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  Antibiotic treatment protocols revisited: The challenges of a conclusive assessment by mathematical modeling

Uecker, H., & Bonhoeffer, S. (2018). Antibiotic treatment protocols revisited: The challenges of a conclusive assessment by mathematical modeling. bioRxiv. doi:10.1101/372938.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-7040-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7041-5
Genre: Paper

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372938.full.pdf (Preprint), 374KB
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Uecker, Hildegard1, Author              
Bonhoeffer, Sebastian, Author
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1Research Group Stochastic Evolutionary Dynamics, Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2640692              

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 Abstract: Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic resistant strains that delay or impede effective treatment. To ensure a successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (“combination therapy”) but also the random assignment of drugs to different patients (“mixing”) and a regular switch in the default drug used in the hospital from drug A to drug B and back (“cycling”). For the past 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing, and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modeling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation, and its analysis. We thereafter discuss how progress can be made and suggest future modeling directions.

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Language(s): eng - English
 Dates: 2018-07-20
 Publication Status: Published online
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 Rev. Method: No review
 Identifiers: DOI: 10.1101/372938
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Title: bioRxiv
Source Genre: Journal
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