Aug 2026· International Journal on Sustainable Industrial Revolution· Vol 1· 0 citations· 68 references
TL;DR
The outcome shows the increasing potentials of artificial intelligence in the provision of strong support and strength for the public health systems as well as in the identification of important challenges like limitation of data, implementations of barriers.
Abstract
The study focused on a systematic review of the application of artificial intelligence in predicting the disease surveillance as well as early warning system in Nigeria. Relevant literatures from the main database were selected, screened and analysed using a predefined criterion. The study assessed the techniques, sources of data and their effectiveness in enhancing the detection and response to disease outbreak early enough. The outcome shows the increasing potentials of artificial intelligence in the provision of strong support and strength for the public health systems as well as in the identification of important challenges like limitation of data, implementations of barriers. Thus, the study provides adequate information into the recent trends, gaps an opportunity for improving the artificial intelligence driven disease surveillance in the country.
Background and Objectives: Artificial intelligence (AI) has emerged in recent years as a transformative technology in disease surveillance systems and has considerable potential to improve the early detection of disease outbreaks. This narrative review was conducted with the aim of identifying, comparing, and comparati...
Seyed Ali Mousavi, Ghobad Moradi, Meysam Abshenas Jami et al.· Iranian Journal of Epidemiol...· 0 citations
Artificial intelligence (AI) could be of assistance in the management of infectious diseases, as well
as in the prevention and early diagnosis of infectious diseases. Additionally, machines that contain
AI are specifically built to work with varying degrees of autonomy during their operation. In
addition, it is a sy...
Dina Darwish· International Journal of Com...· 0 citations
This paper presents a methodological illustration of malaria surveillance in Northern Nigeria using synthetic data, benchmarking five ML models under temporal validation, and employing SHapley Additive exPlanations for robust model interpretability, and proposes a tiered strategic framework encompassing policy, infrast...
L. Aliyu, Abbas B. Umar, Saifuddeen K. Sani et al.· BMC Artificial Intelligence· 1 citation
Background. Foodborne diseases (FBDs) constitute a significant public health issue: the WHO estimates 600 million cases, 420,000 deaths, and 33 million DALYs annually. Traditional surveillance systems suffer from delays in outbreak recognition, under-reporting, and data fragmentation. Artificial intelligence (AI) is em...
D. Lipari, E. Modica, A. L. Puma et al.· Journal of Biological Resear...· 0 citations
Artificial intelligence (AI) is transforming disease surveillance by enabling early outbreak detection, predictive modeling, and real-time analysis of diverse health data sources. These advances can strengthen epidemic preparedness and improve public health decision-making. However, AI adoption has progressed faster th...
Abdullahi Osman Mohamed, A. Afyare, Abdinasir Mohamed Abdi et al.· Frontiers in Digital Health· 0 citations
The feasibility of applying machine learning for public health surveillance in resource constrained environments is confirmed and provides policymakers and healthcare providers with vital tool for mitigating health risk in crude oil host communities through data-driven early warning systems rather than reactive approac...
Olutomisin M. Orogbemi, Balogun John Tope· International Journal of Sci...· 0 citations
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