2.1 What are the main uses of WGS data for surveillance?

The use of WGS for AMR surveillance has grown exponentially since it was introduced in the early 2000s. Public, veterinary and environmental health agencies around the world now use WGS as part of routine surveillance. In the US state of Washington, the Department of Health (DOH) integrated a genomics-first approach into its AMR surveillance system, with the result that it is now better able to identify additional outbreak cases, sensitively classify outbreak or non-outbreak cases, and confirm hypothesised linkages between cases (Torres, et al., 2025). In South Africa, the National Institute for Communicable Diseases conducts WGS-based AMR surveillance with support from the UK Aid Fleming Fund’s SeqAfrica project (DTU National Food Institute, n.d. 1).

Case Study 1 is an example of the use of WGS for AMR surveillance at the national level.

Case Study 1: DANMAP

The Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP) integrated WGS into its ongoing surveillance activities to monitor and control AMR starting in 2018. It now uses WGS to:

  • improve the detection of resistance genes and mutations in bacterial pathogens
  • monitor trends in AMR over time
  • investigate and manage outbreaks of resistant infections.

DANMAP is a One Health system that can integrate data from various health registers, thereby connecting genomic data to patient-level metadata, such as hospital records, prescription data and regional food laboratory records and epidemiological information. DANMAP makes use of EPILINX, a visualisation tool that maps patient networks and tracks transmission events during healthcare-setting outbreaks. This tool supports molecular epidemiologists in linking patient movements, risk factors and infection sources through visualisations such as an epidemic curve and colour-coded hospital ward mappings.

DANMAP’s website includes more information about the programme, including previous reports and recordings.

Figure 2 provides a demonstration of how this tool visualises AMR trends over time across different animal and food sources using DANMAP data (DANMAP, 2025).

Described image
Figure 2 Ampicillin resistance in E. coli from Danish livestock and meat, 2000–2023 – percentage of resistance from broilers, pigs, cattle and corresponding meat products (DANMAP, n.d.).
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This graph shows the percentage of E. coli isolates resistant to ampicillin from Danish livestock (broilers, pigs, and cattle) and corresponding meat products (broiler meat, pork, and beef) between 2000 and 2023. Data are based on phenotypic antimicrobial susceptibility testing as reported annually in DANMAP. Each coloured line represents one source, with live animal and meat product categories shown separately to highlight differences in resistance trends across the production chain. The general trend is that resistance in cattle remains low, between 0 and 10%; from the mid-2010s, pork is the highest, at around 30%; broilers begin and end the period at around 15%, with some variation; beef is a similar low level to cattle between 2008 and 2015; pork is similar to pigs over the same period.

Figure 2 Ampicillin resistance in E. coli from Danish livestock and meat, 2000–2023 – percentage of resistance from broilers, pigs, ...

Based on Figure 2, which source has the highest ampicillin-resistant E. coli levels from 2015 onward, and how does this compare to resistance in other livestock groups during that time? What might explain the difference?

Answer

From 2015 onwards, E. coli isolates from Danish pigs show the highest resistance to ampicillin, often exceeding 30%. In contrast, resistance in cattle remains consistently low, generally under 10%. This likely reflects differences in antimicrobial usage between pig and cattle production systems, as well as variation in infection pressure and treatment practices. For broilers, the level is intermediate between that observed in pigs and cattle.

In this section, you have seen examples that illustrate how WGS is transforming AMR surveillance by enabling more precise detection, tracking and response to resistance trends, while also allowing data to be integrated across sectors to follow outbreaks and monitor changes over time, ultimately supporting more effective, data-driven public health interventions.

2 How is WGS used in AMR-related surveillance?

2.2 How can WGS surveillance supplement phenotypic surveillance?