Risk assessment of future antibiotic resistance - eliciting and modelling probabilistic dependencies between multivariate uncertainties of bug-drug combinations

Werner, Christoph and Colson, Abigail and Morton, Alec and Bedford, Tim (2021) Risk assessment of future antibiotic resistance - eliciting and modelling probabilistic dependencies between multivariate uncertainties of bug-drug combinations. Frontiers in Applied Mathematics and Statistics: Mathematics of Computation and Data Science, 7. 669391.

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    Abstract

    The increasing impact of antibacterial resistance concerns various stakeholders, including clinicians, researchers and decision-makers in the pharmaceutical industry, and healthcare policy-makers. In particular, possible multidrug resistance of bacteria poses complex challenges for healthcare risk assessments and for pharmaceutical companies' willingness to invest in research and development (R&D). Neglecting dependencies between uncertainties of future resistance rates can severely underestimate the systemic risk for certain bug-drug combinations. In this paper, we model the dependencies between several important bug-drug combinations' resistance rates that are of interest for the UK probabilistically through copulas. As a commonly encountered challenge in probabilistic dependence modelling is the lack of relevant historical data to quantify a model, we present a method for eliciting dependence information from experts in a formal and structured manner. It aims at providing transparency and robustness of the elicitation results while also mitigating common cognitive fallacies of dependence assessments. Methodological robustness is of particular importance whenever elicitation results are used in complex decisions such as prioritising investments of antibiotics R&D.

    ORCID iDs

    Werner, Christoph ORCID logoORCID: https://orcid.org/0000-0002-5307-9686, Colson, Abigail ORCID logoORCID: https://orcid.org/0000-0002-3241-5855, Morton, Alec ORCID logoORCID: https://orcid.org/0000-0003-3803-8517 and Bedford, Tim ORCID logoORCID: https://orcid.org/0000-0002-3545-2088;