Automatic Learning for Dynamic Markov Fields with Application to Epidemiology

S. Yakowitz, R. Hayes and J. Gani
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S. Yakowitz: University of Arizona, Tucson, Arizona
R. Hayes: Sun Microsystems, Mountain View, California
J. Gani: University of California, Santa Barbara, California

Operations Research, 1992, vol. 40, issue 5, 867-876

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.

Keywords: computers/computer science: artificial intelligence; health care: epidemiology; probability: stochastic model applications (search for similar items in EconPapers)
Date: 1992
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