Lyimo Neema Nicodemus, Fue Kadeghe Goodluck, Materu Silvia Francis, Kilatu Ndimile Charles, Telemala Joseph Philipo
Department of Informatics and Information Technology, Sokoine University of Agriculture, Morogoro, Tanzania.
Department of Engineering Sciences and Technology, Sokoine University of Agriculture, Morogoro, Tanzania.
Front Artif Intell. 2025 Jun 4;8:1597727. doi: 10.3389/frai.2025.1597727. eCollection 2025.
This study evaluates the potential of machine learning (ML) to predict and manage weather-sensitive waterborne diseases (WSWDs) in selected Tanzanian districts, focusing on environmental health officers' (EHOs) knowledge and perceptions. It explores EHOs' familiarity with information and communication technology (ICT) and artificial intelligence (AI)/ML, alongside challenges and opportunities for integrating AI-driven public health solutions.
A census-style survey was conducted among EHOs in three district councils. A structured questionnaire, piloted in one district, was administered to 76 EHOs, achieving a 66% response rate. Data were analyzed using descriptive and inferential statistics to assess knowledge levels, perceptions, and gender-related differences.
Most EHOs were moderately familiar with ICT; however, only 54% had prior exposure to AI/ML concepts, and 64% reported limited AI familiarity. Among the variables examined, only prior exposure to AI/ML concepts and self-reported familiarity with AI demonstrated statistically significant associations with gender. Despite this, the majority recognized AI/ML's potential to improve disease prediction accuracy. Key barriers to ML adoption include inadequate technical infrastructure, data quality issues, and a shortage of expertise. Opportunities identified included utilizing historical disease data, integrating AI with meteorological information, and using satellite imagery for surveillance.
The study highlights frontline health workers' perceived barriers to ML adoption and suggests that gender influences awareness and engagement with AI and ML technologies. Strengthening technical capacity, improving data quality, and fostering cross-sector collaboration are critical for successful AI/ML integration. These insights offer a roadmap for resilience to WSWDs in developing countries like Tanzania through data-driven technologies.
本研究评估了机器学习（ML）在坦桑尼亚选定地区预测和管理对天气敏感的水源性疾病（WSWDs）的潜力，重点关注环境卫生官员（EHOs）的知识和认知。它探讨了EHOs对信息通信技术（ICT）和人工智能（AI）/ML的熟悉程度，以及整合人工智能驱动的公共卫生解决方案所面临的挑战和机遇。
在三个区议会的EHOs中进行了一次普查式调查。在一个区进行了预测试的结构化问卷，对76名EHOs进行了调查，回复率为66%。使用描述性和推断性统计分析数据，以评估知识水平、认知和性别差异。
大多数EHOs对ICT有一定程度的熟悉；然而，只有54%的人以前接触过AI/ML概念，64%的人表示对AI的熟悉程度有限。在所研究的变量中，只有以前接触过AI/ML概念和自我报告的对AI的熟悉程度与性别有统计学上的显著关联。尽管如此，大多数人认识到AI/ML在提高疾病预测准确性方面的潜力。采用ML的主要障碍包括技术基础设施不足、数据质量问题和专业知识短缺。确定的机会包括利用历史疾病数据、将AI与气象信息整合以及使用卫星图像进行监测。
该研究强调了一线卫生工作者在采用ML方面所感知到的障碍，并表明性别会影响对AI和ML技术的认识和参与度。加强技术能力、提高数据质量和促进跨部门合作对于成功整合AI/ML至关重要。这些见解为通过数据驱动技术增强坦桑尼亚等发展中国家对WSWDs的抵御能力提供了路线图。
