Asonganyi Ettah Agnes, Tanue Elvis Asangbeng, Kwalar Ginyu Innocentia, Kibu Odette Dzemo, Ondua Moise, Sandeu Maurice Marcel, Ngono Ema Patrick Jolly, Nkweteyim Denis, Nyamsi Madeleine L, Achankeng Peter L, Tchapga Christian, Ayuk Justine, Halle-Ekane Gregory Eddie, Kong Jude Dzevela, Nsagha Dickson Shey
Department of Public Health and Hygiene, Faculty of Health Sciences, University of Buea, P.O Box 63, Buea, Cameroon.
DigiCare Cameroon Consortium, University of Buea, South West Region, Buea, Cameroon.
J Trop Med. 2025 Aug 19;2025:8896234. doi: 10.1155/jotm/8896234. eCollection 2025.
Rapid digital responses to pandemics highlight advancements in healthcare, data sharing, and artificial intelligence (AI). While AI has driven progress in precision medicine, drug discovery, and vaccine development, its application to emerging and reemerging infectious diseases (ERIDs) remains underexplored, presenting critical challenges in addressing future health threats. The study evaluated knowledge of ERIDs, AI, and Digital One Health (DOH) technologies, examined preparedness for their adoption in home healthcare, and identified factors influencing readiness to utilize these technologies in selected health districts of Cameroon. A cross-sectional study assessed the preparedness of communities in Buea, Limbe, Bonassama, and New-Bell Health Districts to adopt AI and DOH technologies from April to May 2024. Systematic random sampling included 33 communities, with data collected using face-to-face structured questionnaires. Analysis using SPSS Version 26 involved descriptive statistics and logistic regression, with statistical significance set at < 0.05 and a 95% confidence interval to identify key associations. Among 1625 participants, only 280 (17.2%) had good knowledge of ERIDs, with COVID-19 (68.8%) and cholera (94.5%) being the most recognized examples. Knowledge of AI and DOH technologies was poor, with only 166 (10.2%) demonstrating accurate understanding. Early disease detection emerged as a critical application of AI for ERID control. Preparedness to adopt AI and DOH technologies was reported by 941 (57.9%), with 64.5% comfortable with AI-generated interpretations and willing to use digital health tools during ERID outbreaks. Factors independently associated with preparedness included being a student (AOR = 2.678; 95% CI: 1.744-4.113; < 0.001), good knowledge of AI and DOH (AOR = 7.141; 95% CI: 4.192-12.162; < 0.001), and prior training on AI and digital health (AOR = 3.081; 95% CI: 2.272-4.179; < 0.001). The study revealed insufficient knowledge of ERIDs, AI, and DOH but high preparedness to adopt these technologies for home care. Enhanced educational campaigns are recommended to improve community understanding and effective utilization of AI and DOH for controlling ERIDs.
对大流行病的快速数字应对凸显了医疗保健、数据共享和人工智能（AI）方面的进步。虽然人工智能推动了精准医学、药物发现和疫苗开发的进展，但其在新兴和再发传染病（ERIDs）中的应用仍未得到充分探索，在应对未来健康威胁方面面临重大挑战。该研究评估了对新兴和再发传染病、人工智能和数字一体化健康（DOH）技术的了解，检查了在家庭医疗保健中采用这些技术的准备情况，并确定了影响喀麦隆选定健康区使用这些技术意愿的因素。一项横断面研究评估了布埃亚、林贝、博纳萨马和新贝尔健康区的社区在2024年4月至5月采用人工智能和数字一体化健康技术的准备情况。系统随机抽样包括33个社区，使用面对面结构化问卷收集数据。使用SPSS 26版进行的分析包括描述性统计和逻辑回归，统计显著性设定为<0.05和95%置信区间以确定关键关联。在1625名参与者中，只有280人（17.2%）对新兴和再发传染病有良好的了解，其中新冠病毒（68.8%）和霍乱（94.5%）是最广为人知的例子。对人工智能和数字一体化健康技术的了解很差，只有166人（10.2%）表现出准确的理解。早期疾病检测成为人工智能在控制新兴和再发传染病方面的关键应用。941人（57.9%）报告了采用人工智能和数字一体化健康技术的准备情况，64.5%的人对人工智能生成的解释感到满意，并愿意在新兴和再发传染病爆发期间使用数字健康工具。与准备情况独立相关的因素包括是学生（比值比=2.678；95%置信区间：1.744-4.113；<0.001）、对人工智能和数字一体化健康有良好的了解（比值比=7.141；95%置信区间：4.192-12.162；<0.001）以及之前接受过人工智能和数字健康方面的培训（比值比=3.081；95%置信区间：2.272-4.179；<0.001）。该研究揭示了对新兴和再发传染病、人工智能和数字一体化健康的了解不足，但采用这些技术进行家庭护理的准备程度很高。建议加强教育宣传活动，以提高社区对人工智能和数字一体化健康的理解以及对控制新兴和再发传染病的有效利用。
