International Journal of Academic Management Science Research (IJAMSR) ISSN: 2643-900X Vol. 9 Issue 3 March - 2025, Pages: 247-267 www.ijeais.org/ijamsr 247 Advances in Data-Driven Strategies for Disease Mitigation and Global Health Preparedness 1 ADELAIDE YEBOAH FORKUO, 2 Tunde Victor Nihi; 3 Opeyemi Olaoluawa Ojo, * 4 Collins Nwannebuike Nwokedi, OLAKUNLE SAHEED SOYEGE 5 1 California State University, Bakersfield CA, USA 2 East Texas A&M University, Commerce, TX , USA 3 Tritek Business Consulting, London, United Kingdom 4 Umgeni Pschiatric Hospital Medical, Pietermaritzburg, South Africa., 5 Independent Researcher, Maryland, USA *Email: collinsnwokedi@gmail.com Abstract: The rapid advancement of data-driven strategies has significantly transformed disease mitigation and global health preparedness. Leveraging artificial intelligence (AI), machine learning (ML), big data analytics, and digital health technologies, modern epidemiological frameworks enable real-time disease surveillance, predictive modeling, and targeted intervention strategies. This study explores emerging innovations in data-driven public health responses, focusing on the integration of diverse data sources, computational modeling, and decision support systems for pandemic preparedness and infectious disease control. The proposed framework consists of four core components: (1) Data Acquisition and Integration, which consolidates structured and unstructured data from electronic health records (EHRs), mobile health (mHealth) platforms, wearable devices, and genomic databases; (2) Predictive Analytics and Modeling, where AI and ML algorithms process vast datasets to identify outbreak patterns, transmission dynamics, and risk factors; (3) Digital Health and Decision Support Systems, which leverage cloud computing and Internet of Things (IoT) technologies to enhance early warning mechanisms and resource allocation; and (4) Policy Implementation and Global Health Strategies, which focus on improving real-time response, health equity, and international collaboration in disease prevention efforts. Recent applications of AI-driven disease surveillance in COVID-19, Ebola, and antimicrobial resistance management demonstrate the effectiveness of integrating multi-source data and computational epidemiology to guide strategic public health interventions. Despite the potential of data-driven solutions, challenges such as data privacy, interoperability, algorithmic biases, and ethical considerations must be addressed to ensure equitable healthcare outcomes. Future research should focus on refining predictive models, integrating real-time genomic surveillance, and enhancing the resilience of global health infrastructures. This study concludes that data-driven approaches play a crucial role in strengthening disease mitigation strategies, improving response efficiency, and fostering global health security. By harnessing AI, ML, and big data analytics, health systems can proactively detect emerging threats, optimize resource distribution, and improve pandemic resilience. Future advancements in computational epidemiology and digital health solutions will be critical in addressing evolving global health challenges. Keywords: Data-driven strategies, disease mitigation, global health preparedness, artificial intelligence, machine learning, big data analytics, epidemiology, public health surveillance, predictive modeling, digital health solutions. 1.0. Introduction Emerging infectious diseases (EIDs) present significant challenges to global health, economic stability, and societal well-being, a reality underscored by recent outbreaks such as COVID-19, Ebola, and Zika virus. These events have highlighted the urgent need for advanced strategies that can predict and manage such diseases more effectively. Traditional public health interventions have proven inadequate in responding to the complexities and rapid spread of these diseases, prompting a shift towards innovative approaches that enhance global health preparedness (Olayinka et al., 2017; Jia et al., 2020). As evidenced by the limitations observed during the COVID-19 pandemic, integrating health systems with robust data-driven strategies is vital to improving outreach and intervention capabilities in public health frameworks (Waddell et al., 2024; Negri et al., 2024). Data-driven methodologies are increasingly at the forefront of modern disease mitigation. These innovations harness the power of data analytics, artificial intelligence (AI), and machine learning, which enable real-time surveillance and predictive modeling essential for early outbreak detection (Adewumi, et al., 2024, Edoh, et al., 2024, Elufioye, et al., 2024, Nnagha, et al., 2023). Public health surveillance increasingly relies on comprehensive datasets drawn from various sources like social media, electronic health records, and geographical information systems. By analyzing these data, public health professionals can identify disease patterns, predict potential outbreaks, and deploy resources more strategically and effectively (Nazakat et al., 2022; , Babarinde et al., 2023; ,