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Inferring Infection Transmission Parameters That Influence Water Treatment Decisions

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  • Stephen E. Chick

    ()
    (Technology Management Area, INSEAD, Boulevard de Constance, 77305 Fontainebleau CEDEX, France)

  • Sada Soorapanth

    ()
    (Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, Michigan 48109)

  • James S. Koopman

    ()
    (Department of Epidemiology, School of Public Health-I, and Center for the Study of Complex Systems, University of Michigan, 109 Observatory Street, Ann Arbor, Michigan 48109)

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    Abstract

    One charge of the United States Environmental Protection Agency is to study the risk of infection for microbial agents that can be disseminated through drinking water systems, and to recommend water treatment policy to counter that risk. Recently proposed dynamical system models quantify indirect risks due to secondary transmission, in addition to primary infection risk from the water supply considered by standard assessments. Unfortunately, key parameters that influence water treatment policy are unknown, in part because of lack of data and effective inference methods. This paper develops inference methods for those parameters by using stochastic process models to better incorporate infection dynamics into the inference process. Our use of endemic data provides an alternative to waiting for, identifying, and measuring an outbreak. Data both from simulations and from New York City illustrate the approach.

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    File URL: http://dx.doi.org/10.1287/mnsc.49.7.920.16386
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 49 (2003)
    Issue (Month): 7 (July)
    Pages: 920-935

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    Handle: RePEc:inm:ormnsc:v:49:y:2003:i:7:p:920-935

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    Related research

    Keywords: Stochastic Infection Modeling; Water Treatment Policy; Public Health Policy; Bayesian Inference; Risk Dynamics;

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