Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses
BackgroundThe need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1016169/full |
| _version_ | 1811160384082542592 |
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| author | Yangji Choi Anaïs Ladoy Anaïs Ladoy David De Ridder David De Ridder David De Ridder David De Ridder Damien Jacot Séverine Vuilleumier Claire Bertelli Idris Guessous Idris Guessous Idris Guessous Trestan Pillonel Stéphane Joost Stéphane Joost Stéphane Joost Gilbert Greub |
| author_facet | Yangji Choi Anaïs Ladoy Anaïs Ladoy David De Ridder David De Ridder David De Ridder David De Ridder Damien Jacot Séverine Vuilleumier Claire Bertelli Idris Guessous Idris Guessous Idris Guessous Trestan Pillonel Stéphane Joost Stéphane Joost Stéphane Joost Gilbert Greub |
| author_sort | Yangji Choi |
| collection | DOAJ |
| description | BackgroundThe need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection.MethodsFor this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes.ResultsOur results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency. |
| first_indexed | 2024-04-10T05:56:28Z |
| format | Article |
| id | doaj.art-b2b3106ec39b4551ae0705aee4f155b6 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-10T05:56:28Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-b2b3106ec39b4551ae0705aee4f155b62023-03-03T15:08:27ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-12-011010.3389/fpubh.2022.10161691016169Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analysesYangji Choi0Anaïs Ladoy1Anaïs Ladoy2David De Ridder3David De Ridder4David De Ridder5David De Ridder6Damien Jacot7Séverine Vuilleumier8Claire Bertelli9Idris Guessous10Idris Guessous11Idris Guessous12Trestan Pillonel13Stéphane Joost14Stéphane Joost15Stéphane Joost16Gilbert Greub17Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandGroup of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, SwitzerlandLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandGroup of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, SwitzerlandFaculty of Medicine, University of Geneva (UNIGE), Geneva, SwitzerlandDivision and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, SwitzerlandInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandLa Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, SwitzerlandInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandGroup of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, SwitzerlandFaculty of Medicine, University of Geneva (UNIGE), Geneva, SwitzerlandDivision and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, SwitzerlandInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandGroup of Geographic Information Research and Analysis in Population Health (GIRAPH), Geneva, SwitzerlandLa Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, SwitzerlandInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandBackgroundThe need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection.MethodsFor this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes.ResultsOur results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.https://www.frontiersin.org/articles/10.3389/fpubh.2022.1016169/fullSARS-CoV-2COVID-19epidemiologyspatiotemporal clustergenomicspublic health surveillance |
| spellingShingle | Yangji Choi Anaïs Ladoy Anaïs Ladoy David De Ridder David De Ridder David De Ridder David De Ridder Damien Jacot Séverine Vuilleumier Claire Bertelli Idris Guessous Idris Guessous Idris Guessous Trestan Pillonel Stéphane Joost Stéphane Joost Stéphane Joost Gilbert Greub Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses Frontiers in Public Health SARS-CoV-2 COVID-19 epidemiology spatiotemporal cluster genomics public health surveillance |
| title | Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses |
| title_full | Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses |
| title_fullStr | Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses |
| title_full_unstemmed | Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses |
| title_short | Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses |
| title_sort | detection of sars cov 2 infection clusters the useful combination of spatiotemporal clustering and genomic analyses |
| topic | SARS-CoV-2 COVID-19 epidemiology spatiotemporal cluster genomics public health surveillance |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1016169/full |
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