The prevalence of antimicrobial resistance (AMR) in Escherichia coli found on retail beef and pork meat samples in the UK is relatively low, according to surveillance conducted by the UK Animal and Plant Health Agency (APHA).

The U.S. Food and Drug Administration (FDA) recently released the findings of a sampling assignment that collected and tested ready-to-eat (RTE) dips and spreads with the aim of determining the presence of Listeria monocytogenes and Salmonella.

The UK Food Standards Agency recently highlighted the Pathogen Surveillance in Agriculture, Food and Environment (PATH-SAFE) program, which aims to develop a national surveillance network that uses whole genome sequencing (WGS) to improve the monitoring of foodborne pathogens and antimicrobial resistance (AMR).

In this episode of Food Safety Matters, we talk with Martin Wiedmann, Ph.D., D.V.M., the Gellert Family Professor in Food Safety and Food Science at Cornell University, about his research on Listeria and Salmonella, his work to strengthen foodborne illness surveillance and response, his use of whole genome sequencing (WGS), and other topics.

The Food and Agriculture Organization of the United Nations (FAO) and Wageningen University recently held a workshop about early warning tools and systems that can be used to manage imminent and emerging food safety issues. 

Data from OzFoodNet has shown campylobacterosis and salmonellosis to be the most common foodborne illnesses reported in Australia in 2017.

A recent case study from the Sapienza University of Rome examined the surveillance of foodborne pathogens through a One Health lens in Europe, with a specific focus on the One Health European Joint Program (OHEJP) MATRIX Project framework.

The European Food Safety Commission (EFSA) has published guidelines for reporting whole genome sequencing (WGS) data to its One Health WGS System, which will support outbreak investigations and other EFSA activities.

The U.S. Food and Drug Administration, the U.S. Centers for Disease Control and Prevention, and the U.S. Department of Agriculture have developed a collaborative approach to improve the coordination of multistate outbreak investigations.

Opportunities exist for the use of data science in preventing and mitigating foodborne disease outbreaks, often using publicly accessible data. This article examines machine learning/data science approaches, including whole-genome sequencing, to enhance food safety.