Strengthening public health surveillance through advanced analytics and artificial intelligence for national preparedness
Year: 2025
Type: article
Study design: Systematic Review
Abstract: Public health surveillance systems serve as the operational backbone of national health security. Traditional surveillance approaches, however, suffer from well-documented limitations including reporting delays, incomplete case ascertainment, siloed data systems and limited capacity for real-time signal detection. The COVID-19 pandemic exposed these vulnerabilities at a catastrophic scale, which demonstrates that gaps in surveillance infrastructure translate directly into preventable morbidity, mortality and economic loss. Advanced analytics and artificial intelligence (AI) have emerged as transformative tools with the potential to fundamentally strengthen the architecture of public health surveillance and national preparedness. This article provides a comprehensive, evidence-based examination of how AI and advanced analytics are reshaping public health surveillance for national preparedness. This paper synthesizes empirical evidence, identifies implementation challenges, evaluates equity implications and proposes a policy and research agenda aligned with emerging national and global health security priorities. A systematic narrative review of peer-reviewed literature published between 2012 and 2025 was conducted using PubMed, Google Scholar and Web of Science. Studies were selected based on relevance to AI, machine learning, natural language processing, epidemiological forecasting and public health surveillance. A total of 58 sources met inclusion criteria and formed the evidentiary foundation of this review. AI-enabled surveillance systems demonstrated superior performance compared to conventional approaches. Machine learning models achieve outbreak detection 2-4 weeks earlier than traditional methods. Deep learning algorithms for syndromic surveillance attain sensitivity rates exceeding 90%. NLP-based systems extract disease signals from unstructured data sources with accuracy approaching 94%. AI-driven forecasting models outperform standard epidemiological models in COVID-19, influenza and dengue prediction. However, significant implementation barriers persist, including data fragmentation, algorithmic bias, workforce capacity gaps and regulatory uncertainty. AI and advanced analytics represent a generational opportunity to modernize public health surveillance infrastructure. Realizing this opportunity demands sustained investment in interoperable data systems, equity-centered algorithm design, multidisciplinary workforce development and evidence-based governance frameworks. Nations that act decisively to integrate AI into their surveillance architectures will be substantially better positioned to detect, respond to and mitigate future public health emergencies. Keywords: Public Health, Surveillance, Analytics, AI.
Authors: Collins Amoah, Glory Edinam Afeti
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7166312941
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