Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria

Adamu Muhammad Ibrahim; Mohamed Mustaf Ahmed; Shuaibu Saidu Musa; Usman Abubakar Haruna; Mohammed Raihanatu Hamid; Olalekan John Okesanya; Aishat Muhammad Saleh; Don Eliso III Lucero-Prisno

doi:10.37349/edht.2025.101140

Exploration of Digital Health Technologies ›› 2025, Vol. 3 ›› Issue (1) :101140 DOI: 10.37349/edht.2025.101140

Letter to the Editor

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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria

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¹ Department of Immunology, School of Medical Laboratory Science, Usmanu Danfodiyo University, Sokoto 840004, Nigeria

² Department of Medicine and Surgery, Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu 252, Somalia

³ School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand

⁴ Department of Nursing Science, Ahmadu Bello University, Zaria 810107, Nigeria

⁵ Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan

⁶ Department of Mathematics and Computer Science, College of Education, Billiri, Gombe 771101, Nigeria

⁷ Department of Public Health and Maritime Transport, University of Thessaly, 38221 Volos, Greece

⁸ Department of Nursing Science, Faculty of Allied Health Sciences, Usmanu Danfodiyo University, Sokoto 840004, Nigeria

⁹ Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, WC1E 7HT London, United Kingdom

¹⁰ Office for Research, Innovation and Extension Services, Southern Leyte State University, Sogod 6606, Philippines

¹¹ Office for Research, Centre for University Research, University of Makati, Makati City 1215, Philippines

^*Adamu Muhammad Ibrahim, Department of Immunology, School of Medical Laboratory Science, Usmanu Danfodiyo University, Sokoto 840004, Nigeria.amuhammadibrahim37@gmail.com

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Abstract

Cholera continues to pose a significant public health challenge in Nigeria, driven by poor sanitation, inadequate water quality, and climatic factors that create favorable conditions for outbreaks. Since the first epidemic in 1972, Nigeria has experienced recurrent outbreaks, with the most severe in 1991, resulting in over 7,000 deaths. Current surveillance systems and diagnostic methods are limited by infrastructural gaps, insufficient skilled personnel, and inadequate reporting, leading to delays in outbreak detection and response. These limitations exacerbate the public health burden, increasing mortality and the economic impact of cholera epidemics. This paper explores the potential of artificial intelligence (AI) and machine learning (ML) to address these challenges. AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources-such as meteorological, environmental, and health records-to detect patterns and predict outbreaks. Case studies from other cholera-endemic regions, where AI achieved high predictive accuracy, demonstrate its transformative potential. By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. However, challenges such as data quality, standardization, and infrastructural deficits must be addressed. Multi-sectoral collaboration involving public health authorities, AI specialists, and policymakers is essential for the successful deployment of these technologies. This article concludes that AI-powered cholera surveillance systems have the potential to revolutionize public health outcomes, reducing cholera-related morbidity and mortality in resource-limited settings like Nigeria.

Keywords

Cholera / artificial intelligence / public health / Nigeria

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Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso III Lucero-Prisno. Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria. Exploration of Digital Health Technologies, 2025, 3 (1) : 101140 DOI:10.37349/edht.2025.101140

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References

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[1]	Adagbada AO, Adesida SA, Nwaokorie FO, Niemogha M, Coker AO. Cholera epidemiology in Nigeria: an overview. Pan Afr Med J. 2012; 12:59.

[2]	Fagbamila IO, Abdulkarim MA, Aworh MK, Uba B, Balogun MS, Nguku P, et al. Cholera outbreak in some communities in North-East Nigeria, 2019: an unmatched case-control study. BMC Public Health. 2023; 23:446.

[3]	Buliva E, Elnossery S, Okwarah P, Tayyab M, Brennan R, Abubakar A. Cholera prevention, control strategies, challenges and World Health Organization initiatives in the Eastern Mediterranean Region: A narrative review. Heliyon. 2023; 9:e15598.

[4]	Alam M, Hasan NA, Sultana M, Nair GB, Sadique A, Faruque AS, et al. Diagnostic Limitations to Accurate Diagnosis of Cholera. J Clin Microbiol. 2010; 48:3918-22.

[5]	Cholera-Global situation [Internet]. WHO; c2025 [cited 2024 Sep 24]. Available from:https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON426

[6]

Alloghani M, Al-Jumeily D, Aljaaf AJ, Khalaf M, Mustafina J, Tan SY. The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review. In: Khalaf MI, Al-Jumeily D, Lisitsa A, editors. Applied Computing to Support Industry: Innovation and Technology. Cham: Springer; 2020. pp. 248-61.

[7]	Kumar KK, Nasar IS. Various Approaches and Applications of Artificial Intelligence in Preventing and Detecting Diseases. In: Rokeya B, editor. Pharmaceutical Research-Recent Advances and Trends Vol. 1. BP International;2024. pp. 89-100.

[8]	Cholera outbreaks predicted using climate data and AI [Internet]. UNDRR; [cited 2024 Sep 24]. Available from:https://www.preventionweb.net/news/cholera-outbreaks-predicted-using-climate-data-and-ai

[9]	Badkundri R, Valbuena V, Pinnamareddy S, Cantrell B, Standeven J. Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning. arXiv:1902.06739 [Preprint].2019 [cited 2024 Sep 24]. Available from:http://arxiv.org/abs/1902.06739

[10]	Eze CE, Igwama GT, Nwankwo EI, Emeihe EV. AI-driven health data analytics for early detection of infectious diseases: A conceptual exploration of U.S. public health strategies. Compr Res Rev Sci Technol. 2024; 2:74-82.