable alternative to response surface methodology, in many applications.) The end product is the same optimal control as would be obtained by a conventional analysis. The point is that our current understanding of dynamic Markov fields does not permit conventional analysis; automatic learning has no computationally competitive alternative. The theory is illustrated by application to a spatial epidemic control problem.

Suggested Citation

  • S. Yakowitz & R. Hayes & J. Gani, 1992. "Automatic Learning for Dynamic Markov Fields with Application to Epidemiology," Operations Research, INFORMS, vol. 40(5), pages 867-876, October.
    • Handle: RePEc:inm:oropre:v:40:y:1992:i:5:p:867-876
    DOI: 10.1287/opre.40.5.867
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