P10.2 Beyond mapping hotspots: using geospatial models to guide surveillance against antimicrobial resistance in animals.

Abstract

The rise of antimicrobial resistance (AMR) in food animals is a growing threat for animal health, and potentially for human health. China, as the world’s largest user of veterinary antimicrobials could play a pivotal role in leading the international response to AMR, and maps of AMR could help coordinate this response (Zhao et al., 2021). Here, we used geospatial models to identify regions where future surveillance effort could be targeted in priority.

We extracted resistance rates from 446 point-prevalence surveys on Escherichia coli, Salmonella, Campylobacter, and Staphylococcus aureus. We mapped the proportion of drugs tested with resistance rates higher than 50%. The uncertainty map associated with these predictions was used to identify locations where surveillance efforts could be intensified in the future. We calculated an index of ‘necessity for additional surveillance’ - the product between uncertainty in AMR level and animal densities- to identify locations where 50 surveys could be conducted such as to minimize uncertainty on future AMR levels in China. We computed both exact and approximate solution for the spatially optimal allocation of surveys to improve AMR surveillance in China.

In China, AMR levels were the highest in the east and lowest in the southwest. Regions that would benefit the most from increased surveillance were the southwest (21/50 surveys) and northeast (11/50 surveys).  Using geographically targeted surveillance could reduce uncertainty of AMR level by 104% compared with an equal surveillance effort across administrative divisions. Our findings help outline priorities for AMR surveillance in China, and identify where future surveys could best improve the accuracy of AMR maps.