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Hospital effluents contain a hazardous amalgam of drug residues and infectious agents. Qualitative and quantitative evidence shows that hospital effluents are enriched in antibiotics, multidrug-resistant bacteria, antibiotic resistance genes and genetic vectors which could facilitate the horizontal transfer of these genes. This chapter provides an overview of the current status of antimicrobial resistance (AMR) surveillance in hospital effluents and draws comparisons to other AMR monitoring studies in domestic wastewaters and natural aquatic environments. We discuss approaches and standard tools that have been used to measure levels of AMR contamination and provide insights to the latest developments in the detection and profiling of AMR which have yet to gain traction in present surveillance programs.

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to “fix” a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.