Utilising digital contact tracing during a pandemic to measure contact trends by risk group
9 affiliations
- 1Communicable Diseases Agency, Singapore, Singapore
- 2Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- 3Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore
- 4National Centre for Infectious Diseases, Singapore, Singapore
- 5Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- 6Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- 7Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- 8Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, Singapore
- 9Ministry of Health, Singapore, Singapore
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.
Methods: 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.
Results: 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.
Conclusion: DCT data can provide insight into contact patterns and enable assessment of PHSM’s impact over pandemic phases.
Related
- Utilising digital technologies for mycetoma management: A comprehensive review of advances and opportunities
- Evaluating the acceptability, feasibility, and implementation fidelity of a multipurpose mobile health (mHealth) app used for Tuberculosis (TB) contact tracing by Ward-Based Outreach Teams (WBOTs) in South Africa
- Variation in social contact levels and changes during the COVID-19 pandemic by occupation and socio-economic status in east Zimbabwe
- Are fast test results preferable to high test sensitivity in contact-tracing strategies?
- The Effects of a Digital Exercise Therapy Application for Patients with Shoulder and Knee Pain During the COVID-19 Pandemic
BibTeX
@article{wee2026utilising,
title = {{Utilising digital contact tracing during a pandemic to measure contact trends by risk group}},
author = {Liang En Wee and Jeremy Wei Quan Chan and Calvin Chiew and Russ Li and Zheng Jie Marc Ho and Vernon Lee and David Lye and Kelvin Bryan Tan},
journal = {Communications Medicine},
year = {2026},
month = jul,
doi = {10.1038/s43856-026-01763-8},
url = {https://doi.org/10.1038/s43856-026-01763-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Utilising digital contact tracing during a pandemic to measure contact trends by risk group
AU - Liang En Wee
AU - Jeremy Wei Quan Chan
AU - Calvin Chiew
AU - Russ Li
AU - Zheng Jie Marc Ho
AU - Vernon Lee
AU - David Lye
AU - Kelvin Bryan Tan
PY - 2026
DA - 2026/07/08
JO - Communications Medicine
DO - 10.1038/s43856-026-01763-8
UR - https://doi.org/10.1038/s43856-026-01763-8
AB - 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. Methods: 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. Results: 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. Conclusion: DCT data can provide insight into contact patterns and enable assessment of PHSM’s impact over pandemic phases.
SN - 2730-664X
ER -