The increasing frequency and severity of infectious disease outbreaks—exemplified by COVID-19, seasonal influenza, and emerging pathogens such as HMPV and MERS—demand a paradigm shift toward proactive, data-driven public health strategies. This whitepaper explores the transformative role of predictive analytics in outbreak mitigation, emphasizing real-time disease forecasting, early intervention, and strategic resource allocation. Drawing upon a comprehensive methodological review, the paper evaluates statistical, machine learning (ML), and hybrid modelling approaches, alongside real-world case studies and validation metrics. Findings reveal that machine learning (ML) and hybrid models significantly outperform traditional approaches in terms of sensitivity, specificity, and adaptability, particularly when leveraging diverse data sources such as syndromic surveillance, mobility trends, and social media signals. Key challenges—such as data sparsity, model scalability, interpretability, and ethical concerns—are critically examined, with corresponding mitigation strategies proposed. The discussion highlights the necessity of interdisciplinary collaboration, equitable access, and clinician training to ensure operational success. The whitepaper concludes with actionable policy recommendations and future research directions, advocating for next-generation algorithms, explainable AI, and federated learning frameworks to support global health resilience. As predictive analytics evolve into a cornerstone of epidemiological intelligence, their responsible adoption will be pivotal to enhancing preparedness and response in the face of current and future health crises.
Keywords: Infectious diseases, machine learning, outbreak forecasting, predictive analytics, public health strategy, real-time surveillance
