Antimicrobial Resistance case study

The early detection, assessment and monitoring of a hidden pandemic of antimicrobial-resistant infections

Disease description

The emergence and spread of drug-resistant pathogens have led to antimicrobial resistance (AMR) now being considered a major public health concern. AMR occurs when bacteria, viruses, fungi and parasites adapt, resulting in these pathogens no longer responding to commonly used drugs. This makes infections caused by resistant pathogens harder to treat, increasing the risk of severe illness and death. Drivers of the emergence and spread of AMR are multi-sectoral, and relate to, among other factors, antibiotic exposure in humans, animals and the environment, standards of infection control, and international travel and trade. To date, AMR surveillance in Europe and elsewhere is mainly relying on indicator-based surveillance. ECDC and national governments launched One Health programs for AMR, involving in addition to monitoring AMR in humans and animals (including food products), environmental surveillance, such as sewage monitoring. Nonetheless, the level of implementation of the One Health approach for AMR between European countries is variable, and the intersectoral evaluation of data originating from these multi-sectoral surveillance remains challenging.

Disease profile

To reach a comprehensive understanding of the factors affecting the ecology, distribution, and trend in Europe of the infectious diseases included in MOOD, information from the available literature and expert input was collected and summarized in “disease profiles”. These documents synthesize the available knowledge on specific diseases, such as AMR (In Press).

AMR Covariates Dashboard

Fig 1: A snippet of the dashboard for surveys and maps of AMR in animals

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Publications

Scientific Article
  • Predictive Mapping of Antimicrobial Resistance for Escherichia coli, Salmonella, and Campylobacter in Food-Producing Animals, Europe, 2000–2021, DOI: 10.3201/eid3001.221450

Risk maps

Fig 2: Data from study of predictive mapping for antimicrobial resistance of Escherichia coli, Salmonella, and Campylobacter in food-producing animals, Europe, 2000–2021. A) Geographic distribution of point prevalence surveys (PPSs). B) Number of PPSs published per year.

Fig 3: Mapping of predicted P50s and hotspot areas for antimicrobial resistance of Escherichia coli, Salmonella, and Campylobacter, Europe. A) Predicted proportions of antimicrobials with P50 at 10 × 10 km resolution per bacteria. B) Antimicrobial resistance hotspots (light blue) in eastern Europe, Italy, and Spain. Cutoffs: E. coli, 0.43; Salmonella, 0.23; Campylobacter, 0.6 (95% percentile). P50, >50% antimicrobial resistance.

Case study leaders

Department of Environmental Systems Science, ETH Zürich, Thomas Van Boeckel (thomas.vanboeckel@env.ethz.ch). Has now moved to the University of Zürich (thomas.vanboeckel@uzh.ch)

For more video content about Antimicrobial resistance

The video shows an operation example of the open-access platform resistancebank.org. Visualize the hotspots of antimicrobial resistance in animals in low- and middle-income countries, retrieve point prevalence surveys information and help to create a global community by uploading your study results online at https://resistancebank.org.
Alan Radford is a professor of veterinary health informatics at the University of Liverpool. He is the coordinator of the Small Animal Veterinary Surveillance Network (SAVSNET), which exploits electronic health records from veterinary practitioners across the United Kingdom, and identifies significant trends in diseases. Alan shared his experience in big data analytics across the network.
This talk first touched on why we use maps at all then look at the factors (“covariates”) that drive disease occurrence. The session led by William Wint (E.R.G.O., UK) & Cederic Marsboom (Avia-GIS, Belgium) examines what these covariates might be and identify the environmental, agricultural, socio-economic, ecological and climatic parameters that can best contribute to spatial modelling. It is also important to know where these data can be found, what are the pros and cons of different data sources for the common covariate variables, and what datasets can be used for different types of models. The available covariate data are not always in a form that is convenient for spatial modellers and the session will provide examples of the processing and selection needed to provide modellers with what they need. Finally, the use of selected covariates in spatial models is be discussed and illustrated with worked examples.