Challenges in Forecasting Antimicrobial Resistance

Abstract

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Keywords: Blumberg S; Cascante Vega J; Medford RJ; Robin T; Suggested citation for this article: Pei S; Zhang Y; antimicrobial resistance; et al. Challenges in forecasting antimicrobial resistance. Emerg Infect Dis. 2023 Apr [date cited]. https://doi.org/10.3201/eid2904.221552; healthcare-associated infections; infectious disease forecasting.

Figure

Open questions for predictive modeling of MRSA. Example questions at the facility level, the population level, and across scales are listed. The upper left panel depicts population-level and facility-level MRSA transmission. The lower left panel represents the uncertainty about the roles of co-selection and competition with MSSA in affecting the dynamics of MRSA. MRSA, methicillin-resistant Staphylococcus aureus; MSSA, methicillin-susceptible S. aureus.