Okmi Mohammed, Ang Tan Fong, Mohd Zaki Muhammad Faiz, Ku Chin Soon, Phan Koo Yuen, Wahyudi Irfan, Por Lip Yee
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayar Persekutuan, Malaysia.
Department of Information Technology and Security, Jazan University, Jazan, Saudi Arabia.
PLoS One. 2025 Apr 29;20(4):e0322520. doi: 10.1371/journal.pone.0322520. eCollection 2025.
The use of traditional mobility datasets, such as travel surveys and census data, has significantly impacted various disciplines, including transportation, urban sensing, criminology, and healthcare. However, because these datasets represent only discrete instances of measurement, they miss continuous temporal shifts in human activities, failing to record the majority of human mobility patterns in real-time. Bolstered by the rapid expansion of telecommunication networks and the ubiquitous use of smartphones, mobile phone network data (MPND) played a pivotal role in fighting and controlling the spread of COVID-19.
We conduct an extensive review of the state-of-the-art and recent advancements in the application of MPND for analyzing the early and post-stages of the COVID-19 pandemic, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Additionally, we evaluate and assess the included studies using the Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP). Furthermore, we apply bibliometric analysis to visualize publication structures, co-authorship networks, and keyword co-occurrence networks.
After the full-text screening process against the inclusion and exclusion criteria, our systematic literature review identified 55 studies that utilized MPND in the context of the COVID-19 pandemic: 46 (83.6%) were quantitative, and 9 (16.4%) were qualitative. These quantitative studies can be classified into five main groups: monitoring and tracking of human mobility patterns (n = 11), investigating the correlation between mobility patterns and the spread of COVID-19 (n = 7), analyzing the recovery of economic activities and travel patterns (n = 5), assessing factors associated with NPI compliance (n = 5), and investigating the impact of COVID-19 lockdowns and non-pharmaceutical interventions (NPI) measures on human behaviors, urban dynamics, and economic activity (n = 18). In addition, our findings indicate that NPI measures had a significant impact on reducing human movement and dynamics. However, demographics, political party affiliation, socioeconomic inequality, and racial inequality had a significant impact on population adherence to NPI measures, which could increase disease spread and delay social and economic recovery.
The usage of MPND for monitoring and tracking human activities and mobility patterns during the COVID-19 pandemic raises privacy implications and ethical concerns. Thus, striking a balance between meeting the ethical requirements and maintaining privacy risks should be further discovered and investigated in the future.
传统移动数据集的使用，如出行调查和人口普查数据，对包括交通、城市传感、犯罪学和医疗保健在内的各个学科产生了重大影响。然而，由于这些数据集仅代表离散的测量实例，它们遗漏了人类活动的连续时间变化，无法实时记录大多数人类移动模式。在电信网络迅速扩张和智能手机广泛使用的推动下，移动电话网络数据（MPND）在抗击和控制新冠疫情传播中发挥了关键作用。
我们按照系统评价和荟萃分析的首选报告项目（PRISMA）指南，对MPND在分析新冠疫情早期和后期阶段应用的最新技术水平和近期进展进行了广泛综述。此外，我们使用混合方法评估工具（MMAT）和批判性评估技能计划（CASP）对纳入的研究进行评估。此外，我们应用文献计量分析来可视化出版物结构、共同作者网络和关键词共现网络。
在根据纳入和排除标准进行全文筛选后，我们的系统文献综述确定了55项在新冠疫情背景下使用MPND的研究：46项（83.6%）为定量研究，9项（16.4%）为定性研究。这些定量研究可分为五个主要组：监测和跟踪人类移动模式（n = 11）、调查移动模式与新冠疫情传播之间的相关性（n = 7）、分析经济活动和出行模式的恢复情况（n = 5）、评估与遵守非药物干预措施相关的因素（n = 5）以及调查新冠疫情封锁和非药物干预（NPI）措施对人类行为、城市动态和经济活动的影响（n = 18）。此外，我们的研究结果表明，NPI措施对减少人类流动和动态有重大影响。然而，人口统计学、政党归属、社会经济不平等和种族不平等对民众遵守NPI措施有重大影响，这可能会增加疾病传播并延迟社会和经济复苏。
在新冠疫情期间使用MPND监测和跟踪人类活动及移动模式引发了隐私问题和伦理担忧。因此，未来应进一步探索和研究如何在满足伦理要求和维持隐私风险之间取得平衡。
