Progressing Antimicrobial Resistance Sensing Technologies across Human, Animal, and Environmental Health Domains.

The spread of antimicrobial resistance (AMR) is a rapidly growing threat to humankind on both regional and global scales. As countries worldwide prepare to embrace a One Health approach to AMR management, which is one that recognizes the interconnectivity between human, animal, and environmental health, increasing attention is being paid to identifying and monitoring key contributing factors and critical control points. Presently, AMR sensing technologies have significantly progressed phenotypic antimicrobial susceptibility testing (AST) and genotypic antimicrobial resistance gene (ARG) detection in human healthcare. For effective AMR management, an evolution of innovative sensing technologies is needed for tackling the unique challenges of interconnected AMR across various and different health domains. This review comprehensively discusses the modern state-of-play for innovative commercial and emerging AMR sensing technologies, including sequencing, microfluidic, and miniaturized point-of-need platforms. With a unique view toward the future of One Health, we also provide our perspectives and outlook on the constantly changing landscape of AMR sensing technologies beyond the human health domain.

Using GIS to create synthetic disease outbreaks.

BACKGROUND: The ability to detect disease outbreaks in their early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatio-temporal analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. However, to ensure that the algorithms are effective they need to be evaluated. The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation. A state-transition model which simulates disease outbreaks in daily time steps using specified disease-specific parameters was developed to model the spread of infectious diseases transmitted by person-to-person contact. The software was developed using the MapBasic programming language for the MapInfo Professional geographic information system environment. RESULTS: The simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations. This model provides a means to explore the ability of outbreak detection algorithms to detect a variety of events across a large number of stochastic replications where the influence of uncertainty can be controlled. The software also allows historical data which is free from known outbreaks to be combined with simulated outbreak data to produce files for algorithm performance assessment. CONCLUSION: This simulation model provides a flexible method to generate data which may be useful for the evaluation and comparison of outbreak detection algorithm performance.