Background

Antimicrobial Resistance (AMR)

Antimicrobial resistance (AMR) refers to the ability of microorganisms, such as bacteria, fungi, or viruses, to withstand (or partially withstand) the effects of an antimicrobial to which they were formerly susceptible. While AMR may occur naturally as a result of the evolutionary process, the development and spread of AMR in microbial populations has been accelerated by the sustained anthropogenic use of antimicrobials. At present, an estimated 700,000 persons die each year as a result of antimicrobial resistant infections [1] – a number which is expected to grow as the prevalence of resistance continues to increase at a rate that far outpaces our ability to develop new antimicrobial therapies.

[1]	O’Neill, J. Tackling drug-resistant infections globally: final report and recommendations. Rev. Antimicrob. Resist. 84 (2016). doi:10.1016/j.jpha.2015.11.005

AMR and the Agri-food Production System

In addition to human and veterinary medicine, antimicrobials are used in livestock and agricultural production (together, agri-food production) to reduce the occurrence of disease and increase yield. While antimicrobial use (AMU) in human medicine is recognized as the primary driver of anthropogenic resistance development, AMU and other resistance-promoting practices in the agri-food production system are of particular concern with respect to human health, given the ease with which resistant pathogens may transfer between animals, humans, and their environments along the farm-to-fork continuum. Despite the risk posed by these pathogens, there remain a number of significant knowledge gaps in our understanding of the processes governing the development and persistence of AMR in the agri-food production system.

Integrated Assessment Modelling

Integrated assessment modelling differs from traditional risk modelling approaches in that it (generally) does not seek to develop numerical answers to specific questions; while integrated assessment models (iAMs) are simplifications of reality, they are not designed to simplify systems to the point of solution. Rather, iAMs are designed to integrate vastly different forms and scales of information, from traditional and non-traditional stakeholders, into a single framework through which users can address broad and complex questions. The output of an iAM, while often unrealistic or nonsensical in terms of a specific numerical value, is designed to increase the users’ understanding of the direction and magnitude of changes resulting from perturbations to a large, complex system. In the context of integrated assessment modeling’s original application – climate change science – these perturbations are often characterized as inadvertent consequences of human actions. In the context of generalized risk assessment, these perturbations may also take the form of strategic interventions, designed to achieve risk reductions throughout (or more commonly, within a manageable subsection of) the system.