Assessing antibiotic usage data capture accuracy on dairy farms in England and Wales.

BACKGROUND: Accurate farm-level data on antibiotic usage (ABU) are needed for the surveillance of antibiotic resistance. Therefore, this study aimed to determine the accuracy of ABU data capture by dairy farmers in South West England and Wales. METHODS: Through a cross-sectional survey of 48 dairy farmers, the accuracy of ABU recording was measured by farmers' assessment of the completeness and timeliness of ABU recording ('perceived accuracy') and the completeness and correctness of on-farm ABU records ('actual accuracy'). Completeness and correctness were compared for paper and software recording methods. RESULTS: Perceived accuracy was higher than actual accuracy. Antibiotic names, withdrawal periods and dates that products were fit for human consumption were often incomplete or incorrect. More inaccuracies were seen with paper than software. In some software platforms, the date that milk would be fit for human consumption was frequently rounded down by half a day, increasing the risk of residue failures. LIMITATION: The small number of on-farm records assessed limits the generalisability of the results. CONCLUSIONS: Electronic recording of ABU should be encouraged. However, functionality needs improvement, alongside consultation with dairy farmers to increase awareness of inaccuracies.

When are pathogen genome sequences informative of transmission events?

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of 'transmission divergence', defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.

Equine herpesvirus - a constant threat.

Camilla Strang of the Animal Health Trust provides an overview of the diagnosis and control of conditions associated with infection by equine herpesvirus.