AI-Enabled Disease Surveillance and Outbreak Prediction: A Theoretical Account of Signal, Noise and Action in Data-Sparse Settings
DOI:
https://doi.org/10.68854/qchzs694Keywords:
disease surveillance, outbreak prediction, epidemic intelligence, artificial intelligence, early warning systems, data sparsity, reporting delay, public health response, Nigeria, AfricaAbstract
Artificial intelligence (AI) is frequently presented as a means of detecting infectious-disease outbreaks earlier, yet the settings in which early detection matters most, including Nigeria and much of sub-Saharan Africa, are those in which surveillance data are incomplete, delayed and unevenly reported. Research on the subject has divided into technical studies reporting high forecasting accuracy and policy analyses calling for stronger governance, and neither explains what a model can infer from sparse reports or when its output leads to action. This paper addresses that gap. Using a conceptual, theory-building design, the study conducted a structured integrative review of peer-reviewed journal articles published between 2023 and 2026. Records were identified through purposive, criterion-based searches of PubMed/MEDLINE and Google Scholar using five pre-specified search strings, and 40 articles formed the final evidence base. Each article was verified against its Crossref, PubMed or publisher record, screened for retractions and corrections, and appraised with design-specific quality criteria, and every claim in the text was checked against the cited abstract. Findings were coded under signal, noise and action and synthesised abductively into propositions. The synthesis indicates that AI improves outbreak detection substantially, chiefly by drawing on data whose errors are independent of routine reporting, but improves the attribution of outbreaks to causes and places only modestly and locally, and contributes little to the public health response unless response pathways are agreed in advance. Data sparsity is accordingly reframed as a problem of identification created by under-ascertainment and reporting delay rather than as a mere shortage of observations. The paper advances seven testable propositions and a confidence-to-response framework linking signal confidence, data quality and corroboration to tiered action. Agencies adopting AI alerts should attach pre-agreed response protocols to each alert and fund data quality alongside modelling, and model developers should validate models over time, at the level at which decisions are made, under independent governance.
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