Modelling the effectiveness of surveillance based on metagenomics in detecting, monitoring, and forecasting antimicrobial resistance in livestock production under economic constraints

Current surveillance of AMR is mostly based on testing indicator bacteria using MIC panels. Metagenomics has the potential to identify all known ARGs in complex samples and thereby detect changes in the occurrence earlier. Here, we simulate the results of an AMR surveillance program based on metagenomics in the Danish pig population. We modelled both an increase in the occurrence of ARGs and an introduction of a new ARG in a few farms and subsequent spread to the entire population. To make the simulation realistic, total cost of the surveillance was constrained, and the sampling schedule set at one pool per month with 5, 20, 50, 100, or 200 samples. Our simulations demonstrate that a pool of 50–100 samples and a sequencing depth of 250 million fragments resulted in the shortest time to detection in both scenarios, with a time-delay to detection of change of \(>\)15 months in all scenarios. Overall, our findings suggest that using metagenomics could improve the monitoring of AMR in the animal population.