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

Nicolas Houy () and Julien Flaig
Additional contact information
Nicolas Houy: GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

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

Keywords: health care associated infection; antimicrobial resistance; antibiotic cycling; optimization; Monte-Carlo methods; stochastic model. (search for similar items in EconPapers)
Date: 2021
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03506952v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Computer Methods and Programs in Biomedicine, 2021, 204, pp.106050. ⟨10.1016/j.cmpb.2021.106050⟩

Downloads: (external link)
https://shs.hal.science/halshs-03506952v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-03506952

DOI: 10.1016/j.cmpb.2021.106050

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().