Evelyn Orevaoghene Onosakponome^1* and Robinson Ndifrekeabasi Itek²

¹Department of Medical Microbiology and Parasitology, Federal University Otuoke, Bayelsa State, Nigeria
²Department of Medical Microbiology and Parasitology, Rivers State University, Nigeria

*Corresponding Author: Evelyn Orevaoghene Onosakponome, Department of Medical Microbiology and Parasitology, Federal University Otuoke, Bayelsa State, Nigeria.

Received: June 15, 2026; Published: July 22, 2026

Abstract

Neglected tropical and parasitic diseases (NTPDs) impose a disproportionate burden on populations in low- and middle-income countries, where conventional surveillance infrastructure is too slow, spatially imprecise, and chronically underresourced to detect outbreaks before they spread. Reliance on manual reporting chains and laboratory confirmation generates delays that allow parasitic infections to establish themselves across communities before any coordinated response is mobilised. Advances in artificial intelligence (AI), machine learning (ML), geographic information systems (GIS), global positioning systems (GPS), remote sensing, mobile health (mHealth), and Internet of Things (IoT) technologies have made it technically feasible to construct real-time smart surveillance architectures capable of delivering high-resolution risk maps, automated diagnostics, and predictive alerts. Evidence from infectious disease prediction, environmental monitoring, and digital health implementation supports the position that AI- enabled image analysis, sensor networks, and predictive modelling can substantially shorten outbreak detection timelines and guide targeted interventions in ways conventional systems cannot. Significant obstacles remain, particularly around infrastructure gaps, data governance, trained workforce shortages, and the long-term financial sustainability of digital systems in endemic settings. This review synthesises current literature on the concept, core components, public health applications, advantages, implementation challenges, and future directions of smart surveillance systems for NTPD control, and concludes with practical recommendations for programme adoption.

Keywords: Neglected Tropical Diseases; Parasitic Disease; Smart Surveillance; Artificial Intelligence; Geographic Information Systems

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Citation

Citation: Evelyn Orevaoghene Onosakponome and Robinson Ndifrekeabasi Itek. “Real-Time Smart Surveillance for Neglected Tropical and Parasitic Diseases (NTPDs)". Acta Scientific Microbiology 9.8 (2026): 02-17.

Copyright

Copyright: © 2026 Evelyn Orevaoghene Onosakponome and Robinson Ndifrekeabasi Itek. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.