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Seller A, Hackenbruch C, Walz JS, Nelde A, Heitmann JS. Long-Term Follow-Up of COVID-19 Convalescents-Immune Response Associated with Reinfection Rate and Symptoms. Viruses 2023; 15:2100. [PMID: 37896879 PMCID: PMC10611319 DOI: 10.3390/v15102100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 10/29/2023] Open
Abstract
SARS-CoV-2 has spread worldwide, causing millions of deaths and leaving a significant proportion of people with long-term sequelae of COVID-19 ("post-COVID syndrome"). Whereas the precise mechanism of post-COVID syndrome is still unknown, the immune response after the first infection may play a role. Here, we performed a long-term follow-up analysis of 110 COVID-19 convalescents, analyzing the first SARS-CoV-2-directed immune response, vaccination status, long-term symptoms (approximately 2.5 years after first infection), and reinfections. A total of 96% of convalescents were vaccinated at least once against SARS-CoV-2 after their first infection. A reinfection rate of 47% was observed, and lower levels of anti-spike IgG antibodies after the first infection were shown to associate with reinfection. While T-cell responses could not be clearly associated with persistent postinfectious symptoms, convalescents with long-term symptoms showed elevated SARS-CoV-2-specific antibody levels at the first infection. Evaluating the immune response after the first infection might be a useful tool for identifying individuals with increased risk for re-infections and long-term symptoms.
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Affiliation(s)
- Anna Seller
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Department of Obstetrics and Gynecology, University Hospital Tuebingen, Calwerstraße 7, 72076 Tuebingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
| | - Christopher Hackenbruch
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
| | - Juliane S. Walz
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tuebingen, Germany
| | - Annika Nelde
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tuebingen, Germany
| | - Jonas S. Heitmann
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tuebingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tuebingen, Germany
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van Tonder AJ, McCullagh F, McKeand H, Thaw S, Bellis K, Raisen C, Lay L, Aggarwal D, Holmes M, Parkhill J, Harrison EM, Kucharski A, Conlan A. Colonization and transmission of Staphylococcus aureus in schools: a citizen science project. Microb Genom 2023; 9:mgen000993. [PMID: 37074324 PMCID: PMC10210949 DOI: 10.1099/mgen.0.000993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/22/2023] [Indexed: 04/20/2023] Open
Abstract
Aggregation of children in schools has been established to be a key driver of transmission of infectious diseases. Mathematical models of transmission used to predict the impact of control measures, such as vaccination and testing, commonly depend on self-reported contact data. However, the link between self-reported social contacts and pathogen transmission has not been well described. To address this, we used Staphylococcus aureus as a model organism to track transmission within two secondary schools in England and test for associations between self-reported social contacts, test positivity and the bacterial strain collected from the same students. Students filled out a social contact survey and their S. aureus colonization status was ascertained through self-administered swabs from which isolates were sequenced. Isolates from the local community were also sequenced to assess the representativeness of school isolates. A low frequency of genome-linked transmission precluded a formal analysis of links between genomic and social networks, suggesting that S. aureus transmission within schools is too rare to make it a viable tool for this purpose. Whilst we found no evidence that schools are an important route of transmission, increased colonization rates found within schools imply that school-age children may be an important source of community transmission.
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Affiliation(s)
| | | | | | - Sue Thaw
- St Bede's Inter-Church School, Cambridge, UK
| | - Katie Bellis
- Wellcome Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Claire Raisen
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Liz Lay
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Dinesh Aggarwal
- Wellcome Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Mark Holmes
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Ewan M. Harrison
- Wellcome Sanger Institute, Hinxton, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Adam Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Andrew Conlan
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
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3
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Olawoye IB, Oluniyi PE, Oguzie JU, Uwanibe JN, Kayode TA, Olumade TJ, Ajogbasile FV, Parker E, Eromon PE, Abechi P, Sobajo TA, Ugwu CA, George UE, Ayoade F, Akano K, Oyejide NE, Nosamiefan I, Fred-Akintunwa I, Adedotun-Sulaiman K, Brimmo FB, Adegboyega BB, Philip C, Adeleke RA, Chukwu GC, Ahmed MI, Ope-Ewe OO, Otitoola SG, Ogunsanya OA, Saibu MF, Sijuwola AE, Ezekiel GO, John OG, Akin-John JO, Akinlo OO, Fayemi OO, Ipaye TO, Nwodo DC, Omoniyi AE, Omwanghe IB, Terkuma CA, Okolie J, Ayo-Ale O, Ikponmwosa O, Benevolence E, Naregose GO, Patience AE, Blessing O, Micheal A, Jacqueline A, Aiyepada JO, Ebhodaghe P, Racheal O, Rita E, Rosemary GE, Solomon E, Anieno E, Edna Y, Chris AO, Donatus AI, Ogbaini-Emovon E, Tatfeng MY, Omunakwe HE, Bob-Manuel M, Ahmed RA, Onwuamah CK, Shaibu JO, Okwuraiwe A, Ataga AE, Bock-Oruma A, Daramola F, Yusuf IF, Fajola A, Ntia NA, Ekpo JJ, Moses AE, Moore-Igwe BW, Fakayode OE, Akinola M, Kida IM, Oderinde BS, Wudiri ZW, Adeyemi OO, Akanbi OA, Ahumibe A, Akinpelu A, Ayansola O, Babatunde O, Omoare AA, Chukwu C, Mba NG, Omoruyi EC, Olisa O, Akande OK, Nwafor IE, Ekeh MA, Ndoma E, Ewah RL, Duruihuoma RO, Abu A, Odeh E, Onyia V, Ojide CK, Okoro S, Igwe D, Ogah EO, Khan K, Ajayi NA, Ugwu CN, Ukwaja KN, Ugwu NI, Abejegah C, Adedosu N, Ayodeji O, Liasu AA, Isamotu RO, Gadzama G, Petros BA, Siddle KJ, Schaffner SF, Akpede G, Erameh CO, Baba MM, Oladiji F, Audu R, Ndodo N, Fowotade A, Okogbenin S, Okokhere PO, Park DJ, Mcannis BL, Adetifa IM, Ihekweazu C, Salako BL, Tomori O, Happi AN, Folarin OA, Andersen KG, Sabeti PC, Happi CT. Emergence and spread of two SARS-CoV-2 variants of interest in Nigeria. Nat Commun 2023; 14:811. [PMID: 36781860 PMCID: PMC9924892 DOI: 10.1038/s41467-023-36449-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the SARS-CoV-2 B.1.1.318 and B.1.525 (Eta) variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave in Nigeria emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Data from this study show how regional connectivity of Nigeria drove the spread of these variants of interest to surrounding countries and those connected by air-traffic. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission, as bidirectional transmission within and between African nations are grossly underestimated as seen in our import risk index estimates.
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Affiliation(s)
- Idowu B Olawoye
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Paul E Oluniyi
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Judith U Oguzie
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Jessica N Uwanibe
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Tolulope A Kayode
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Testimony J Olumade
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Fehintola V Ajogbasile
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Edyth Parker
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Philomena E Eromon
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Priscilla Abechi
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Tope A Sobajo
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Chinedu A Ugwu
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Uwem E George
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Femi Ayoade
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Kazeem Akano
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Nicholas E Oyejide
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Iguosadolo Nosamiefan
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Iyanuoluwa Fred-Akintunwa
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Kemi Adedotun-Sulaiman
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Farida B Brimmo
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Babatunde B Adegboyega
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Courage Philip
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Richard A Adeleke
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Grace C Chukwu
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Muhammad I Ahmed
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Oludayo O Ope-Ewe
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Shobi G Otitoola
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Olusola A Ogunsanya
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Mudasiru F Saibu
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Ayotunde E Sijuwola
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Grace O Ezekiel
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Oluwagboadurami G John
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Julie O Akin-John
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Oluwasemilogo O Akinlo
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Olanrewaju O Fayemi
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Testimony O Ipaye
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Deborah C Nwodo
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Abolade E Omoniyi
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Iyobosa B Omwanghe
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Christabel A Terkuma
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Johnson Okolie
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Olubukola Ayo-Ale
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Odia Ikponmwosa
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Ebo Benevolence
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | | | - Osiemi Blessing
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Airende Micheal
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | - John O Aiyepada
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | - Omiunu Racheal
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Esumeh Rita
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Giwa E Rosemary
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | - Ekanem Anieno
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Yerumoh Edna
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | - Aire O Chris
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | | | - Mirabeau Y Tatfeng
- Department of Medical Laboratory Science, Niger Delta University, Amassoma, Bayelsa State, Nigeria
| | - Hannah E Omunakwe
- Satellite Molecular Laboratory, Rivers State University Teaching Hospital, Port Harcourt, Rivers State, Nigeria
| | - Mienye Bob-Manuel
- Satellite Molecular Laboratory, Rivers State University Teaching Hospital, Port Harcourt, Rivers State, Nigeria
| | - Rahaman A Ahmed
- The Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Chika K Onwuamah
- The Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Joseph O Shaibu
- The Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Azuka Okwuraiwe
- The Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | - Anthony E Ataga
- Molecular Laboratory, Regional Centre for Biotechnology and Bioresources Research, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria
| | | | - Funmi Daramola
- Clinical Health, SPDC, Port Harcourt, Rivers State, Nigeria
| | | | - Akinwumi Fajola
- Regional Community Health, SPDC, Port Harcourt, Rivers State, Nigeria
| | | | - Julie J Ekpo
- Department of Medical Microbiology and Parasitology, University of Uyo, Uyo, Akwa Ibom State, Nigeria
| | - Anietie E Moses
- Department of Medical Microbiology and Parasitology, University of Uyo, Uyo, Akwa Ibom State, Nigeria
| | | | | | - Monilade Akinola
- WHO Polio Laboratory, University of Maiduguri Teaching Hospital, Maiduguri, Borno State, Nigeria
| | - Ibrahim M Kida
- Department of Immunology, University of Maiduguri Teaching Hospital, Maiduguri, Nigeria
| | - Bamidele S Oderinde
- Department of Immunology, University of Maiduguri Teaching Hospital, Maiduguri, Nigeria
| | - Zara W Wudiri
- Department of Community Medicine, University of Maiduguri Teaching Hospital, Maiduguri, Borno State, Nigeria
| | - Oluwapelumi O Adeyemi
- Department of Medical Microbiology and Parasitology. Faculty of Basic Clinical Sciences. College of Health Sciences, University of Ilorin, Ilorin, Kwara State, Nigeria
| | | | | | | | | | | | | | | | - Nwando G Mba
- Nigeria Centre for Disease Control, Abuja, Nigeria
| | - Ewean C Omoruyi
- Medical Microbiology and Parasitology Department, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Olasunkanmi Olisa
- Biorepository Clinical Virology Laboratory, University of Ibadan, Ibadan, Nigeria
| | - Olatunji K Akande
- Biorepository Clinical Virology Laboratory, University of Ibadan, Ibadan, Nigeria
| | - Ifeanyi E Nwafor
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Matthew A Ekeh
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Erim Ndoma
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Richard L Ewah
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Rosemary O Duruihuoma
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Augustine Abu
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Elizabeth Odeh
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Venatius Onyia
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Chiedozie K Ojide
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | - Sylvanus Okoro
- Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Daniel Igwe
- Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Emeka O Ogah
- Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Kamran Khan
- Department of Medicine, University of Toronto, Toronto, Canada
- BlueDot, Toronto, Canada
| | - Nnennaya A Ajayi
- Internal Medicine Department, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Collins N Ugwu
- Internal Medicine Department, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Kingsley N Ukwaja
- Internal Medicine Department, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | - Ngozi I Ugwu
- Haematology Department, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
| | | | | | | | | | | | - Galadima Gadzama
- Department of Medical Microbiology, University of Maiduguri Teaching Hospital, Maiduguri, Borno State, Nigeria
| | | | | | | | - George Akpede
- Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria
| | | | - Marycelin M Baba
- WHO Polio Laboratory, University of Maiduguri Teaching Hospital, Maiduguri, Borno State, Nigeria
- Department of Immunology, University of Maiduguri Teaching Hospital, Maiduguri, Nigeria
| | - Femi Oladiji
- Department of Epidemiology and Community Health, Faculty of Clinical Sciences, College of Health Sciences, University of Ilorin, Ilorin, Nigeria
| | - Rosemary Audu
- The Nigerian Institute of Medical Research, Yaba, Lagos State, Nigeria
| | | | - Adeola Fowotade
- Virology Laboratory, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi State, Nigeria
| | | | | | - Danny J Park
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | | | | | | | - Oyewale Tomori
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Anise N Happi
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Onikepe A Folarin
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria
| | - Kristian G Andersen
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Pardis C Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Christian T Happi
- Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Osun State, Nigeria.
- African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria.
- Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.
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4
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Chen X, Yan X, Sun K, Zheng N, Sun R, Zhou J, Deng X, Zhuang T, Cai J, Zhang J, Ajelli M, Yu H. Estimation of disease burden and clinical severity of COVID-19 caused by Omicron BA.2 in Shanghai, February-June 2022. Emerg Microbes Infect 2022; 11:2800-2807. [PMID: 36205530 DOI: 10.1080/22221751.2022.2128435] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
An outbreak of COVID-19 caused by the SARS-CoV-2 Omicron BA.2 sublineage occurred in Shanghai, China from February 26 to June 30, 2022. We use official reported data retrieved from Shanghai municipal Health Commissions to estimate the incidence of infections, severe/critical infections, and deaths to assess the disease burden. By adjusting for right censoring and RT PCR sensitivity, we provide estimates of clinical severity, including the infection fatality ratio, symptomatic case fatality ratio, and risk of developing severe/critical disease upon infection. The overall infection rate, severe/critical infection rate, and mortality rate were 2.74 (95% CI: 2.73-2.74) per 100 individuals, 6.34 (95% CI: 6.02-6.66) per 100,000 individuals and 2.42 (95% CI: 2.23-2.62) per 100,000 individuals, respectively. The severe/critical infection rate and mortality rate increased with age, noted in individuals aged 80 years or older. The overall fatality ratio and risk of developing severe/critical disease upon infection were 0.09% (95% CI: 0.09-0.10%) and 0.27% (95% CI: 0.24-0.29%), respectively. Having received at least one vaccine dose led to a 10-fold reduction in the risk of death for infected individuals aged 80 years or older. Under the repeated population-based screenings and strict intervention policies implemented in Shanghai, our results found a lower disease burden and mortality of the outbreak compared to other settings and countries, showing the impact of the successful outbreak containment in Shanghai. The estimated low clinical severity of this Omicron BA.2 epidemic in Shanghai highlight the key contribution of vaccination and availability of hospital beds to reduce the risk of death.
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Affiliation(s)
- Xinhua Chen
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Nan Zheng
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jiaxin Zhou
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaowei Deng
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tingyu Zhuang
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jun Cai
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juanjuan Zhang
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Hongjie Yu
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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Bingham J, Tempia S, Moultrie H, Viboud C, Jassat W, Cohen C, Pulliam JRC. Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.22.22277932. [PMID: 35982666 PMCID: PMC9387150 DOI: 10.1101/2022.07.22.22277932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Objectives We aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers. Methods We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data. Results Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. Discussion Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.
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Affiliation(s)
- Jeremy Bingham
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Stefano Tempia
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Harry Moultrie
- Centre for Tuberculosis, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cecile Viboud
- Fogarty International Center, NIH, Bethesda, MD, USA
| | - Waasila Jassat
- Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa
- Right to Care, Pretoria, South Africa
| | - Cheryl Cohen
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Juliet R C Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
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6
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Chen Z, Deng X, Fang L, Sun K, Wu Y, Che T, Zou J, Cai J, Liu H, Wang Y, Wang T, Tian Y, Zheng N, Yan X, Sun R, Xu X, Zhou X, Ge S, Liang Y, Yi L, Yang J, Zhang J, Ajelli M, Yu H. Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.06.11.22276273. [PMID: 35765564 PMCID: PMC9238184 DOI: 10.1101/2022.06.11.22276273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background In early March 2022, a major outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant spread rapidly throughout Shanghai, China. Here we aimed to provide a description of the epidemiological characteristics and spatiotemporal transmission dynamics of the Omicron outbreak under the population-based screening and lockdown policies implemented in Shanghai. Methods We extracted individual information on SARS-CoV-2 infections reported between January 1 and May 31, 2022, and on the timeline of the adopted non-pharmacological interventions. The epidemic was divided into three phases: i) sporadic infections (January 1-February 28), ii) local transmission (March 1-March 31), and iii) city-wide lockdown (April 1 to May 31). We described the epidemic spread during these three phases and the subdistrict-level spatiotemporal distribution of the infections. To evaluate the impact on the transmission of SARS-CoV-2 of the adopted targeted interventions in Phase 2 and city-wide lockdown in Phase 3, we estimated the dynamics of the net reproduction number ( R t ). Findings A surge in imported infections in Phase 1 triggered cryptic local transmission of the Omicron variant in early March, resulting in the largest coronavirus disease 2019 (COVID-19) outbreak in mainland China since the original wave. A total of 626,000 SARS-CoV-2 infections were reported in 99.5% (215/216) of the subdistricts of Shanghai. The spatial distribution of the infections was highly heterogeneous, with 40% of the subdistricts accounting for 80% of all infections. A clear trend from the city center towards adjacent suburban and rural areas was observed, with a progressive slowdown of the epidemic spread (from 544 to 325 meters/day) prior to the citywide lockdown. During Phase 2, R t remained well above 1 despite the implementation of multiple targeted interventions. The citywide lockdown imposed on April 1 led to a marked decrease in transmission, bringing R t below the epidemic threshold in the entire city on April 14 and ultimately leading to containment of the outbreak. Interpretation Our results highlight the risk of widespread outbreaks in mainland China, particularly under the heightened pressure of imported infections. The targeted interventions adopted in March 2022 were not capable of halting transmission, and the implementation of a strict, prolonged city-wide lockdown was needed to successfully contain the outbreak, highlighting the challenges for successfully containing Omicron outbreaks. Funding Key Program of the National Natural Science Foundation of China (82130093). Research in context Evidence before this study: On May 24, 2022, we searched PubMed and Europe PMC for papers published or posted on preprint servers after January 1, 2022, using the following query: ("SARS-CoV-2" OR "Omicron" OR "BA.2") AND ("epidemiology" OR "epidemiological" OR "transmission dynamics") AND ("Shanghai"). A total of 26 studies were identified; among them, two aimed to describe or project the spread of the 2022 Omicron outbreak in Shanghai. One preprint described the epidemiological and clinical characteristics of 376 pediatric SARS-CoV-2 infections in March 2022, and the other preprint projected the epidemic progress in Shanghai, without providing an analysis of field data. In sum, none of these studies provided a comprehensive description of the epidemiological characteristics and spatiotemporal transmission dynamics of the outbreak.Added value of this study: We collected individual information on SARS-CoV-2 infection and the timeline of the public health response. Population-based screenings were repeatedly implemented during the outbreak, which allowed us to investigate the spatiotemporal spread of the Omicron BA.2 variant as well as the impact of the implemented interventions, all without enduring significant amounts of underreporting from surveillance systems, as experienced in other areas. This study provides the first comprehensive assessment of the Omicron outbreak in Shanghai, China.Implications of all the available evidence: This descriptive study provides a comprehensive understanding of the epidemiological features and transmission dynamics of the Omicron outbreak in Shanghai, China. The empirical evidence from Shanghai, which was ultimately able to curtail the outbreak, provides invaluable information to policymakers on the impact of the containment strategies adopted by the Shanghai public health officials to prepare for potential outbreaks caused by Omicron or novel variants.
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaowei Deng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Liqun Fang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Yanpeng Wu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tianle Che
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Junyi Zou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jun Cai
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Tao Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Yuyang Tian
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Nan Zheng
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuemei Yan
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Ruijia Sun
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaoyu Zhou
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Shijia Ge
- Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxiang Liang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Lan Yi
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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7
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Lambarey H, Blumenthal MJ, Chetram A, Joyimbana W, Jennings L, Tincho MB, Burgers WA, Orrell C, Schäfer G. SARS-CoV-2 Infection Is Associated with Uncontrolled HIV Viral Load in Non-Hospitalized HIV-Infected Patients from Gugulethu, South Africa. Viruses 2022; 14:v14061222. [PMID: 35746693 PMCID: PMC9229655 DOI: 10.3390/v14061222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 12/04/2022] Open
Abstract
In South Africa, high exposure to SARS-CoV-2 occurs primarily in densely populated, low-income communities, which are additionally burdened by highly prevalent Human Immunodeficiency Virus (HIV). With the aim to assess SARS-CoV-2 seroprevalence and its association with HIV-related clinical parameters in non-hospitalized patients likely to be highly exposed to SARS-CoV-2, this observational cross-sectional study was conducted at the Gugulethu Community Health Centre Antiretroviral clinic between October 2020 and June 2021, after the first COVID-19 wave in South Africa and during the second and beginning of the third wave. A total of 150 adult (median age 39 years [range 20−65 years]) HIV-infected patients (69% female; 31% male) were recruited. 95.3% of the cohort was on antiretroviral therapy (ART), had a median CD4 count of 220 cells/µL (range 17−604 cells/µL) and a median HIV viral load (VL) of 49 copies/mL (range 1−1,050,867 copies/mL). Furthermore, 106 patients (70.7%) were SARS-CoV-2 seropositive, and 0% were vaccinated. When stratified for HIV VL, patients with uncontrolled HIV viremia (HIV VL > 1000 copies/mL) had significantly higher odds of SARS-CoV-2 seropositivity than patients with HIV VL < 1000 copies/mL, after adjusting for age, sex and ART status (p = 0.035, adjusted OR 2.961 [95% CI: 1.078−8.133]). Although the cause−effect relationship could not be determined due to the cross-sectional study design, these results point towards a higher risk of SARS-CoV-2 susceptibility among viremic HIV patients, or impaired HIV viral control due to previous co-infection with SARS-CoV-2.
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Affiliation(s)
- Humaira Lambarey
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town 7925, South Africa; (H.L.); (M.J.B.); (A.C.)
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Department of Integrative Biomedical Sciences, Division of Medical Biochemistry, University of Cape Town, Cape Town 7925, South Africa
| | - Melissa J. Blumenthal
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town 7925, South Africa; (H.L.); (M.J.B.); (A.C.)
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Department of Integrative Biomedical Sciences, Division of Medical Biochemistry, University of Cape Town, Cape Town 7925, South Africa
| | - Abeen Chetram
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town 7925, South Africa; (H.L.); (M.J.B.); (A.C.)
| | - Wendy Joyimbana
- Desmond Tutu Health Foundation, Cape Town 7925, South Africa; (W.J.); (L.J.)
| | - Lauren Jennings
- Desmond Tutu Health Foundation, Cape Town 7925, South Africa; (W.J.); (L.J.)
| | - Marius B. Tincho
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Department of Pathology, Division of Medical Virology, University of Cape Town, Cape Town 7925, South Africa
| | - Wendy A. Burgers
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Department of Pathology, Division of Medical Virology, University of Cape Town, Cape Town 7925, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town 7925, South Africa
| | - Catherine Orrell
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Desmond Tutu Health Foundation, Cape Town 7925, South Africa; (W.J.); (L.J.)
| | - Georgia Schäfer
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town 7925, South Africa; (H.L.); (M.J.B.); (A.C.)
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town 7925, South Africa; (M.B.T.); (W.A.B.); (C.O.)
- Department of Integrative Biomedical Sciences, Division of Medical Biochemistry, University of Cape Town, Cape Town 7925, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town 7925, South Africa
- Correspondence: ; Tel.: +27-21-404-7688
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8
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Pulliam JRC, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science 2022. [PMID: 35289632 DOI: 10.1101/2021.11.11.21266068] [Citation(s) in RCA: 175] [Impact Index Per Article: 87.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.
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Affiliation(s)
- Juliet R C Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Cari van Schalkwyk
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Nevashan Govender
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Anne von Gottberg
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michelle J Groome
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jonathan Dushoff
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- McMaster University, Hamilton, Ontario, Canada
| | - Koleka Mlisana
- National Health Laboratory Service, Johannesburg, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Harry Moultrie
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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9
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Pulliam JRC, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science 2022. [PMID: 35289632 DOI: 10.5281/zenodo.6108448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.
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Affiliation(s)
- Juliet R C Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Cari van Schalkwyk
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Nevashan Govender
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Anne von Gottberg
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michelle J Groome
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jonathan Dushoff
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- McMaster University, Hamilton, Ontario, Canada
| | - Koleka Mlisana
- National Health Laboratory Service, Johannesburg, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Harry Moultrie
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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10
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Pulliam JRC, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science 2022; 376:eabn4947. [PMID: 35289632 PMCID: PMC8995029 DOI: 10.1126/science.abn4947] [Citation(s) in RCA: 481] [Impact Index Per Article: 240.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/09/2022] [Indexed: 12/12/2022]
Abstract
We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.
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Affiliation(s)
- Juliet R. C. Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Cari van Schalkwyk
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Nevashan Govender
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Anne von Gottberg
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michelle J. Groome
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jonathan Dushoff
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- McMaster University, Hamilton, Ontario, Canada
| | - Koleka Mlisana
- National Health Laboratory Service, Johannesburg, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Harry Moultrie
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Pulliam JRC, van Schalkwyk C, Govender N, von Gottberg A, Cohen C, Groome MJ, Dushoff J, Mlisana K, Moultrie H. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science 2022. [PMID: 35289632 DOI: 10.5281/zenodo.5807591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.
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Affiliation(s)
- Juliet R C Pulliam
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Cari van Schalkwyk
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Nevashan Govender
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Anne von Gottberg
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Cheryl Cohen
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michelle J Groome
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Jonathan Dushoff
- South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- McMaster University, Hamilton, Ontario, Canada
| | - Koleka Mlisana
- National Health Laboratory Service, Johannesburg, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Harry Moultrie
- National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
- School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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12
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Racine É, Boivin G, Longtin Y, McCormack D, Decaluwe H, Savard P, Cheng MP, Hamelin MÈ, Carbonneau J, Tadount F, Adams K, Bourdin B, Nantel S, Gilca V, Corbeil J, De Serres G, Quach-Thanh C. The REinfection in COVID-19 Estimation of Risk (RECOVER) study: Reinfection and serology dynamics in a cohort of Canadian healthcare workers. Influenza Other Respir Viruses 2022; 16:916-925. [PMID: 35510653 PMCID: PMC9343327 DOI: 10.1111/irv.12997] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022] Open
Abstract
Background Understanding the immune response to natural infection by SARS‐CoV‐2 is key to pandemic management, especially in the current context of emerging variants. Uncertainty remains regarding the efficacy and duration of natural immunity against reinfection. Methods We conducted an observational prospective cohort study in Canadian healthcare workers (HCWs) with a history of PCR‐confirmed SARS‐CoV‐2 infection to (i) measure the average incidence rate of reinfection and (ii) describe the serological immune response to the primary infection. Results Our cohort comprised 569 HCWs; median duration of individual follow‐up was 371 days. We detected six cases of reinfection in absence of vaccination between August 21, 2020, and March 1, 2022, for a reinfection incidence rate of 4.0 per 100 person‐years. Median duration of seropositivity was 415 days in symptomatics at primary infection compared with 213 days in asymptomatics (p < 0.0001). Other characteristics associated with prolonged seropositivity for IgG against the spike protein included age over 55 years, obesity, and non‐Caucasian ethnicity. Conclusions Among unvaccinated healthcare workers, reinfection with SARS‐CoV‐2 following a primary infection remained rare. SARS‐CoV‐2 reinfections remained rare events among unvaccinated healthcare workers. Prior natural infection confers some protection against reinfection and clinical disease, but waning of serum antibodies suggests this protection may not last in the long term.
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Affiliation(s)
- Étienne Racine
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.,Sainte-Justine Hospital Health and Research Center, Montreal, Quebec, Canada
| | - Guy Boivin
- Department of Microbiology-Immunology and Infectious Diseases, Laval University, Quebec City, Quebec, Canada.,Infectious and Immune Diseases Axis, Research Center of the Centre Hospitalier de l'Université Laval, Quebec City, Quebec, Canada
| | - Yves Longtin
- Jewish General Hospital and Lady Davis Research Institute, Montreal, Quebec, Canada
| | | | - Hélène Decaluwe
- Immune Diseases and Cancer Axis, Sainte-Justine Hospital University Health and Research Center, Montreal, Quebec, Canada.,Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
| | - Patrice Savard
- Department of Microbiology, Infectious Diseases and Immunology, University of Montreal, Montreal, Quebec, Canada.,Immunopathology Axis, Research Center of the Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.,Infectious Disease Service, Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Matthew P Cheng
- Divisions of Infectious Diseases and Medical Microbiology, McGill University Health Center, McGill University, Montreal, Quebec, Canada.,McGill Interdisciplinary Initiative in Infection and Immunity, Montreal, Quebec, Canada
| | - Marie-Ève Hamelin
- Infectious and Immune Diseases Axis, Research Center of the Centre Hospitalier de l'Université Laval, Quebec City, Quebec, Canada
| | - Julie Carbonneau
- Infectious and Immune Diseases Axis, Research Center of the Centre Hospitalier de l'Université Laval, Quebec City, Quebec, Canada
| | - Fazia Tadount
- Sainte-Justine Hospital Health and Research Center, Montreal, Quebec, Canada
| | - Kelsey Adams
- Sainte-Justine Hospital Health and Research Center, Montreal, Quebec, Canada
| | - Benoîte Bourdin
- Immune Diseases and Cancer Axis, Sainte-Justine Hospital University Health and Research Center, Montreal, Quebec, Canada
| | - Sabryna Nantel
- Immune Diseases and Cancer Axis, Sainte-Justine Hospital University Health and Research Center, Montreal, Quebec, Canada.,Department of Microbiology, Infectiology and Immunology, University of Montreal, Montreal, Quebec, Canada
| | - Vladimir Gilca
- Quebec National Public Health Institute, Quebec City, Quebec, Canada
| | - Jacques Corbeil
- Department of Molecular Medicine, Big Data Research Center, Institute Intelligence and Data, Laval University, Quebec City, QC, Canada.,Infectiology Research Center of the Centre Hospitalier Universitaire de Québec, Quebec City, QC, Canada
| | - Gaston De Serres
- Quebec National Public Health Institute, Quebec City, Quebec, Canada.,Department of Social and Preventive Medicine, Laval University, Quebec City, Quebec, Canada
| | - Caroline Quach-Thanh
- Department of Microbiology, Infectious Diseases, and Immunology, University of Montreal, Montreal, Quebec, Canada.,Sainte-Justine Hospital University Health and Research Center, Montreal, Quebec, Canada
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13
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Madewell ZJ, Yang Y, Longini IM, Halloran ME, Dean NE. Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status: An Updated Systematic Review and Meta-analysis. JAMA Netw Open 2022; 5:e229317. [PMID: 35482308 PMCID: PMC9051991 DOI: 10.1001/jamanetworkopen.2022.9317] [Citation(s) in RCA: 117] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/10/2022] [Indexed: 12/25/2022] Open
Abstract
Importance An overall household secondary attack rate (SAR) of 18.9% (95% CI, 16.2%-22.0%) through June 17, 2021 was previously reported for SARS-CoV-2. Emerging variants of concern and increased vaccination have affected transmission rates. Objective To evaluate how reported household SARs changed over time and whether SARs varied by viral variant and index case and contact vaccination status. Data Sources PubMed and medRxiv from June 18, 2021, through March 8, 2022, and reference lists of eligible articles. Preprints were included. Study Selection Articles with original data reporting the number of infected and total number of household contacts. Search terms included SARS-CoV-2, COVID-19, variant, vaccination, secondary attack rate, secondary infection rate, household, index case, family contacts, close contacts, and family transmission. Data Extraction and Synthesis The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline was followed. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95% CIs. Main Outcomes and Measures SAR stratified by covariates according to variant, index case and contact vaccination status, and index case identification period. SARs were used to estimate vaccine effectiveness on the basis of the transmission probability for susceptibility to infection (VES,p), infectiousness given infection (VEI,p), and total vaccine effectiveness (VET,p). Results Household SARs were higher for 33 studies with midpoints in 2021 to 2022 (37.3%; 95% CI, 32.7% to 42.1%) compared with 63 studies with midpoints through April 2020 (15.5%; 95% CI, 13.2% to 18.2%). Household SARs were 42.7% (95% CI, 35.4% to 50.4%) for Omicron (7 studies), 36.4% (95% CI, 33.4% to 39.5%) for Alpha (11 studies), 29.7% (95% CI, 23.0% to 37.3%) for Delta (16 studies), and 22.5% (95% CI, 18.6% to 26.8%) for Beta (3 studies). For full vaccination, VES,p was 78.6% (95% CI, 76.0% to 80.9%) for Alpha, 56.4% (95% CI, 54.6% to 58.1%) for Delta, and 18.1% (95% CI, -18.3% to 43.3%) for Omicron; VEI,p was 75.3% (95% CI, 69.9% to 79.8%) for Alpha, 21.9% (95% CI, 11.0% to 31.5%) for Delta, and 18.2% (95% CI, 0.6% to 32.6%) for Omicron; and VET,p was 94.7% (95% CI, 93.3% to 95.8%) for Alpha, 64.4% (95% CI, 58.0% to 69.8%) for Delta, and 35.8% (95% CI, 13.0% to 52.6%) for Omicron. Conclusions and Relevance These results suggest that emerging SARS-CoV-2 variants of concern have increased transmissibility. Full vaccination was associated with reductions in susceptibility and infectiousness, but more so for Alpha than Delta and Omicron. The changes in estimated vaccine effectiveness underscore the challenges of developing effective vaccines concomitant with viral evolution.
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Affiliation(s)
| | - Yang Yang
- Department of Biostatistics, University of Florida, Gainesville
| | - Ira M. Longini
- Department of Biostatistics, University of Florida, Gainesville
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle
| | - Natalie E. Dean
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia
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14
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Madewell ZJ, Yang Y, Longini IM, Halloran ME, Dean NE. Household secondary attack rates of SARS-CoV-2 by variant and vaccination status: an updated systematic review and meta-analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.01.09.22268984. [PMID: 35043125 PMCID: PMC8764734 DOI: 10.1101/2022.01.09.22268984] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We previously reported a household secondary attack rate (SAR) for SARS-CoV-2 of 18.9% through June 17, 2021. To examine how emerging variants and increased vaccination have affected transmission rates, we searched PubMed from June 18, 2021, through January 7, 2022. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95%CI, disaggregated by several covariates. SARs were used to estimate vaccine effectiveness based on the transmission probability for susceptibility ( VE S,p ), infectiousness ( VE I,p ), and total vaccine effectiveness ( VE T,p ). Household SAR for 27 studies with midpoints in 2021 was 35.8% (95%CI, 30.6%-41.3%), compared to 15.7% (95%CI, 13.3%-18.4%) for 62 studies with midpoints through April 2020. Household SARs were 38.0% (95%CI, 36.0%-40.0%), 30.8% (95%CI, 23.5%-39.3%), and 22.5% (95%CI, 18.6%-26.8%) for Alpha, Delta, and Beta, respectively. VE I,p , VE S,p , and VE T,p were 56.6% (95%CI, 28.7%-73.6%), 70.3% (95%CI, 59.3%-78.4%), and 86.8% (95%CI, 76.7%-92.5%) for full vaccination, and 27.5% (95%CI, -6.4%-50.7%), 43.9% (95%CI, 21.8%-59.7%), and 59.9% (95%CI, 34.4%-75.5%) for partial vaccination, respectively. Household contacts exposed to Alpha or Delta are at increased risk of infection compared to the original wild-type strain. Vaccination reduced susceptibility to infection and transmission to others. SUMMARY Household secondary attack rates (SARs) were higher for Alpha and Delta variants than previous estimates. SARs were higher to unvaccinated contacts than to partially or fully vaccinated contacts and were higher from unvaccinated index cases than from fully vaccinated index cases.
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Affiliation(s)
| | - Yang Yang
- Department of Biostatistics, University of Florida, Gainesville, FL
| | - Ira M. Longini
- Department of Biostatistics, University of Florida, Gainesville, FL
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Natalie E. Dean
- Department of Biostatistics, University of Florida, Gainesville, FL
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15
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Akinbami LJ, Biggerstaff BJ, Chan PA, McGibbon E, Pathela P, Petersen LR. Reinfection with SARS-CoV-2 among previously infected healthcare personnel and first responders. Clin Infect Dis 2021; 75:e201-e207. [PMID: 34791108 PMCID: PMC8767877 DOI: 10.1093/cid/ciab952] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Indexed: 12/22/2022] Open
Abstract
Background SARS-CoV-2 virus testing among first responders and healthcare personnel who participated in a May-August 2020 serosurvey which assessed spike protein antibodies (S1 region) provided an opportunity to assess reinfection. Methods Serology survey data were merged with virus testing results from Rhode Island (March 1, 2020-February 17, 2021) and New York City (March 10-December 14, 2020). Participants with a positive virus test ≥14 days before their serology test were included. Reinfection was defined as a second positive SARS-CoV-2 test result ≥90 days after the first positive test. The association between serostatus and reinfection was assessed with a proportional hazards model adjusting for demographics, exposures, and virus testing frequency. Results Among 1,572 previously infected persons, 40 (2.5%) were reinfected. Reinfection differed by serostatus: 8.4% among seronegative versus 1.9% among seropositive participants (p<0.0001). Most reinfections occurred among Rhode Island nursing home and corrections (RINHC) personnel (n=30) who were most frequently tested (mean 30.3 tests versus 4.6 for other Rhode Island and 2.3 for New York City participants). The adjusted hazard ratio (aHR) for reinfection in seropositive versus seronegative persons was 0.41 (95% CI 0.20, 0.81). Exposure to a household member with COVID-19 before the serosurvey was also protective (aHR 0.34, 95% CI 0.13, 0.89). Conclusions Reinfections were uncommon among previously infected persons over a 9-month period that preceded widespread variant circulation. Seropositivity decreased reinfection risk. Lower reinfection risk associated with exposure to a household member with COVID-19 before the serosurvey may reflect subsequently reduced household transmission among members of previously infected households.
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Affiliation(s)
- Lara J Akinbami
- National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA.,U.S. Public Health Service, Rockville, Maryland, USA
| | - Brad J Biggerstaff
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, USA
| | - Philip A Chan
- Rhode Island Department of Health, Providence, Rhode Island, USA
| | - Emily McGibbon
- New York City Department of Health and Mental Hygiene, Queens, New York, USA
| | - Preeti Pathela
- New York City Department of Health and Mental Hygiene, Queens, New York, USA
| | - Lyle R Petersen
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, USA
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