{
  "abstract": "Background: Monitoring contact patterns is important for assessing impact of public-health-and-social-measures (PHSM) during a pandemic, but existing methods, such as contact surveys or mobility data, have limitations. While the COVID-19 pandemic provided impetus for population-wide adoption of digital-contact-tracing (DCT), prior studies that utilised DCT to evaluate contact patterns evaluated temporal variations, lacking granularity pertaining to impact in at-risk groups.\n\nMethods: Population-wide DCT data was utilised to construct a cohort of SARS-CoV-2-infected community-dwelling adults (N = 544,259), evaluating temporal trends in contact patterns up to 5 days prior to positive SARS-CoV-2 test. Average contact number recorded on DCT was aggregated across four phases (baseline; tightened measures; relaxed measures; Omicron-surge). Incidence-rate-ratios (IRR) of average contacts was then compared across various phases, with the baseline phase as reference, via generalised linear models with a Poisson distribution. Results were stratified by vaccination status, comorbidities and occupation.\n\nResults: Over the 8-month study period, we observe a downtrend in average contacts, versus baseline. Amongst fully-vaccinated cases, average contacts fall from 5.93 at baseline to 4.68 during tightened-measures (IRR = 0.78[95%CI = 0.77-0.79]), then increase to 5.23 when measures are relaxed, albeit still below baseline (IRR = 0.86[95%CI = 0.85-0.87]), and finally drop to 3.51 during Omicron (IRR = 0.58[95%CI = 0.57-0.58]). PHSM consistently impacts non-household contact, but does not initially affect household contact, which declines only during Omicron emergence, despite no change in PHSM. Frail individuals with comorbidities have higher non-household close-contacts, as do essential/frontline workers.\n\nConclusion: DCT data can provide insight into contact patterns and enable assessment of PHSM’s impact over pandemic phases.",
  "abstract_shown": true,
  "affiliations": [
    "Communicable Diseases Agency, Singapore, Singapore",
    "Duke-NUS Medical School, National University of Singapore, Singapore, Singapore",
    "Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore",
    "National Centre for Infectious Diseases, Singapore, Singapore",
    "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore",
    "Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore",
    "Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore",
    "Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, Singapore",
    "Ministry of Health, Singapore, Singapore"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Liang En Wee",
      "orcid": "0000-0001-6428-9999"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Jeremy Wei Quan Chan"
    },
    {
      "affs": [
        0
      ],
      "name": "Calvin Chiew",
      "orcid": "0000-0002-8217-7971"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Russ Li",
      "orcid": "0000-0002-5672-0766"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Zheng Jie Marc Ho"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Vernon Lee"
    },
    {
      "affs": [
        0,
        3,
        5,
        6,
        7
      ],
      "name": "David Lye"
    },
    {
      "affs": [
        0,
        1,
        4,
        5,
        8
      ],
      "name": "Kelvin Bryan Tan",
      "orcid": "0000-0002-4778-7184"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5pqk1p7bpve8p.bib",
  "cited_by": 0,
  "doi": "10.1038/s43856-026-01763-8",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13724710/",
  "id": "5pqk1p7bpve8p",
  "issns": [
    "2730-664X"
  ],
  "landing_url": "https://doi.org/10.1038/s43856-026-01763-8",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1710|COVID-19 Digital Contact Tracing"
    ]
  ],
  "openalex": "W7167683180",
  "pdf_url": "https://www.nature.com/articles/s43856-026-01763-8_reference.pdf",
  "pmcid": "PMC13724710",
  "pmid": "42420400",
  "published": "2026-07-08",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/5pqk1p7bpve8p.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:23:14Z",
      "native_id": "PMC13724710",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s43856-026-01763-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2730-664X",
      "subfield": "general"
    }
  ],
  "title": "Utilising digital contact tracing during a pandemic to measure contact trends by risk group",
  "title_text": "Utilising digital contact tracing during a pandemic to measure contact trends by risk group",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:21:07Z",
  "url": "https://research.draconitic.com/paper/5pqk1p7bpve8p",
  "venue": "Communications Medicine",
  "versions": []
}