Challenges in Forecasting Antimicrobial Resistance.

Area of Science:

• Public Health
• Infectious Diseases
• Computational Biology

Background:

• Antimicrobial resistance (AMR) is a critical global health concern.
• Computational tools for infectious disease prediction have advanced significantly since the 2000s.
• Real-time forecasting models for antimicrobial-resistant organisms (AMROs) are currently lacking.

Purpose of the Study:

• To discuss the utility of AMRO forecasting across various scales.
• To identify and highlight key challenges in developing AMRO forecasting models.
• To propose future research priorities in the field of AMRO forecasting.

Main Methods:

• This is a perspective piece, not an empirical study.
• Discussion of existing advancements in computational epidemiology.
• Analysis of challenges in data acquisition, model calibration, and implementation.

Main Results:

• The utility of AMRO forecasting at different scales is explored.
• Significant challenges exist in scientific understanding, data quality, and model evaluation.
• The need for immediate research initiation using available data is emphasized.

Conclusions:

• Developing real-time AMRO forecasting models is crucial for public health.
• Addressing data limitations and methodological challenges is essential for progress.
• Initiating research now can galvanize the scientific community and address practical questions.