Effective contact tracing for COVID-19: A systematic review
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Abstract
ABSTRACT Background Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. This systematic review aimed to examine contact tracing effectiveness in the context of COVID-19. Methods Following PRISMA guidelines, MEDLINE, Embase, Global Health, and All EBM Reviews were searched using a range of terms related to contact tracing for COVID-19. Articles were included if they reported on the ability of contact tracing to slow or stop the spread of COVID-19 or on characteristics of effective tracing efforts. Two investigators screened all studies. Results A total of 32 articles were found. All were observational or modelling studies, so the quality of the evidence was low. Observational studies (n=14) all reported that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19. Results of modelling studies (n=18) depended on their assumptions. Under assumptions of prompt and thorough tracing with no further transmission, they found that contact tracing could stop an outbreak (e.g. by reducing the reproduction number from 2.2 to 0.57) or that it could reduce infections (e.g. by 24%-71% with a mobile tracing app). Under assumptions of slower, less efficient tracing, modelling studies suggested that tracing could slow, but not stop COVID-19. Conclusions Observational and modelling studies suggest that contact tracing is associated with better control of COVID-19. Its effectiveness likely depends on a number of factors, including how many and how fast contacts are traced and quarantined, and how effective quarantines are at preventing further transmission. A cautious interpretation suggests that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts, and that once isolated, cases and contacts should infect zero new cases. Less efficient tracing may slow, but not stop, the spread of COVID-19. Inefficient tracing (with delays of 4-5+ days or less than 60% of contacts quarantined with no further transmission) may not contribute meaningfully to control of COVID-19. Funding LP holds the Canada Research Chair in Community Approaches and Health Inequalities (CRC 950-232541). This funding source had no role in the design, conduct, or reporting of the study. Competing interests CEJ has contractual agreements with the Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l’Île-de-Montréal and is founder of Dr. Muscle and the COVID-19 Science Updates ( https://covid1.substack.com/ ). Registration PROSPERO CRD42020198462
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Across disease areas, evidence has shown that partner services are effective if applied strategically for identifying new cases, stopping onward transmission, helping to end outbreaks, and supporting elimination goals.
“Across these disease areas, evidence has shown how effective partner services are if they are applied strategically – not only for identifying new cases to enable individuals and their partners to access treatment and care (84, 85), but also for stopping onward transmission (86), helping to end outbreaks (87) and supporting the achievement of elimination goals (88).”
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- Authors
- Juneau, C.-E., Briand, A.-S., Collazzo, P., Siebert, U., & Pueyo, T.
- Affiliations
- Université de Montréal (CA)
- Universität für Weiterbildung Krems (AT)
- Harvard University (US)
- Corresponding author
- Collazzo, Pablo
- Venue
- Global Epidemiology
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 5
- Pages
- 100103
- ISSN
- 2590-1133 (ISSN-L)
- PMC
- PMC9997056
- OpenAlex
- W4323667182
- Cited by
- 85
- References
- 68
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 32 · 2023: 13 · 2022: 10 · 2021: 9 · 2020: 1
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Global Epidemiology (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Université de Montréal
- Topics
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- contact tracing, COVID-19 pandemic, quarantine, isolation, reproduction number, disease transmission control, public health interventions
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