lancet-header

Preprints with The Lancet is part of SSRN´s First Look, a place where journals identify content of interest prior to publication. Authors have opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early stage research papers that have not been peer-reviewed. The findings should not be used for clinical or public health decision making and should not be presented to a lay audience without highlighting that they are preliminary and have not been peer-reviewed. For more information on this collaboration, see the comments published in The Lancet about the trial period, and our decision to make this a permanent offering, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com.

The Timing of COVID-19 Transmission

30 Pages Posted: 23 Oct 2020

See all articles by Luca Ferretti

Luca Ferretti

University of Oxford - Big Data Institute

Alice Ledda

Imperial College London - MRC Centre for Global Infectious Disease Analysis

Chris Wymant

University of Oxford - Big Data Institute

Lele Zhao

University of Oxford - Big Data Institute

Virginia Ledda

Liverpool University Hospitals NHS Foundation Trust (LUH)

Lucie Abeler- Dörner

University of Oxford - Big Data Institute

Michelle Kendall

University of Oxford - Big Data Institute

Anel Nurtay

University of Oxford - Big Data Institute

Hao-Yuan Cheng

Taiwan Centers for Disease Control - Epidemic Intelligence Center

Ta-Chou Ng

National Taiwan University - Institute of Epidemiology and Preventive Medicine

Hsien-Ho Lin

National Taiwan University - Institute of Epidemiology and Preventive Medicine

Rob Hinch

University of Oxford - Big Data Institute

Joanna Masel

University of Arizona - Ecology & Evolutionary Biology

A. Marm Kilpatrick

University of California, Santa Cruz - Ecology & Evolutionary Biology

Christophe Fraser

University of Oxford - Big Data Institute

More...

Abstract

Background: The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non-pharmaceutical interventions on the spread of COVID-19 epidemics. 

Methods: We examined the distribution of transmission event times with respect to exposure and onset of symptoms. We analysed 119 transmission pairs  with known date of onset of symptoms for both index and secondary cases and partial information on their intervals of exposure. We inferred the distribution for generation time and time from onset of symptoms to transmission by maximum likelihood. We modelled different relations between time of infection, onset of symptoms and transmission, inferring the most appropriate one according to the Akaike Information Criterion. Finally, we estimated the fraction of pre-symptomatic and early symptomatic transmissions among all pairs using a Bayesian approach.

Findings: For symptomatic individuals, the timing of transmission of SARS-CoV-2 was more directly linked to the onset of clinical symptoms of COVID-19 than to the time since infection.  The time of transmission was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. The pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41%; 95%CI 31-50%), but a comparably large fraction of transmissions occurred on the same day as the onset of symptoms or the next day (35%; 95%CI 26-45%). We caution against overinterpretation of the fraction and timing of late symptomatic transmissions, due to their dependence on behavioural factors and interventions. 

Interpretation: Infectiousness is causally driven by the onset of symptoms. Public health authorities should reassess their policies on the contact tracing window in the light of individual variability in presymptomatic infectious period. Information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced.  The large fraction of transmission from strictly pre-symptomatic infections limits the efficacy of symptom-based interventions, while the large fraction of early symptomatic transmissions underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if mild. Rapid or at-home testing and contextual risk information could greatly facilitate efficient early isolation.

Funding Statement: The study was funded by an award from the Li Ka Shing Foundation to CF.

Declaration of Interests: None of the authors have competing financial or non-financial interests.

Keywords: SARS-CoV-2Infectionpre-symptomaticearly symptomaticIncubation PeriodGeneration TimeSerial IntervalTime from Onset of Symptoms to Transmission (TOST)

Suggested Citation

Ferretti, Luca and Ledda, Alice and Wymant, Chris and Zhao, Lele and Ledda, Virginia and Abeler- Dörner, Lucie and Kendall, Michelle and Nurtay, Anel and Cheng, Hao-Yuan and Ng, Ta-Chou and Lin, Hsien-Ho and Hinch, Rob and Masel, Joanna and Kilpatrick, A. Marm and Fraser, Christophe, The Timing of COVID-19 Transmission. Available at SSRN: https://ssrn.com/abstract=3716879 or http://dx.doi.org/10.2139/ssrn.3716879

Luca Ferretti

University of Oxford - Big Data Institute

England
United Kingdom

Alice Ledda

Imperial College London - MRC Centre for Global Infectious Disease Analysis ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Chris Wymant

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Lele Zhao

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Virginia Ledda

Liverpool University Hospitals NHS Foundation Trust (LUH) ( email )

United Kingdom

Lucie Abeler- Dörner

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Michelle Kendall

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Anel Nurtay

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Hao-Yuan Cheng

Taiwan Centers for Disease Control - Epidemic Intelligence Center

No.6, Linsen S. Rd
Taipei City, 10050
Taiwan

Ta-Chou Ng

National Taiwan University - Institute of Epidemiology and Preventive Medicine ( email )

Taipei
Taiwan

Hsien-Ho Lin

National Taiwan University - Institute of Epidemiology and Preventive Medicine ( email )

Taipei
Taiwan

Rob Hinch

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Joanna Masel

University of Arizona - Ecology & Evolutionary Biology ( email )

Department of History
Tucson, AZ 85721
United States

A. Marm Kilpatrick

University of California, Santa Cruz - Ecology & Evolutionary Biology ( email )

1156 High St
Santa Cruz, CA 95064
United States

Christophe Fraser (Contact Author)

University of Oxford - Big Data Institute ( email )

England
United Kingdom

Click here to go to TheLancet.com

Paper statistics

Abstract Views
1,516
Downloads
239
PlumX Metrics