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Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method

Author

Listed:
  • Nicolas Houy

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Julien Flaig

Abstract

Background and objectives. We present a heuristic solution method to the problem of choosing hospital-wide antimicrobial treatments that minimize the cumulative infected patient-days in the long run in a health care facility. Methods. Our solution method is a rollout algorithm. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Treatments are chosen at the beginning of each period based on the count of patients with each health status, and on stochastic simulations of the future emergence and spread of antimicrobial resistance. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. Results. In our simulations, our algorithm allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy, and by 32.2% compared to a similar heuristic algorithm not using surveillance data (significantly at the 95% threshold). Conclusion. Our heuristic solution method is simple yet flexible. We explain how it can be used either to perform online optimization, or to produce data for quantitative analysis. Its performance is illustrated using a relatively simple infectious disease transmission model, but it is compatible with more advanced epidemiological models

Suggested Citation

  • Nicolas Houy & Julien Flaig, 2021. "Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method," Post-Print halshs-03506952, HAL.
  • Handle: RePEc:hal:journl:halshs-03506952
    DOI: 10.1016/j.cmpb.2021.106050
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03506952
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    References listed on IDEAS

    as
    1. Nicolas Houy & François Le Grand, 2019. "Optimizing immune cell therapies with artificial intelligence," Post-Print halshs-01949619, HAL.
    2. Pia Abel zur Wiesch & Roger Kouyos & Sören Abel & Wolfgang Viechtbauer & Sebastian Bonhoeffer, 2014. "Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models," PLOS Pathogens, Public Library of Science, vol. 10(6), pages 1-13, June.
    3. Nicolas Houy & François Le Grand, 2019. "Optimizing immune cell therapies with artificial intelligence," Post-Print hal-02312260, HAL.
    4. Nicolas Houy & François Le Grand, 2018. "Optimal dynamic regimens with artificial intelligence: The case of temozolomide," Post-Print halshs-01949651, HAL.
    5. Nicolas Houy & François Le Grand, 2018. "Optimal dynamic regimens with artificial intelligence : The case of temozolomide," Post-Print hal-02312154, HAL.
    6. Goodson, Justin C. & Thomas, Barrett W. & Ohlmann, Jeffrey W., 2017. "A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 216-229.
    7. Burcu Tepekule & Hildegard Uecker & Isabel Derungs & Antoine Frenoy & Sebastian Bonhoeffer, 2017. "Modeling antibiotic treatment in hospitals: A systematic approach shows benefits of combination therapy over cycling, mixing, and mono-drug therapies," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-22, September.
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