Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts
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Abstract
Abstract Background To assess the viability of isolation and contact tracing to control onwards transmission from imported cases of 2019-nCoV. Methods We developed a stochastic transmission model, parameterised to the 2019-nCoV outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a 2019 nCoV-like pathogen. We considered scenarios that varied in: the number of initial cases; the basic reproduction number R 0 ; the delay from symptom onset to isolation; the probability contacts were traced; the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings While simulated outbreaks starting with only 5 initial cases, R 0 of 1.5 and little transmission before symptom onset could be controlled even with low contact tracing probability, the prospects of controlling an outbreak dramatically dropped with the number of initial cases, with higher R 0 , and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1.5 were controllable with under 50% of contacts successfully traced. For R 0 of 2.5 and 3.5, more than 70% and 90% of contacts respectively had to be traced to control the majority of outbreaks. The delay between symptom onset and isolation played the largest role in determining whether an outbreak was controllable for lower values of R 0 . For higher values of R 0 and a large initial number of cases, contact tracing and isolation was only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation We found that in most scenarios contact tracing and case isolation alone is unlikely to control a new outbreak of 2019-nCov within three months. The probability of control decreases with longer delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and HDR UK. Research in Context Evidence before this study Contact tracing and isolation of cases is a commonly used intervention for controlling infectious disease outbreaks. This intervention can be effective, but may require intensive public health effort and cooperation to effectively reach and monitor all contacts. When the pathogen has infectiousness before symptom onset, control of outbreaks using contact tracing and isolation is more challenging. Added value of this study This study uses a mathematical model to assess the feasibility of contact tracing and case isolation to control outbreaks of 2019-nCov, a newly emerged pathogen. We used disease transmission characteristics specific to the pathogen and therefore give the best available evidence if contact tracing and isolation can achieve control of outbreaks. Implications of all the available evidence Contact tracing and isolation may not contain outbreaks of 2019-nCoV unless very high levels of contact tracing are achieved. Even in this case, if there is asymptomatic transmission, or a high fraction of transmission before onset of symptoms, this strategy may not achieve control within three months.
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Managed and effective contact tracing and case isolation can control new outbreaks of COVID-19 within a period of three months.
“Studies have highlighted that managed and effective contact tracing and case isolation can control new outbreaks of COVID-19 within a period of three months (Hellewell et al. 2020).”
From Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life (2020) · cites this paper as Hellewell et al. 2020
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- Authors
- Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., Munday, J. D., Kucharski, A. J., Edmunds, W. J., Sun, F., Flasche, S., Quilty, B. J., Davies, N., Liu, Y., Clifford, S., Klepac, P., Jit, M., Diamond, C., Gibbs, H., van Zandvoort, K., Funk, S., & Eggo, R. M.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Eggo, Rosalind M
- ORCID
- Venue
- The Lancet Global Health
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 8
- Issue
- 4
- Pages
- e488-e496
- ISSN
- 2214-109X (ISSN-L)
- PMC
- PMC7097845
- OpenAlex
- W3008294222
- Cited by
- 2,550
- References
- 33
- Citations by year
- 2026: 40 · 2025: 60 · 2024: 104 · 2023: 222 · 2022: 329 · 2021: 806 · 2020: 987 · 2019: 1
- FWCI
- 93.17
- Type
- journal article
- Language
- en
- Update notices
- erratum (2021-05-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Global Health (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- London School of Hygiene & Tropical Medicine (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by-nc-nd)
- reponame:Expeditio Repositorio Institucional UJTL
- Funders
- National Institute for Health and Care Research (16/137/109)
- Department of Health and Social Care (ITCRZ 03010)
- Research Councils UK
- Bill and Melinda Gates Foundation (HPRU-2012–10096, INV-003174)
- Wellcome Trust (206250/Z/17/Z, 210758/Z/18/Z, ES/P010873/1)
- Economic and Social Research Council (208812/Z/17/Z)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Keywords
- COVID-19 pandemic, SARS-CoV-2, contact tracing, case isolation, epidemic control, basic reproduction number, pre-symptomatic transmission, asymptomatic infection, imported infections, stochastic transmission model, epidemic modeling, non-pharmaceutical interventions
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Disease Outbreaks, Feasibility Studies, Humans, Patient Isolation, Pneumonia, Viral, Contact Tracing, Coronavirus, Coronavirus Infections
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