Antimicrobial resistance as a super wicked problem: how do we engage the public to be part of the solution.

Antimicrobial resistance (AMR) is now regarded as one of the greatest global challenges of the 21st century. The complexity, urgent timeframe, and lack of clear solution to AMR have contributed to its classification as a 'super wicked problem'. Yet knowledge surveys of the general public have found that they still harbour numerous misconceptions linked to both the sources and impact of AMR. This confusion is compounded by AMR being a One Health issue, and therefore a factor in not just human health but in other industries, such as farming. This can further inhibit understanding and knowledge transfer around AMR for those without a prior knowledge base. In order to address the escalating risk that AMR presents, however, it is essential to address this knowledge gap and engage with the public to support wide scale changes in behaviour and consumer choice. The WHO now requires national action plans tackling AMR to include patient and public involvement/engagement (PPI/E) to support changing the trajectory of AMR. Despite this, little detail is available as part of strategic plans on how PPI/E should be undertaken in order to aid implementation. This paper discusses a number of approaches to support the design and delivery of PPI/E in relation to AMR, including the different social behaviour models underlying successful PPI/E strategies, and key considerations linked to specific activity types. The framework produced includes features for steps from initial planning and design through to evaluation. The aim is to help improve the ability of scientists and healthcare professionals to produce high quality AMR PPI/E.

Modeling and experimental study of dispersion and deposition of respiratory emissions with implications for disease transmission.

The ability to model the dispersion of pathogens in exhaled breath is important for characterizing transmission of the SARS-CoV-2 virus and other respiratory pathogens. A Computational Fluid Dynamics (CFD) model of droplet and aerosol emission during exhalations has been developed and for the first time compared directly with experimental data for the dispersion of respiratory and oral bacteria from ten subjects coughing, speaking, and singing in a small unventilated room. The modeled exhalations consist of a warm, humid, gaseous carrier flow and droplets represented by a discrete Lagrangian particle phase which incorporates saliva composition. The simulations and experiments both showed greater deposition of bacteria within 1 m of the subject, and the potential for a substantial number of bacteria to remain airborne, with no clear difference in airborne concentration of small bioaerosols (<10 µm diameter) between 1 and 2 m. The agreement between the model and the experimental data for bacterial deposition directly in front of the subjects was encouraging given the uncertainties in model input parameters and the inherent variability within and between subjects. The ability to predict airborne microbial dispersion and deposition gives confidence in the ability to model the consequences of an exhalation and hence the airborne transmission of respiratory pathogens such as SARS-CoV-2.