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Complex Thinking for Antimicrobial Resistance

An innovative scientific approach towards achieving a more comprehensive understanding of antimicrobial resistance (AMR) is the application of complexity science to predict the emergence of AMR in response to existing and changing public policies and socio-economic conditions. This project aims to close current knowledge gaps on the prevalence of AMR, especially in countries with weaker healthcare systems. Such knowledge is essential to guide surveillance efforts to inform antimicrobial treatment decisions, such as empirical treatment guidelines, at national, regional or local level, and to predict the efficacy of interventions aimed at reducing AMR.

Photo by Joel Filipe at Unsplash

Antimicrobial resistance (AMR) is an increasingly serious threat to global public health. AMR develops when a microorganism (bacteria, fungus, virus or parasite) is no longer susceptible to a drug to which it was originally susceptible. This means that standard treatments become ineffective; infections are more difficult or impossible to treat; the risk of the spread of infection to others is increased; illness and hospital stays are prolonged, with added economic and social burden; and the risk of death is greater—in some cases, twice that of patients who have infections caused by susceptible bacteria.

The Amsterdam Institute for Global Health and Development (AIGHD) and the UvA Institute for Advanced Study (IAS) have joined forces to better understand AMR patterns and key drivers of AMR emergence. The project takes an interdisciplinary approach, with a team of researchers with biomedical, computational science, social science, and economics backgrounds.

Complex systems modelling and analysis will be used to better interpret available data and discover unidentified drivers and trends of AMR. The final aim is to have a predictive model describing AMR in households and hospitals and predicting its prevalence at regional or country level, in order to allow for comparative analyses between regions and countries, to get a better understanding of the currently unknown drivers of AMR and of the potential impact of different interventions to reduce AMR from country to global level.

The team received seed funding from the Amsterdam University Fund.