ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. Medicine and Public Health

Volume 8 - 2025 | doi: 10.3389/frai.2025.1597727

This article is part of the Research TopicOutbreak Oracles: How AI's Journey through COVID-19 Shapes Future Epidemic StrategyView all 8 articles

Assessing the potential for application of ML in predicting weather-sensitive waterborne diseases in selected districts of Tanzania

Provisionally accepted
Neema Nicodemus LyimoNeema Nicodemus Lyimo1*Kadeghe Goodluck FueKadeghe Goodluck Fue2Silvia Francis MateruSilvia Francis Materu3Ndimile C KilatuNdimile C Kilatu4Joseph TelemalaJoseph Telemala1
  • 1Sokoine University of Agriculture, Morogoro, Tanzania
  • 2Department of Engineering Sciences and Technology, Sokoine University of Agriculture, Morogoro, Tanzania
  • 3Department of Biosciences, Sokoine University of Agriculture, Morogoro, Tanzania
  • 4Morogoro Regional Referral Hospital, Morogoro, Morogoro, Tanzania

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This study assesses the potential of machine learning (ML) in predicting and managing weather-sensitive waterborne diseases (WSWDs) in selected districts of Tanzania, focusing on environmental health officers (EHOs). It examines EHOs' knowledge of information and communication technology (ICT) and artificial intelligence (AI)/ML, their perceptions of AI/ML applications, and the key challenges and opportunities for integrating AI-driven solutions into public health. A structured questionnaire was administered to 76 EHOs across three district councils, achieving a 66% response rate. Findings reveal that while most EHOs are moderately familiar with ICT, only 54% had prior exposure to AI/ML concepts, and 64% reported limited familiarity with AI. Despite this knowledge gap, the majority recognized AI/ML's potential to improve disease prediction accuracy. Key challenges include inadequate technical infrastructure, data quality limitations, and a shortage of expertise. However, opportunities exist in leveraging historical disease data, integrating AI with meteorological information, and utilizing satellite imagery for disease surveillance. The study concludes that addressing infrastructure gaps, enhancing capacity-building efforts, and fostering cross-sector collaborations are essential for adopting AI-driven public health solutions. These findings offer a roadmap for strengthening Tanzania's public health resilience to WSWDs through artificial intelligence and machine learning interventions.

Keywords: climate-sensitive, artificial intelligence, Tanzania, Predictive ML, Waterborne diseases

Received: 25 Mar 2025; Accepted: 12 May 2025.

Copyright: © 2025 Lyimo, Fue, Materu, Kilatu and Telemala. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Neema Nicodemus Lyimo, Sokoine University of Agriculture, Morogoro, Tanzania

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material