Lessons learned from the investigation of a COVID-19 cluster in Creil, France: effectiveness of targeting symptomatic cases and conducting contact tracing around them

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Abstract

Background

This study presents the methods and results of the investigation into a SARS-CoV-2 outbreak in a professional community. Due to the limited testing capacity available in France at the time, we elaborated a testing strategy according to pre-test probability.

Methods

The investigation design combined active case finding and contact tracing around each confirmed case with testing of at-risk contact persons who had any evocative symptoms ( n = 88). One month later, we performed serology testing to test and screen symptomatic and asymptomatic cases again ( n = 79).

Results

Twenty-four patients were confirmed (14 with RT-PCR and 10 with serology). The attack rate was 29% (24/83). Median age was 40 (24 to 59), and the sex ratio was 15/12. Only three cases were asymptomatic (= no symptoms at all, 13%, 95% CI, 3–32). Nineteen symptomatic cases (79%, 95% CI, 63–95) presented a respiratory infection, two of which were severe. All the RT-PCR confirmed cases acquired protective antibodies.

Median incubation was 4 days (from 1 to 13 days), and the median serial interval was 3 days (0 to 15). We identified pre-symptomatic transmission in 40% of this cluster, but no transmission from asymptomatic to symptomatic cases.

Conclusion

We report the effective use of targeted testing according to pre-test probability, specifically prioritizing symptomatic COVID-19 diagnosis and contact tracing. The asymptomatic rate raises questions about the real role of asymptomatic infected people in transmission. Conversely, pre-symptomatic contamination occurred frequently in this cluster, highlighting the need to identify, test, and quarantine asymptomatic at-risk contact persons (= contact tracing). The local lockdown imposed helped reduce transmission during the investigation period.

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Most cited references 29

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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Clinical Characteristics of Coronavirus Disease 2019 in China

Wei-jie Guan, Zheng-yi Ni, Yu Hu … (2020)

Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)

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Author and article information

Contributors

Franck de Laval: f_de_laval@hotmail.com

Journal

Journal ID (nlm-ta): BMC Infect Dis

Journal ID (iso-abbrev): BMC Infect Dis

Title: BMC Infectious Diseases

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2334

Publication date (Electronic): 19 May 2021

Publication date PMC-release: 19 May 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 457

Affiliations

[1 ]SSA (French Military Health Service), CESPA (French Armed Forces Center for Epidemiology and Public Health), Epidemiological Surveillance and Investigations Unit, BdD Marseille-Aubagne, 111 Avenue de la Corse, Marseille, 13568 France

[2 ]Aix-Marseille University, INSERM, IRD, SESSTIM (Economic and Social Sciences, Health Systems, and Medical Informatics), Marseille, France

[3 ]SSA (French Military Health Service), 1st Armed Forces Medical Center, Paris, France

[4 ]GRID grid.476258.a, SSA (French Military Health Service), , Armed Forces Biomedical Research Institute, ; Brétigny-sur-Orge, France

[5 ]GRID grid.484080.0, ISNI 0000 0001 0671 8206, Direction Générale de l’Armement, Maîtrise NRBC, ; Vert-le-Petit, France

[6 ]GRID grid.414007.6, ISNI 0000 0004 1798 6865, SSA (French Military Health Service), , Bégin Military Teaching Hospital, ; Saint-Mandé, France

[7 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, G5 Evolutionary genomics of RNA viruses, , Institut Pasteur, ; Paris, France

[8 ]National Reference Center for Respiratory Viruses, Molecular Genetics of RNA Viruses, Institut Pasteur, CNRS—UMR 3569, University of Paris, Paris, France

[9 ]GRID grid.428999.7, ISNI 0000 0001 2353 6535, Mutualized Platform of Microbiology, Pasteur International Bioresources Network, , Institut Pasteur, ; Paris, France

[10 ]Aix-Marseille University, IRD, AP-HM, SSA (French Military Health Service), VITROME, Marseille, France

[11 ]GRID grid.414005.4, ISNI 0000 0001 0029 7279, SSA (French Military Health Service), , Laveran Military Teaching Hospital, ; Marseille, France

Article

Publisher ID: 6166

DOI: 10.1186/s12879-021-06166-9

PMC ID: 8133048

PubMed ID: 34011278

SO-VID: dcdc56fd-4663-43a6-9e42-8e014a64e0d0

License:

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