Algorithmic Fairness in Clinical Predictive Models: A Review of Bias Audits and Mitigation Strategies in Epidemiological Research

Abstract

Background: Digital Epidemiology has emerged as a transformative approach to infectious disease surveillance, leveraging digital data streams such as social media, search queries, and mobility patterns. While these methods offer speed and scale, they introduce significant statistical and ethical challenges, particularly bias and fairness concerns in predictive modeling. Aim: This review aims to examine algorithmic fairness in clinical predictive models within epidemiological research, focusing on bias audits and mitigation strategies in the context of Digital Epidemiology. Methods: A comprehensive literature review was conducted, analyzing methodological differences between classical and digital approaches, sources of bias, and corrective strategies. Key themes include representativeness, measurement error, and algorithmic bias in machine learning models trained on digital data. Results: Findings reveal that Digital Epidemiology offers real-time, large-scale data collection but suffers from structural biases due to self-selection, platform design, and digital divides. Bias mitigation is often retrospective, relying on weighting, normalization, and cross-validation. Ethical concerns such as privacy and informed consent intersect with fairness, as predictive models risk amplifying inequities. Integration of classical rigor with digital flexibility and continuous bias audits is essential for equitable outcomes. Conclusion: Digital Epidemiology complements classical methods but requires robust frameworks for bias detection, ethical governance, and algorithmic transparency. Sustained collaboration, standardization, and inclusive data practices are critical to ensure predictive models support fair and actionable public health decisions.

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