Enhancing Nigeria’s health surveillance system: A data-driven approach to epidemic preparedness and response

Abstract

Introduction: Comprehensive health surveillance systems are needed to identify, monitor, and manage infectious disease outbreaks. Deficits in disease surveillance have resulted in poor resource allocation, high rates of morbidity and mortality, and delays in responding to epidemics in Nigeria. Incorporating cutting-edge technologies such as electronic reporting systems, mobile health (mHealth) applications, artificial intelligence (AI), and geospatial mapping offers a revolutionary chance to fortify Nigeria's health surveillance infrastructure in light of the expanding global adoption of digital health innovations. This article evaluates the advantages and disadvantages of Nigeria's present health surveillance system, examines the role of digital technology in epidemic planning and response, and suggests using digital technologies to enhance disease monitoring and control. Materials and Methods: This study adopted a PRISMA-compliant systematic review technique to guarantee an organised and complete examination of available material. Data were sourced from multiple electronic databases, including Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar, using targeted search terms such as "health system surveillance," "digital health innovations," "electronic reporting system," "data-driven approach," and "epidemic preparedness." Inclusion criteria comprised peer-reviewed journal papers, conference proceedings, and book chapters published in English between 2010 and 2020, concentrating on health monitoring, digital innovations, and pandemic preparation. Studies missing actual evidence or presenting just expert opinions were rejected. A total of 1,697 items were initially retrieved, with 1,375 remaining after duplication elimination. Through title and abstract screening, 798 articles were removed, and additional quality evaluation led to a final selection of 205 suitable sources. Data were retrieved using a standardized pro forma, collecting crucial data such as research objectives, procedures, findings, and consequences. The study employed theme synthesis and narrative synthesis methodologies, supported by the Critical Appraisal Skills Programme (CASP) and Mixed Methods Appraisal Tool (MMAT), to promote validity and reliability. Results: Findings from the comprehensive study revealed that Nigeria’s health monitoring system exhibits severe shortcomings, including insufficient infrastructure, inadequate digital reporting procedures, poor internet penetration in remote regions, and limited implementation of AI-driven predictive modeling. However, worldwide best practices reveal that digital technologies have considerably increased epidemic response in other nations. Notable examples include India’s Aarogya Setu mobile surveillance system, AI-powered illness tracking in South Korea, and geospatial mapping tools utilised in the United States for the COVID-19 response. The paper also shows that Nigeria’s Surveillance Outbreak Response Management and Analysis System (SORMAS), although promising, is underutilized owing to infrastructural limitations, poor health worker training, and policy inadequacies. The convergence of AI-driven analytics, cloud-based health surveillance, and mobile-based community reporting systems has the potential to overcome existing gaps and improve Nigeria’s epidemic preparation. Discussion: Comparative research with other countries highlights the importance of investing in digital infrastructure and implementing legislative changes to improve disease surveillance. Stronger epidemic response skills have been demonstrated by nations with interoperable digital platforms, AI-driven early warning systems, and contemporary electronic health records (EHRs). Effective disease monitoring and response have been hampered by Nigeria's digital divide, disjointed surveillance networks, and low rates of technology use. Notwithstanding these obstacles, the study points to a number of opportunities, such as public-private partnerships for internet expansion, training initiatives to raise health workers' digital literacy, and partnerships with international health organisations to improve real-time monitoring and data integration. The results highlight the necessity of a multi-stakeholder approach that combines community involvement, technology advancements, and government actions to modernise Nigeria's health monitoring system. Conclusion: This study underlines the critical need for digital transformation in Nigeria’s health monitoring system to improve epidemic preparedness and response. Strengthening electronic reporting, utilising AI for predictive modeling, integrating geographic mapping technologies, and increasing mHealth applications are crucial for building a strong surveillance framework. Policy changes should focus on boosting internet connectivity, standardizing data-sharing procedures, and promoting engagement with global health institutions. Future studies should examine the practicality of AI-driven health surveillance in resource-constrained contexts and analyse the long-term impact of digital health advances on epidemic management in Nigeria. Implementing these suggestions would strengthen Nigeria’s capacity to recognise and respond to emerging health concerns effectively