Literature DB >> 32353350

Impact of contact tracing on SARS-CoV-2 transmission.

Kaiyuan Sun1, Cécile Viboud2.   

Abstract

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Year:  2020        PMID: 32353350      PMCID: PMC7185949          DOI: 10.1016/S1473-3099(20)30357-1

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


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As the far-reaching impacts of the coronavirus disease 2019 (COVID-19) pandemic expand to more and more countries, key questions about transmission dynamics and optimal intervention strategies remain unanswered. In particular, the age profile of susceptibility and infectivity, the frequency of super-spreading events, the amount of transmission in the household, and the contribution of asymptomatic individuals to transmission remain debated. The study by Qifang Bi and colleagues in The Lancet Infectious Diseases explores some of these questions by analysing detailed contact tracing data from Shenzhen, a large and affluent city in southern China at the border with Hong Kong. To dissect the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, the authors modelled PCR-confirmed infections in 391 cases and 1286 of their close contacts from Jan 14 to Feb 12, 2020. Shenzhen is an interesting location to study the dynamics of SARS-CoV-2 because it was affected early in the pandemic and reacted quickly. Strict case isolation, contact tracing, and social distancing measures kept the transmission rate near the epidemic threshold throughout the study period. Bi and colleagues report that most secondary infections occurred in the household (77 of 81), with a secondary attack rate estimated at 11·2% (95% CI 9·1–13·8) among household contacts. This figure should be considered an underestimate of the unmitigated household attack rate of SARS-CoV-2, since transmission chains were cut short in Shenzhen because of strict control measures. Index cases detected by symptom-based surveillance were isolated outside of the home on average 4·6 days (95% CI 4·1–5·0) after symptom onset. Furthermore, individuals identified via contact tracing were isolated or quarantined outside of the home on average 2·7 days (95% CI 2·1–3·3) after symptom onset. Consequently, the serial interval of SARS-CoV-2 in Shenzhen (mean estimate 6·3 days; 95% CI 5·2–7·6) should be considered a lower bound and would probably increase in less successfully controlled outbreaks. The age profile of PCR-confirmed infections in Shenzhen indicates that children are as susceptible to SARS-CoV-2 infection as adults, although they are less likely to display symptoms. The distinctive age profile of COVID-19 severity has been noted very early on in the pandemic, although the biological mechanisms at play remain unclear. In the Shenzhen data, the authors noted no difference in the transmission potential of SARS-CoV-2 from children or adults. This is in contrast to pandemic influenza virus, which is more easily transmitted by children. It will be useful to confirm the age profile of SARS-CoV-2 transmissibility with data from other locations and serological surveys, which capture more infections than PCR. Age-specific susceptibility, infectivity, and severity are important factors to get right to project the impact of school closures on SARS-CoV-2 dynamics and disease burden. School closures exert a substantial economic toll on societies and maintaining these interventions for long periods of time requires robust supportive evidence. As would be expected from a well controlled outbreak, the mean R in Shenzhen was very low, at 0·4, substantially reduced from a baseline non-intervention value of 2·0–4·0. This aligns with the strict interventions implemented in this city. However, the mean R does not tell the full story. There is evidence of transmission heterogeneity with SARS-CoV-2, with 10% of cases accounting for 90% of transmission. Such a high level of heterogeneity is consistent with, if a little more extreme than, that of SARS-coronavirus (SARS-CoV), and more pronounced than for other directly transmitted respiratory viruses such as measles or influenza. Beyond the intensity of contacts, there is no clear factor in the Shenzhen data that could explain the high transmission potential of some infections. Further research into the biological (eg, shedding and symptoms) and social factors (eg, type of contacts and environment) that drive transmission heterogeneity is warranted to guide more targeted interventions against SARS-CoV-2. Armed with their descriptive findings, Bi and colleagues go on to simulate the impact of case isolation and contact tracing on SARS-CoV-2 dynamics. They consider a range of possible durations for the infectious period of SARS-CoV-2, which is reasonable given the scarcity of data on this figure. They show that for a given R, the longer the infectious period, the more easily the epidemic can be brought under control with case-based interventions. This is because case isolation reduces the full transmission potential of each case, particularly if the infectious period is long and cases can be isolated 2–5 days after symptom onset. Furthermore, Bi and colleagues show that contact-based interventions are more efficient than case-based interventions to reduce transmission, since infected contacts are typically isolated earlier in their infection history than index cases. This worthwhile modelling exercise highlights the urgent need for more information about the infectious period of SARS-CoV-2. However, there is an important caveat in this modelling work: the potential for pre-symptomatic and asymptomatic transmission is not considered. As a result, the conclusion that case-based or contact-based interventions alone could bring the epidemic under control for longer durations of the infectious period is optimistic, and contrasts with previous simulation studies. Viral shedding studies and epidemiological investigations suggest that in the household, around 40% of transmission occurs before symptom onset, the live virus is shed for at least 1 week after symptom onset, and there is high shedding in asymptomatic individuals.7, 8, 9 Crucially, the effectiveness of case isolation and contact tracing will depend on the fraction of transmission originating from asymptomatic and pre-symptomatic individuals. As we look towards post-lockdown strategies, we should examine the experience of countries that have successfully controlled SARS-CoV2 transmission or have low mortality (eg, China, Singapore, Taiwan, South Korea, Germany, and Iceland). Successful strategies include ample testing and contact tracing, supplemented by moderate forms of social distancing. Contact tracing on the scale that is needed for the SARS-CoV-2 response is labour intensive, and imperfect if done manually. Hence new technology-based approaches are greatly needed to assist in identification of contacts, especially if case detection is aggressive. Building on the SARS-CoV-2 experience in Shenzhen and other settings, we contend that enhanced case finding and contact tracing should be part of the long-term response to this pandemic—this can get us most of the way towards control.
  8 in total

1.  Temporal dynamics in viral shedding and transmissibility of COVID-19.

Authors:  Xi He; Eric H Y Lau; Peng Wu; Xilong Deng; Jian Wang; Xinxin Hao; Yiu Chung Lau; Jessica Y Wong; Yujuan Guan; Xinghua Tan; Xiaoneng Mo; Yanqing Chen; Baolin Liao; Weilie Chen; Fengyu Hu; Qing Zhang; Mingqiu Zhong; Yanrong Wu; Lingzhai Zhao; Fuchun Zhang; Benjamin J Cowling; Fang Li; Gabriel M Leung
Journal:  Nat Med       Date:  2020-04-15       Impact factor: 53.440

2.  Virological assessment of hospitalized patients with COVID-2019.

Authors:  Roman Wölfel; Victor M Corman; Wolfgang Guggemos; Michael Seilmaier; Sabine Zange; Marcel A Müller; Daniela Niemeyer; Terry C Jones; Patrick Vollmar; Camilla Rothe; Michael Hoelscher; Tobias Bleicker; Sebastian Brünink; Julia Schneider; Rosina Ehmann; Katrin Zwirglmaier; Christian Drosten; Clemens Wendtner
Journal:  Nature       Date:  2020-04-01       Impact factor: 49.962

3.  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.

Authors:  Kaiyuan Sun; Jenny Chen; Cécile Viboud
Journal:  Lancet Digit Health       Date:  2020-02-20

4.  Superspreading and the effect of individual variation on disease emergence.

Authors:  J O Lloyd-Smith; S J Schreiber; P E Kopp; W M Getz
Journal:  Nature       Date:  2005-11-17       Impact factor: 49.962

5.  Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study.

Authors:  Qifang Bi; Yongsheng Wu; Shujiang Mei; Chenfei Ye; Xuan Zou; Zhen Zhang; Xiaojian Liu; Lan Wei; Shaun A Truelove; Tong Zhang; Wei Gao; Cong Cheng; Xiujuan Tang; Xiaoliang Wu; Yu Wu; Binbin Sun; Suli Huang; Yu Sun; Juncen Zhang; Ting Ma; Justin Lessler; Tiejian Feng
Journal:  Lancet Infect Dis       Date:  2020-04-27       Impact factor: 25.071

6.  Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study.

Authors:  Juanjuan Zhang; Maria Litvinova; Wei Wang; Yan Wang; Xiaowei Deng; Xinghui Chen; Mei Li; Wen Zheng; Lan Yi; Xinhua Chen; Qianhui Wu; Yuxia Liang; Xiling Wang; Juan Yang; Kaiyuan Sun; Ira M Longini; M Elizabeth Halloran; Peng Wu; Benjamin J Cowling; Stefano Merler; Cecile Viboud; Alessandro Vespignani; Marco Ajelli; Hongjie Yu
Journal:  Lancet Infect Dis       Date:  2020-04-02       Impact factor: 25.071

7.  Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing.

Authors:  Luca Ferretti; Chris Wymant; David Bonsall; Christophe Fraser; Michelle Kendall; Lele Zhao; Anel Nurtay; Lucie Abeler-Dörner; Michael Parker
Journal:  Science       Date:  2020-03-31       Impact factor: 47.728

8.  Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility.

Authors:  Maimuna S Majumder; Kenneth D Mandl
Journal:  Lancet Glob Health       Date:  2020-03-24       Impact factor: 26.763

  8 in total
  19 in total

Review 1.  Artificial intelligence-based approaches for COVID-19 patient management.

Authors:  Lan Lan; Wenbo Sun; Dan Xu; Minhua Yu; Feng Xiao; Huijuan Hu; Haibo Xu; Xinghuan Wang
Journal:  Intell Med       Date:  2021-06-10

2.  A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities.

Authors:  Ling Yin; Hao Zhang; Yuan Li; Kang Liu; Tianmu Chen; Wei Luo; Shengjie Lai; Ye Li; Xiujuan Tang; Li Ning; Shengzhong Feng; Yanjie Wei; Zhiyuan Zhao; Ying Wen; Liang Mao; Shujiang Mei
Journal:  J R Soc Interface       Date:  2021-08-25       Impact factor: 4.118

Review 3.  Projecting the SARS-CoV-2 transition from pandemicity to endemicity: Epidemiological and immunological considerations.

Authors:  Lily E Cohen; David J Spiro; Cecile Viboud
Journal:  PLoS Pathog       Date:  2022-06-30       Impact factor: 7.464

4.  Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model.

Authors:  Bernd Brüggenjürgen; Hans-Peter Stricker; Lilian Krist; Miriam Ortiz; Thomas Reinhold; Stephanie Roll; Gabriele Rotter; Beate Weikert; Miriam Wiese-Posselt; Stefan N Willich
Journal:  Z Gesundh Wiss       Date:  2021-05-17

Review 5.  [Contact tracing in patients infected with SARS-CoV-2. The fundamental role of Primary Health Care and Public Health].

Authors:  J M Bellmunt; J A Caylà; J P Millet
Journal:  Semergen       Date:  2020-06-05

6.  The need for privacy with public digital contact tracing during the COVID-19 pandemic.

Authors:  Yoshua Bengio; Richard Janda; Yun William Yu; Daphne Ippolito; Max Jarvie; Dan Pilat; Brooke Struck; Sekoul Krastev; Abhinav Sharma
Journal:  Lancet Digit Health       Date:  2020-06-02

7.  Passing the Test: A model-based analysis of safe school-reopening strategies.

Authors:  Alyssa Bilinski; Joshua A Salomon; John Giardina; Andrea Ciaranello; Meagan C Fitzpatrick
Journal:  medRxiv       Date:  2021-01-29

Review 8.  Digitalization of contact tracing: balancing data privacy with public health benefit.

Authors:  Jeremy Wacksman
Journal:  Ethics Inf Technol       Date:  2021-06-10

9.  Severe Acute Respiratory Syndrome Coronavirus 2 Prevalence, Seroprevalence, and Exposure among Evacuees from Wuhan, China, 2020.

Authors:  Benjamin D Hallowell; Christina M Carlson; Jesica R Jacobs; Mary Pomeroy; Jonathan Steinberg; Mark W Tenforde; Emily McDonald; Loretta Foster; Leora R Feldstein; Melissa A Rolfes; Amber Haynes; Glen R Abedi; George S Odongo; Kim Saruwatari; Errin C Rider; Gina Douville; Neenaben Bhakta; Panagiotis Maniatis; Stephen Lindstrom; Natalie J Thornburg; Xiaoyan Lu; Brett L Whitaker; Shifaq Kamili; Senthilkumar K Sakthivel; Lijuan Wang; Lakshmi Malapati; Janna R Murray; Brian Lynch; Martin Cetron; Clive Brown; Shahrokh Roohi; Lisa Rotz; Denise Borntrager; Kenta Ishii; Kathleen Moser; Mohammad Rasheed; Brandi Freeman; Sandra Lester; Kizzmekia S Corbett; Olubukola M Abiona; Geoffrey B Hutchinson; Barney S Graham; Nicki Pesik; Barbara Mahon; Christopher Braden; Casey Barton Behravesh; Rebekah Stewart; Nancy Knight; Aron J Hall; Marie E Killerby
Journal:  Emerg Infect Dis       Date:  2020-07-03       Impact factor: 6.883

10.  Analysis of COVID-19 outbreak origin in China in 2019 using differentiation method for unusual epidemiological events.

Authors:  Vladan Radosavljevic
Journal:  Open Med (Wars)       Date:  2021-06-28
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