Olu-Abiodun Oluwatosin, Faturoti Aderinsola, Adepoju Akinmade, Adeloye Davies, Adebiyi Akindele, Abiodun Olumide
Department of Nursing, Crescent University, Abeokuta, Ogun State, Nigeria.
Department of Community Medicine, Babcock University Teaching Hospital, Ilishan-Remo, Ogun State, Nigeria.
PLoS One. 2025 Aug 22;20(8):e0330904. doi: 10.1371/journal.pone.0330904. eCollection 2025.
Monitoring infectious diseases is essential for preventing and controlling outbreaks, especially in low- and middle-income countries (LMICs), where issues like poor infrastructure, lack of trained staff, and limited resources can make data collection challenging. Digital tools such as mobile health apps and electronic reporting systems show promise in addressing these problems. However, it's still unclear how well these tools actually improve the quality of data, like how quickly information is reported, how accurate it is, whether all necessary data is captured, and if the data can be trusted.
This review aims to explore three main points: (1) how digital tools influence the quality of infectious disease data in LMICs; (2) what factors help or hinder their successful use; and (3) what recommendations can be made for policymakers and health workers based on the evidence.
We will search several databases, including PubMed/MEDLINE, EMBASE, Scopus, CINAHL, and Google Scholar, for studies published from January 2000 to July 2025. To further reduce publication bias, we will search the following institutional repositories (African Health Observatory and Indian Council of Medical Research). The types of studies are randomised trials, quasi-experimental studies, and mixed-methods evaluations that compare digital solutions with traditional methods in LMIC settings. Data extracted will include outcomes such as delays in reporting, error rates, and completeness, and factors like infrastructure and workforce readiness. The quality of each study will be assessed using ROBINS-I for non-randomized studies and ROB2 for randomized controlled trials. Where possible, we will combine data statistically using meta-analysis and analyse qualitative findings for deeper insights.
This review will offer a clear picture of how effective digital tools are in improving disease surveillance. It will identify common challenges, such as poor connectivity and issues with system integration, and emphasize factors that lead to success, like proper training and government support. Overall, the findings will help shape better strategies to strengthen digital disease monitoring, finally contributing to stronger global health security.
监测传染病对于预防和控制疫情至关重要，尤其是在低收入和中等收入国家（LMICs），那里基础设施薄弱、缺乏训练有素的工作人员以及资源有限等问题会使数据收集面临挑战。移动健康应用程序和电子报告系统等数字工具在解决这些问题方面显示出前景。然而，这些工具实际上能在多大程度上提高数据质量，比如信息报告的速度有多快、准确性如何、是否捕获了所有必要数据以及数据是否可信，仍不清楚。
本综述旨在探讨三个主要问题：（1）数字工具如何影响低收入和中等收入国家传染病数据的质量；（2）哪些因素有助于或阻碍其成功使用；（3）基于证据可以向政策制定者和卫生工作者提出哪些建议。
我们将在包括PubMed/MEDLINE、EMBASE、Scopus、CINAHL和谷歌学术搜索在内的多个数据库中检索2000年1月至2025年7月发表的研究。为了进一步减少发表偏倚，我们将检索以下机构知识库（非洲卫生观察站和印度医学研究理事会）。研究类型为随机试验、准实验研究以及在低收入和中等收入国家环境中将数字解决方案与传统方法进行比较的混合方法评估。提取的数据将包括报告延迟、错误率和完整性等结果，以及基础设施和劳动力准备情况等因素。每项研究的质量将使用ROBINS - I评估非随机研究，使用ROB2评估随机对照试验。在可能的情况下，我们将使用荟萃分析进行统计数据合并，并分析定性结果以获得更深入的见解。
本综述将清晰呈现数字工具在改善疾病监测方面的效果如何。它将识别常见挑战，如连接性差和系统集成问题，并强调导致成功的因素，如适当的培训和政府支持。总体而言，研究结果将有助于制定更好的策略来加强数字疾病监测，最终为加强全球卫生安全做出贡献。
