{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T03:16:39Z","timestamp":1785294999318,"version":"3.55.0"},"reference-count":77,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec036\">\n<jats:title>Background<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec037\">\n<jats:title>Methods<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec038\">\n<jats:title>Results<\/jats:title>\n<jats:p>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\u2009=\u200911), investigating the correlation between mobility patterns and the spread of COVID-19 (n\u2009=\u20097), analyzing the recovery of economic activities and travel patterns (n\u2009=\u20095), assessing factors associated with NPI compliance (n\u2009=\u20095), and investigating the impact of COVID-19 lockdowns and non-pharmaceutical interventions (NPI) measures on human behaviors, urban dynamics, and economic activity (n\u2009=\u200918). 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec039\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0322520","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T17:34:28Z","timestamp":1745948068000},"page":"e0322520","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implications, and post-pandemic economic recovery 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Yuen","family":"Phan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan","family":"Wahyudi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5865-1533","authenticated-orcid":true,"given":"Lip Yee","family":"Por","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"pone.0322520.ref001","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s42269-020-00393-x","article-title":"Population density, a factor in the spread of COVID-19 in Algeria: statistic study","volume":"44","author":"N Kadi","year":"2020","journal-title":"Bull Natl Res Cent"},{"key":"pone.0322520.ref002","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/s41597-021-01093-5","article-title":"COVID-19 Flow-Maps an open geographic 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