Publication
Face-to-face interactions estimated using mobile phone data to support contact tracing operations
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.author | Cumbane, Silvino | |
| dc.contributor.author | Gidófalvi, Gyözö | |
| dc.contributor.author | Cossa, Osvaldo | |
| dc.contributor.author | Madivadua Júnior, Afonso | |
| dc.contributor.author | Branco, Frederico | |
| dc.contributor.author | Sousa, Nuno | |
| dc.date.accessioned | 2025-01-28T16:48:08Z | |
| dc.date.available | 2025-01-28T16:48:08Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.abstract | Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis—that such co-located interactions occur in a workplace setting—achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis—that calls made during these co-location events are shorter than usual—achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts. | eng |
| dc.identifier.citation | Cumbane, S.P.; Gidófalvi, G.; Cossa, O.F.; Júnior, A.M.; Sousa, N.; Branco, F. Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations. Big Data Cogn. Comput. 2025, 9, 4. https://doi.org/10.3390/ bdcc9010004 | |
| dc.identifier.doi | 10.3390/bdcc9010004 | |
| dc.identifier.issn | 2504-2289 | |
| dc.identifier.uri | http://hdl.handle.net/10400.2/19428 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | https://www.mdpi.com/2504-2289/9/1/4 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Call Detail Records (CDRs) | |
| dc.subject | Face-to-face meetings | |
| dc.subject | Contact tracing | |
| dc.subject | Co-location | |
| dc.subject | Mozambique | |
| dc.title | Face-to-face interactions estimated using mobile phone data to support contact tracing operations | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | Big Data and Cognitive Computing | |
| oaire.citation.volume | 9 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Sousa | |
| person.givenName | Nuno | |
| person.identifier.ciencia-id | E914-E920-B2A0 | |
| person.identifier.orcid | 0000-0002-2681-5035 | |
| person.identifier.rid | F-5307-2014 | |
| person.identifier.scopus-author-id | 7003438443 | |
| relation.isAuthorOfPublication | 0d49a6e8-e597-4edf-a203-a426d69abd77 | |
| relation.isAuthorOfPublication.latestForDiscovery | 0d49a6e8-e597-4edf-a203-a426d69abd77 |