Overview of the Webinar

Abstract: Source tracking of antimicrobial resistance in emerging countries

When: 1pm 2 December

Please register for this free RSPH webinar. If you are not able to view the webinar at the scheduled delivery time, you can still register then view it during the following 24 hours.

The global increase of antimicrobial resistance (AMR) is among the greatest threats to human, animal and environmental health. AMR is the ability of a microorganism, such as bacteria or virus, to stop an antimicrobial (such as antibiotics and antivirals) from working against it. The risk of a post-antibiotic era has to be considered, were common infections and minor injuries can be lethal again.

The burden of AMR is greatest in emerging countries, where increasing economic wealth permits greater use of antibiotics, but poor waste management leads to wider spread. Rivers play an important role in receiving and spreading insufficiently treated sewage, containing residues of antibiotics, resistant genes and resistant bacteria.

Predicting AMR exposure is difficult as routine water quality monitoring programmes rarely include AMR indicators. As such, an urgent need exists to characterise AMR in emerging countries, both to define current conditions, but also to develop models for predicting conditions with limited data.

This project combines newly collected AMR field data for a Southeast Asian river catchment with a parametrised AMR model to help local stakeholders identify optimal interventions to reduce AMR spread. Working together with partners in Malaysia, Singapore, the UK and China, this research targets UN Sustainable Development Goals 3, 6 and 11- Good Health and Wellbeing for people; Clean Water and Sanitation; Sustainable Cities and Communities.

Questions that will be addressed during this webinar:

  • How can and should we monitor AMR spread in the environment?
  • Can we use easy-to-measure surrogates to predict AMR concentrations?
  • How can we better translate water quality measurements across catchments and countries?
  • Can we use existing river water quality models and data sets to predict AMR pollution?