Epistemic Dimensions of Big Data in Nigerian Public Health Surveillance: Data Practices, Institutional Trust, and Knowledge Production

Big data technologies are increasingly positioned as transformative tools for public health surveillance in Nigeria, promising real-time disease intelligence that surpasses the capacity of conventional reporting systems. This study critically examines the epistemic dimensions of big data in Nigerian public health surveillance, investigating how data practices, institutional trust dynamics, and knowledge production processes shape the quality and use of surveillance intelligence in public health decision-making. A critical realist research philosophy informs a multi-site ethnographic methodology conducted across four health surveillance systems in Nigeria, including the Nigeria Centre for Disease Control's Integrated Disease Surveillance and Response system, a commercial mobile-derived surveillance pilot in Lagos, a syndromic surveillance initiative in Kano, and a community-based digital reporting programme in Enugu. Data collection involved 18 months of fieldwork incorporating observation, 68 interviews with epidemiologists, data scientists, community health workers, and public health policymakers, and document analysis. Theoretical contributions draw on data studies scholarship, epistemic justice theory, and the sociology of knowledge to construct an original Epistemic Framework for Public Health Big Data that addresses data provenance, representational adequacy, interpretive legitimacy, and institutional power dynamics. The study challenges technocentric narratives of big data's public health benefits and identifies epistemic risks arising from the datafication of health surveillance in resource-constrained, high-inequality contexts. Keywords: big data, public health surveillance, epistemic justice, Nigeria Centre for Disease Control, health informatics.