Diagnostic serial interval as a novel indicator for contact tracing effectiveness exemplified with the SARS-CoV-2/COVID-19 outbreak in South Korea

https://doi.org/10.1016/j.ijid.2020.07.068Get rights and content
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Highlights

  • New diagnostic serial interval (DSI): time between diagnoses of infector & infectee.

  • DSI is defined regardless of symptoms and easy to obtain.

  • In South Korea, mean DSI was found to be short: 3.63 days (95% CI: 3.24, 4.01).

  • Mean DSI is potentially a new indicator for effectiveness of epidemic surveillance.

Abstract

Background

The clinical onset serial interval is often used as a proxy for the transmission interval of an infectious disease. For SARS-CoV-2/COVID-19, data on clinical onset serial intervals is limited, since symptom onset dates are not routinely recorded and do not exist in asymptomatic carriers.

Methods

We define the diagnostic serial interval as the time between the diagnosis dates of the infector and infectee. Based on the DS4C project data on SARS-CoV-2/COVID-19 in South Korea, we estimate the means of the diagnostic serial interval, the clinical onset serial interval, and the difference between the two. We use the balanced cluster bootstrap method to construct 95% bootstrap confidence intervals.

Results

The mean of the diagnostic serial interval was estimated to be 3.63 days (95% CI: 3.24, 4.01). The diagnostic serial interval was significantly shorter than the clinical onset serial interval (estimated mean difference −1.12 days, 95% CI: −1.98, −0.26).

Conclusions

The relatively short diagnostic serial intervals of SARS-CoV-2/COVID-19 in South Korea are likely due to the country’s extensive efforts towards contact tracing. We propose the mean diagnostic serial interval as a new indicator for the effectiveness of a country’s contact tracing as part of the epidemic surveillance.

Keywords

COVID-19
Serial interval
Diagnostic serial interval
Clinical onset serial interval
Surveillance
Contact tracing

Cited by (0)

1

Sofia Mettler is a Candidate of Medicine at the Faculty of Medicine of the University of Zurich and a Master’s Candidate in Statistics at ETH Zurich.

2

Jihoo Kim is the chief director of the DS4C project and a Master’s Candidate in Computer Science at Hanyang University.

3

Prof. Marloes Maathuis is a Professor of Statistics at ETH Zurich.