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Russell TW, Townsley H, Hellewell J, Gahir J, Shawe-Taylor M, Greenwood D, Hodgson D, Hobbs A, Dowgier G, Penn R, Sanderson T, Stevenson-Leggett P, Bazire J, Harvey R, Fowler AS, Miah M, Smith C, Miranda M, Bawumia P, Mears HV, Adams L, Hatipoglu E, O'Reilly N, Warchal S, Ambrose K, Strange A, Kelly G, Kjar S, Papineni P, Corrah T, Gilson R, Libri V, Kassiotis G, Gamblin S, Lewis NS, Williams B, Swanton C, Gandhi S, Beale R, Wu MY, Bauer DLV, Carr EJ, Wall EC, Kucharski AJ. Real-time estimation of immunological responses against emerging SARS-CoV-2 variants in the UK: a mathematical modelling study. THE LANCET. INFECTIOUS DISEASES 2024:S1473-3099(24)00484-5. [PMID: 39276782 DOI: 10.1016/s1473-3099(24)00484-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/12/2024] [Accepted: 07/16/2024] [Indexed: 09/17/2024]
Abstract
BACKGROUND The emergence of SARS-CoV-2 variants and COVID-19 vaccination have resulted in complex exposure histories. Rapid assessment of the effects of these exposures on neutralising antibodies against SARS-CoV-2 infection is crucial for informing vaccine strategy and epidemic management. We aimed to investigate heterogeneity in individual-level and population-level antibody kinetics to emerging variants by previous SARS-CoV-2 exposure history, to examine implications for real-time estimation, and to examine the effects of vaccine-campaign timing. METHODS Our Bayesian hierarchical model of antibody kinetics estimated neutralising-antibody trajectories against a panel of SARS-CoV-2 variants quantified with a live virus microneutralisation assay and informed by individual-level COVID-19 vaccination and SARS-CoV-2 infection histories. Antibody titre trajectories were modelled with a piecewise linear function that depended on the key biological quantities of an initial titre value, time the peak titre is reached, set-point time, and corresponding rates of increase and decrease for gradients between two timing parameters. All process parameters were estimated at both the individual level and the population level. We analysed data from participants in the University College London Hospitals-Francis Crick Institute Legacy study cohort (NCT04750356) who underwent surveillance for SARS-CoV-2 either through asymptomatic mandatory occupational health screening once per week between April 1, 2020, and May 31, 2022, or symptom-based testing between April 1, 2020, and Feb 1, 2023. People included in the Legacy study were either Crick employees or health-care workers at three London hospitals, older than 18 years, and gave written informed consent. Legacy excluded people who were unable or unwilling to give informed consent and those not employed by a qualifying institution. We segmented data to include vaccination events occurring up to 150 days before the emergence of three variants of concern: delta, BA.2, and XBB 1.5. We split the data for each wave into two categories: real-time and retrospective. The real-time dataset contained neutralising-antibody titres collected up to the date of emergence in each wave; the retrospective dataset contained all samples until the next SARS-CoV-2 exposure of each individual, whether vaccination or infection. FINDINGS We included data from 335 participants in the delta wave analysis, 223 (67%) of whom were female and 112 (33%) of whom were male (median age 40 years, IQR 22-58); data from 385 participants in the BA.2 wave analysis, 271 (70%) of whom were female and 114 (30%) of whom were male (41 years, 22-60); and data from 248 participants in the XBB 1.5 wave analysis, 191 (77%) of whom were female, 56 (23%) of whom were male, and one (<1%) of whom preferred not to say (40 years, 21-59). Overall, we included 968 exposures (vaccinations) across 1895 serum samples in the model. For the delta wave, we estimated peak titre values as 490·0 IC50 (95% credible interval 224·3-1515·9) for people with no previous infection and as 702·4 IC50 (300·8-2322·7) for people with a previous infection before omicron; the delta wave did not include people with a previous omicron infection. For the BA.2 wave, we estimated peak titre values as 858·1 IC50 (689·8-1363·2) for people with no previous infection, 1020·7 IC50 (725·9-1722·6) for people with a previous infection before omicron, and 1422·0 IC50 (679·2-3027·3) for people with a previous omicron infection. For the XBB 1.5 wave, we estimated peak titre values as 703·2 IC50 (415·0-3197·8) for people with no previous infection, 1215·9 IC50 (511·6-7338·7) for people with a previous infection before omicron, and 1556·3 IC50 (757·2-7907·9) for people with a previous omicron infection. INTERPRETATION Our study shows the feasibility of real-time estimation of antibody kinetics before SARS-CoV-2 variant emergence. This estimation is valuable for understanding how specific combinations of SARS-CoV-2 exposures influence antibody kinetics and for examining how COVID-19 vaccination-campaign timing could affect population-level immunity to emerging variants. FUNDING Wellcome Trust, National Institute for Health Research University College London Hospitals Biomedical Research Centre, UK Research and Innovation, UK Medical Research Council, Francis Crick Institute, and Genotype-to-Phenotype National Virology Consortium.
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Affiliation(s)
- Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
| | - Hermaleigh Townsley
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | - Joel Hellewell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Joshua Gahir
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | - Marianne Shawe-Taylor
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | | | - David Hodgson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Agnieszka Hobbs
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | - Giulia Dowgier
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | | | | | - Phoebe Stevenson-Leggett
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | - James Bazire
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | | | | | | | | | | | | | | | | | - Emine Hatipoglu
- Cancer Immunology Unit, Research Department of Haematology, University College London, London, UK
| | | | | | | | | | | | | | - Padmasayee Papineni
- Department of Infectious Diseases, London Northwest University Healthcare NHS Trust, London, UK
| | - Tumena Corrah
- Department of Infectious Diseases, London Northwest University Healthcare NHS Trust, London, UK
| | - Richard Gilson
- Mortimer Market Centre, Central and North West London NHS Trust, London, UK
| | - Vincenzo Libri
- National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK; Cancer Immunology Unit, Research Department of Haematology, University College London, London, UK
| | - George Kassiotis
- Francis Crick Institute, London, UK; Department of Infectious Disease, St Mary's Hospital, Imperial College London, London, UK
| | | | | | - Bryan Williams
- National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK
| | - Charles Swanton
- Francis Crick Institute, London, UK; Cancer Immunology Unit, Research Department of Haematology, University College London, London, UK
| | - Sonia Gandhi
- Francis Crick Institute, London, UK; Cancer Immunology Unit, Research Department of Haematology, University College London, London, UK
| | | | | | | | - Edward J Carr
- Francis Crick Institute, London, UK; Centre for Kidney and Bladder Health, Division of Medicine, University College London, London, UK
| | - Emma C Wall
- Francis Crick Institute, London, UK; National Institute for Health Research Biomedical Research Centre and Clinical Research Facility, University College London Hospitals NHS Foundation Trust, London, UK; Research Department of Infection, University College London, London, UK
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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2
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Lees JA, Russell TW, Shaw LP, Hellewell J. Recent approaches in computational modelling for controlling pathogen threats. Life Sci Alliance 2024; 7:e202402666. [PMID: 38906676 PMCID: PMC11192964 DOI: 10.26508/lsa.202402666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/23/2024] Open
Abstract
In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.
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Affiliation(s)
- John A Lees
- https://ror.org/02catss52 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Timothy W Russell
- https://ror.org/00a0jsq62 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Liam P Shaw
- Department of Biology, University of Oxford, Oxford, UK
- Department of Biosciences, University of Durham, Durham, UK
| | - Joel Hellewell
- https://ror.org/02catss52 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
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3
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Forna A, Weedop KB, Damodaran L, Hassell N, Kondor R, Bahl J, Drake JM, Rohani P. Sequence-based detection of emerging antigenically novel influenza A viruses. Proc Biol Sci 2024; 291:20240790. [PMID: 39140324 PMCID: PMC11323087 DOI: 10.1098/rspb.2024.0790] [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: 10/04/2023] [Revised: 05/21/2024] [Accepted: 07/11/2024] [Indexed: 08/15/2024] Open
Abstract
The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.
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Affiliation(s)
- Alpha Forna
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - K. Bodie Weedop
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
| | - Lambodhar Damodaran
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - Norman Hassell
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Rebecca Kondor
- Centers for Disease Control and Prevention, Atlanta, GA30329, USA
| | - Justin Bahl
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA30606, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA30602, USA
- Center for Influenza Disease & Emergence Research (CIDER), Athens, GA30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA30602, USA
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Edler P, Schwab LSU, Aban M, Wille M, Spirason N, Deng YM, Carlock MA, Ross TM, Juno JA, Rockman S, Wheatley AK, Kent SJ, Barr IG, Price DJ, Koutsakos M. Immune imprinting in early life shapes cross-reactivity to influenza B virus haemagglutinin. Nat Microbiol 2024; 9:2073-2083. [PMID: 38890491 DOI: 10.1038/s41564-024-01732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
Influenza exposures early in life are believed to shape future susceptibility to influenza infections by imprinting immunological biases that affect cross-reactivity to future influenza viruses. However, direct serological evidence linked to susceptibility is limited. Here we analysed haemagglutination-inhibition titres in 1,451 cross-sectional samples collected between 1992 and 2020, from individuals born between 1917 and 2008, against influenza B virus (IBV) isolates from 1940 to 2021. We included testing of 'future' isolates that circulated after sample collection. We show that immunological biases are conferred by early life IBV infection and result in lineage-specific cross-reactivity of a birth cohort towards future IBV isolates. This translates into differential estimates of susceptibility between birth cohorts towards the B/Yamagata and B/Victoria lineages, predicting lineage-specific birth-cohort distributions of observed medically attended IBV infections. Our data suggest that immunological measurements of imprinting could be important in modelling and predicting virus epidemiology.
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Affiliation(s)
- Peta Edler
- Department of Infectious Diseases, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Lara S U Schwab
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Malet Aban
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Michelle Wille
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Centre for Pathogen Genomics, University of Melbourne, Melbourne, Victoria, Australia
| | - Natalie Spirason
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Yi-Mo Deng
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Michael A Carlock
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA, USA
- Florida Research and Innovation Centre, Cleveland Clinic, Port Saint Lucie, FL, USA
| | - Ted M Ross
- Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA, USA
- Florida Research and Innovation Centre, Cleveland Clinic, Port Saint Lucie, FL, USA
- Department of Infection Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jennifer A Juno
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Steve Rockman
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Vaccine Product Development, CSL Seqirus Ltd, Parkville, Victoria, Australia
| | - Adam K Wheatley
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Stephen J Kent
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Melbourne Sexual Health Centre and Department of Infectious Diseases, Alfred Health, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Ian G Barr
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - David J Price
- Department of Infectious Diseases, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Centre for Epidemiology & Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Marios Koutsakos
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia.
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5
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Kim K, Vieira MC, Gouma S, Weirick ME, Hensley SE, Cobey S. Measures of population immunity can predict the dominant clade of influenza A (H3N2) in the 2017-2018 season and reveal age-associated differences in susceptibility and antibody-binding specificity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.26.23297569. [PMID: 37961288 PMCID: PMC10635207 DOI: 10.1101/2023.10.26.23297569] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared to others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectionalantibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season. Methods We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains using Focus Reduction Neutralization Tests (FNRT) and Enzyme-Linked Lectin Assays (ELLA). We estimated relative population-average and age-specific susceptibilities to circulating viral clades and compared those estimates to changes in clade frequencies in the following 2017-18 season. Results The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titers to different HA and NA clades varied dramatically between individuals but showed significant associations with age, suggesting dependence on correlated past exposures. Conclusions This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.
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Affiliation(s)
- Kangchon Kim
- Department of Ecology and Evolution, The University of Chicago, USA
| | - Marcos C. Vieira
- Department of Ecology and Evolution, The University of Chicago, USA
| | - Sigrid Gouma
- Department of Microbiology, Perelman School of Medicine, The University of Pennsylvania, USA
| | - Madison E. Weirick
- Department of Microbiology, Perelman School of Medicine, The University of Pennsylvania, USA
| | - Scott E. Hensley
- Department of Microbiology, Perelman School of Medicine, The University of Pennsylvania, USA
| | - Sarah Cobey
- Department of Ecology and Evolution, The University of Chicago, USA
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6
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Hay JA, Zhu H, Jiang CQ, Kwok KO, Shen R, Kucharski A, Yang B, Read JM, Lessler J, Cummings DAT, Riley S. Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.18.24304371. [PMID: 38562868 PMCID: PMC10984066 DOI: 10.1101/2024.03.18.24304371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual's antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual's antibody profile. Finally, we reconstructed each individual's expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.
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Affiliation(s)
- James A. Hay
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
| | - Huachen Zhu
- Guangdong-Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou University, Shantou, China
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- 5EKIH (Gewuzhikang) Pathogen Research Institute, Guangdong, China
| | | | - Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ruiyin Shen
- Guangzhou No.12 Hospital, Guangzhou, Guangdong, China
| | - Adam Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, United Kingdom
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jonathan M. Read
- Centre for Health Informatics Computing and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, United States
- UNC Carolina Population Center, Chapel Hill, United States
| | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Imperial College London
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7
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Roberts MG, Hickson RI, McCaw JM. How immune dynamics shape multi-season epidemics: a continuous-discrete model in one dimensional antigenic space. J Math Biol 2024; 88:48. [PMID: 38538962 PMCID: PMC10973021 DOI: 10.1007/s00285-024-02076-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/25/2024] [Accepted: 03/05/2024] [Indexed: 04/01/2024]
Abstract
We extend a previously published model for the dynamics of a single strain of an influenza-like infection. The model incorporates a waning acquired immunity to infection and punctuated antigenic drift of the virus, employing a set of coupled integral equations within a season and a discrete map between seasons. The long term behaviour of the model is demonstrated by examples where immunity to infection depends on the time since a host was last infected, and where immunity depends on the number of times that a host has been infected. The first scenario leads to complicated dynamics in some regions of parameter space, and to regions of parameter space with more than one attractor. The second scenario leads to a stable fixed point, corresponding to an identical epidemic each season. We also examine the model with both paradigms in combination, almost always but not exclusively observing a stable fixed point or periodic solution. Adding stochastic perturbations to the between season map fails to destroy the model's qualitative dynamics. Our results suggest that if the level of host immunity depends on the elapsed time since the last infection then the epidemiological dynamics may be unpredictable.
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Affiliation(s)
- M G Roberts
- New Zealand Institute for Advanced Study and the Infectious Disease Research Centre, Massey University, Auckland, New Zealand.
| | - R I Hickson
- Health and Biosecurity, CSIRO, Townsville, QLD, 4814, Australia
- Australian Institute of Tropical Medicine and Hygiene, and College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, 4814, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - J M McCaw
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia
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8
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Gardner BJ, Kilpatrick AM. Predicting Vaccine Effectiveness for Hospitalization and Symptomatic Disease for Novel SARS-CoV-2 Variants Using Neutralizing Antibody Titers. Viruses 2024; 16:479. [PMID: 38543844 PMCID: PMC10975673 DOI: 10.3390/v16030479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 05/23/2024] Open
Abstract
The emergence of new virus variants, including the Omicron variant (B.1.1.529) of SARS-CoV-2, can lead to reduced vaccine effectiveness (VE) and the need for new vaccines or vaccine doses if the extent of immune evasion is severe. Neutralizing antibody titers have been shown to be a correlate of protection for SARS-CoV-2 and other pathogens, and could be used to quickly estimate vaccine effectiveness for new variants. However, no model currently exists to provide precise VE estimates for a new variant against severe disease for SARS-CoV-2 using robust datasets from several populations. We developed predictive models for VE against COVID-19 symptomatic disease and hospitalization across a 54-fold range of mean neutralizing antibody titers. For two mRNA vaccines (mRNA-1273, BNT162b2), models fit without Omicron data predicted that infection with the BA.1 Omicron variant increased the risk of hospitalization 2.8-4.4-fold and increased the risk of symptomatic disease 1.7-4.2-fold compared to the Delta variant. Out-of-sample validation showed that model predictions were accurate; all predictions were within 10% of observed VE estimates and fell within the model prediction intervals. Predictive models using neutralizing antibody titers can provide rapid VE estimates, which can inform vaccine booster timing, vaccine design, and vaccine selection for new virus variants.
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Affiliation(s)
- Billy J. Gardner
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA
| | - A. Marm Kilpatrick
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95060, USA
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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [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] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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10
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Han AX, de Jong SPJ, Russell CA. Co-evolution of immunity and seasonal influenza viruses. Nat Rev Microbiol 2023; 21:805-817. [PMID: 37532870 DOI: 10.1038/s41579-023-00945-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2023] [Indexed: 08/04/2023]
Abstract
Seasonal influenza viruses cause recurring global epidemics by continually evolving to escape host immunity. The viral constraints and host immune responses that limit and drive the evolution of these viruses are increasingly well understood. However, it remains unclear how most of these advances improve the capacity to reduce the impact of seasonal influenza viruses on human health. In this Review, we synthesize recent progress made in understanding the interplay between the evolution of immunity induced by previous infections or vaccination and the evolution of seasonal influenza viruses driven by the heterogeneous accumulation of antibody-mediated immunity in humans. We discuss the functional constraints that limit the evolution of the viruses, the within-host evolutionary processes that drive the emergence of new virus variants, as well as current and prospective options for influenza virus control, including the viral and immunological barriers that must be overcome to improve the effectiveness of vaccines and antiviral drugs.
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Affiliation(s)
- Alvin X Han
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Simon P J de Jong
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Colin A Russell
- Department of Medical Microbiology & Infection Prevention, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
- Department of Global Health, School of Public Health, Boston University, Boston, MA, USA.
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11
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Coyne D, Butler D, Meehan A, Keogh E, Williams P, Carterson A, Hervig T, O'Flaherty N, Waters A. The changing profile of SARS-CoV-2 serology in Irish blood donors. GLOBAL EPIDEMIOLOGY 2023; 5:100108. [PMID: 37122774 PMCID: PMC10121150 DOI: 10.1016/j.gloepi.2023.100108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 05/02/2023] Open
Abstract
Background The present study aimed to investigate the progression of the SARS-CoV-2 pandemic in Ireland over the first three waves of infection. Method A selection of blood donor serum samples collected between February 2020 and December 2021 were analysed by various commercially available serological assays for antibodies to SARS-CoV-2 (n = 15,066). Results An increase in seropositivity was observed between wave 1 (February to September 2020) and wave 2 (November and December 2020) of 2.20% to 3.55%. A large increase in estimated seroprevalence to 11.89% was observed in samples collected in February and March 2021 (wave 3 of infection).The rate of seropositivity varied by age group, with the highest rate observed in the youngest donors (18-29 years) peaking at 18.79% in wave 3. The results of spike antibody (anti-S) testing indicated that 44/1009 (4.36%) of seroreactive donors in wave 3 had a serological profile consistent with vaccination. By November 2021, we detected an overall seropositivity of 97.04%. Conclusions The present study provides a comprehensive estimation of the level of circulating SARS-CoV-2 antibodies in Irish blood donors, enabling differentiation between vaccination and natural infection, as well as real-time monitoring of the progression of the COVID-19 pandemic in Ireland. Seroepidemiology has a role in determining reliable estimates of transmission, infection fatality rates and vaccine uptake. The continued screening of blood donors for this purpose has the potential to generate important data to assist with the management of future waves of SARS-CoV-2.
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Affiliation(s)
- Dermot Coyne
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
| | - Dearbhla Butler
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
| | - Adrienne Meehan
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
| | - Evan Keogh
- Centre for Laboratory Medicine and Molecular Pathology, St James's Hospital, James's Street, Dublin D08 NHY1, Ireland
| | - Pádraig Williams
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
| | - Alex Carterson
- Abbott Laboratories, 100 Abbott Park Road, Abbott Park, IL 60064, USA
| | - Tor Hervig
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
| | - Niamh O'Flaherty
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
- UCD National Virus Reference Laboratory, University College Dublin, Dublin 4, Ireland
| | - Allison Waters
- Irish Blood Transfusion Service, National Blood Centre, James's Street, Dublin D08 NH5R, Ireland
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin 4, Ireland
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12
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Einav T, Ma R. Using interpretable machine learning to extend heterogeneous antibody-virus datasets. CELL REPORTS METHODS 2023; 3:100540. [PMID: 37671020 PMCID: PMC10475791 DOI: 10.1016/j.crmeth.2023.100540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/14/2023] [Accepted: 06/30/2023] [Indexed: 09/07/2023]
Abstract
A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."
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Affiliation(s)
- Tal Einav
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
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13
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Menezes A, Takahashi S, Routledge I, Metcalf CJE, Graham AL, Hay JA. serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes. PLoS Comput Biol 2023; 19:e1011384. [PMID: 37578985 PMCID: PMC10449138 DOI: 10.1371/journal.pcbi.1011384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 08/24/2023] [Accepted: 07/24/2023] [Indexed: 08/16/2023] Open
Abstract
serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.
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Affiliation(s)
- Arthur Menezes
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Saki Takahashi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Isobel Routledge
- Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
| | - Andrea L. Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - James A. Hay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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14
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Yang B, García-Carreras B, Lessler J, Read JM, Zhu H, Metcalf CJE, Hay JA, Kwok KO, Shen R, Jiang CQ, Guan Y, Riley S, Cummings DA. Long term intrinsic cycling in human life course antibody responses to influenza A(H3N2): an observational and modeling study. eLife 2022; 11:81457. [PMID: 36458815 PMCID: PMC9757834 DOI: 10.7554/elife.81457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/01/2022] [Indexed: 12/05/2022] Open
Abstract
Background Over a life course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime. Methods To investigate the cyclic behavior, we analyzed hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China, and applied Fourier spectrum analysis. To investigate possible biological mechanisms, we simulated individual antibody profiles encompassing known feedbacks and interactions due to generally recognized immunological mechanisms. Results We demonstrated a long-term periodicity (about 24 years) in individual antibody responses. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explains the reported cycle. We showed that the reported cycles are predictable at both individual and birth cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of seroconversion to circulating strains, after accounting for age and pre-existing titers of the circulating strains. Conclusions Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by preexisting antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen-specific responses over time until individual's increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy. Funding This study was supported by grants from the NIH R56AG048075 (DATC, JL), NIH R01AI114703 (DATC, BY), the Wellcome Trust 200861/Z/16/Z (SR), and 200187/Z/15/Z (SR). This work was also supported by research grants from Guangdong Government HZQB-KCZYZ-2021014 and 2019B121205009 (YG and HZ). DATC, JMR and SR acknowledge support from the National Institutes of Health Fogarty Institute (R01TW0008246). JMR acknowledges support from the Medical Research Council (MR/S004793/1) and the Engineering and Physical Sciences Research Council (EP/N014499/1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Affiliation(s)
- Bingyi Yang
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
| | - Bernardo García-Carreras
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public HealthBaltimoreUnited States
- Department of Epidemiology, UNC Gillings School of Global Public HealthChapel HillUnited States
- UNC Carolina Population CenterChapel HillUnited States
| | - Jonathan M Read
- Centre for Health Informatics Computing and Statistics, Lancaster UniversityLancasterUnited Kingdom
| | - Huachen Zhu
- Guangdong‐Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou UniversityShantouChina
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
- EKIH (Gewuzhikang) Pathogen Research InstituteGuangdongChina
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton UniversityPrincetonUnited States
| | - James A Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College LondonLondonUnited Kingdom
- Center for Communicable Disease Dynamics, Harvard TH Chan School of Public HealthBostonUnited States
| | - Kin O Kwok
- The Jockey Club School of Public Health and Primary Care, Chinese University of Hong KongHong KongChina
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong KongHong KongChina
- Shenzhen Research Institute of The Chinese University of Hong KongGuangdongChina
| | - Ruiyun Shen
- Guangzhou No.12 Hospital, GuangzhouGuangdongChina
| | - Chao Q Jiang
- Guangzhou No.12 Hospital, GuangzhouGuangdongChina
| | - Yi Guan
- Guangdong‐Hong Kong Joint Laboratory of Emerging Infectious Diseases/MOE Joint Laboratory for International Collaboration in Virology and Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou UniversityShantouChina
- State Key Laboratory of Emerging Infectious Diseases / World Health Organization Influenza Reference Laboratory, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong KongHong KongChina
- EKIH (Gewuzhikang) Pathogen Research InstituteGuangdongChina
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College LondonLondonUnited Kingdom
| | - Derek A Cummings
- Department of Biology, University of FloridaGainesvilleUnited States
- Emerging Pathogens Institute, University of FloridaGainesvilleUnited States
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15
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Ashraf M, Rajaram S, English PM. How the COVID 19 pandemic will shape influenza public health initiatives: The UK experience. Hum Vaccin Immunother 2022; 18:2056399. [PMID: 35435806 PMCID: PMC9255027 DOI: 10.1080/21645515.2022.2056399] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/18/2022] [Indexed: 12/23/2022] Open
Abstract
Although caused by different pathogens, COVID-19 and influenza share many clinical features, as well as the potential for inflammatory, cardiovascular, and other long-term complications. During the 2020-2021 influenza season, COVID-19 mitigation efforts and a robust influenza vaccination campaign led to an unprecedented reduction in influenza cases. The lack of exposure to influenza, along with antigenic changes, may have reduced population immunity to influenza and set the stage for a high severity influenza season in 2021-2022. For the second consecutive season, the UK Department of Health and Social Care has expanded influenza vaccine eligibility to mitigate the impact of both COVID-19 and influenza. Continuation of clear policy decisions, as well as ongoing coordination between manufacturers, distributors, health authorities, and healthcare providers, is key to reducing the burden of influenza and COVID-19 and preventing large numbers of severe cases that can overwhelm the healthcare system.
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16
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Ertesvåg NU, Cox RJ, Lartey SL, Mohn KGI, Brokstad KA, Trieu MC. Seasonal influenza vaccination expands hemagglutinin-specific antibody breadth to older and future A/H3N2 viruses. NPJ Vaccines 2022; 7:67. [PMID: 35750781 PMCID: PMC9232600 DOI: 10.1038/s41541-022-00490-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 05/13/2022] [Indexed: 11/09/2022] Open
Abstract
History of influenza A/H3N2 exposure, especially childhood infection, shape antibody responses after influenza vaccination and infection, but have not been extensively studied. We investigated the breadth and durability of influenza A/H3N2-specific hemagglutinin-inhibition antibodies after live-attenuated influenza vaccine in children (aged 3-17 years, n = 42), and after inactivated influenza vaccine or infection in adults (aged 22-61 years, n = 42) using 14 antigenically distinct A/H3N2 viruses circulating from 1968 to 2018. We found that vaccination and infection elicited cross-reactive antibody responses, predominantly directed against newer or future strains. Childhood H3-priming increased the breadth and magnitude of back-boosted A/H3N2-specific antibodies in adults. Broader and more durable A/H3N2-specific antibodies were observed in repeatedly vaccinated adults than in children and previously unvaccinated adults. Our findings suggest that early A/H3N2 exposure and frequent seasonal vaccination could increase the breadth and seropositivity of antibody responses, which may improve vaccine protection against future viruses.
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Affiliation(s)
- Nina Urke Ertesvåg
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway.
| | - Rebecca Jane Cox
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - Sarah Larteley Lartey
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Kristin G-I Mohn
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Karl Albert Brokstad
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Mai-Chi Trieu
- Influenza Centre, Department of Clinical Science, University of Bergen, Bergen, Norway.
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17
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Minter A, Hoschler K, Jagne YJ, Sallah H, Armitage E, Lindsey B, Hay JA, Riley S, de Silva TI, Kucharski AJ. Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data. J Infect Dis 2022; 225:1750-1754. [PMID: 32556290 PMCID: PMC9113438 DOI: 10.1093/infdis/jiaa338] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 06/13/2020] [Indexed: 11/14/2022] Open
Abstract
Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.
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Affiliation(s)
- Amanda Minter
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Katja Hoschler
- Respiratory Virus Reference Department, Public Health England, London, United Kingdom
| | - Ya Jankey Jagne
- Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Banjul, The Gambia
| | - Hadijatou Sallah
- Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Banjul, The Gambia
| | - Edwin Armitage
- Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Banjul, The Gambia
| | - Benjamin Lindsey
- Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Banjul, The Gambia
| | - James A Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Thushan I de Silva
- Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Banjul, The Gambia
- The Florey Institute, Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Correspondence: Adam Kucharski, London School of Hygiene & Tropical Medicine, Keppel Street, Bloomsbury, London WC1E 7HT, United Kingdom ()
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18
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Bellizzi S, Panu Napodano CM, Pinto S, Pichierri G. COVID-19 and seasonal influenza: The potential 2021–22 “Twindemic”. Vaccine 2022; 40:3286-3287. [PMID: 35527058 PMCID: PMC9069306 DOI: 10.1016/j.vaccine.2022.04.074] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/14/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022]
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19
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Brouwer AF, Balmaseda A, Gresh L, Patel M, Ojeda S, Schiller AJ, Lopez R, Webby RJ, Nelson MI, Kuan G, Gordon A. Birth cohort relative to an influenza A virus's antigenic cluster introduction drives patterns of children's antibody titers. PLoS Pathog 2022; 18:e1010317. [PMID: 35192673 PMCID: PMC8896668 DOI: 10.1371/journal.ppat.1010317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/04/2022] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
An individual's antibody titers to influenza A strains are a result of the complicated interplay between infection history, cross-reactivity, immune waning, and other factors. It has been challenging to disentangle how population-level patterns of humoral immunity change as a function of age, calendar year, and birth cohort from cross-sectional data alone. We analyzed 1,589 longitudinal sera samples from 260 children across three studies in Nicaragua, 2006-16. Hemagglutination inhibition (HAI) titers were determined against four H3N2 strains, one H1N1 strain, and two H1N1pdm strains. We assessed temporal patterns of HAI titers using an age-period-cohort modeling framework. We found that titers against a given virus depended on calendar year of serum collection and birth cohort but not on age. Titer cohort patterns were better described by participants' ages relative to year of likely introduction of the virus's antigenic cluster than by age relative to year of strain introduction or by year of birth. These cohort effects may be driven by a decreasing likelihood of early-life infection after cluster introduction and by more broadly reactive antibodies at a young age. H3N2 and H1N1 viruses had qualitatively distinct cohort patterns, with cohort patterns of titers to specific H3N2 strains reaching their peak in children born 3 years prior to that virus's antigenic cluster introduction and with titers to H1N1 and H1N1pdm strains peaking for children born 1-2 years prior to cluster introduction but not being dramatically lower for older children. Ultimately, specific patterns of strain circulation and antigenic cluster introduction may drive population-level antibody titer patterns in children.
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Affiliation(s)
- Andrew F. Brouwer
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (AFB); (AG)
| | - Angel Balmaseda
- Sócrates Flores Vivas Health Center, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Lionel Gresh
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Mayuri Patel
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sergio Ojeda
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Amy J. Schiller
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Roger Lopez
- Sócrates Flores Vivas Health Center, Ministry of Health, Managua, Nicaragua
- Sustainable Sciences Institute, Managua, Nicaragua
| | - Richard J. Webby
- Department of Infectious Diseases, St. Jude Children’s Research Hospital, Memphis, Tennessee, United States of America
| | - Martha I. Nelson
- Laboratory of Parasitic Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Guillermina Kuan
- Sustainable Sciences Institute, Managua, Nicaragua
- Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, Nicaragua
| | - Aubree Gordon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (AFB); (AG)
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20
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Rees EM, Waterlow NR, Lowe R, Kucharski AJ. Estimating the duration of seropositivity of human seasonal coronaviruses using seroprevalence studies. Wellcome Open Res 2021; 6:138. [PMID: 34708157 PMCID: PMC8517721 DOI: 10.12688/wellcomeopenres.16701.3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 01/08/2023] Open
Abstract
Background: The duration of immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still uncertain, but it is of key clinical and epidemiological importance. Seasonal human coronaviruses (HCoV) have been circulating for longer and, therefore, may offer insights into the long-term dynamics of reinfection for such viruses. Methods: Combining historical seroprevalence data from five studies covering the four circulating HCoVs with an age-structured reverse catalytic model, we estimated the likely duration of seropositivity following seroconversion. Results: We estimated that antibody persistence lasted between 0.9 (95% Credible interval: 0.6 - 1.6) and 3.8 (95% CrI: 2.0 - 7.4) years. Furthermore, we found the force of infection in older children and adults (those over 8.5 [95% CrI: 7.5 - 9.9] years) to be higher compared with young children in the majority of studies. Conclusions: These estimates of endemic HCoV dynamics could provide an indication of the future long-term infection and reinfection patterns of SARS-CoV-2.
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Affiliation(s)
- Eleanor M. Rees
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Naomi R. Waterlow
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Adam J. Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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21
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Oidtman RJ, Arevalo P, Bi Q, McGough L, Russo CJ, Vera Cruz D, Costa Vieira M, Gostic KM. Influenza immune escape under heterogeneous host immune histories. Trends Microbiol 2021; 29:1072-1082. [PMID: 34218981 PMCID: PMC8578193 DOI: 10.1016/j.tim.2021.05.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 11/30/2022]
Abstract
In a pattern called immune imprinting, individuals gain the strongest immune protection against the influenza strains encountered earliest in life. In many recent examples, differences in early infection history can explain birth year-associated differences in susceptibility (cohort effects). Susceptibility shapes strain fitness, but without a clear conceptual model linking host susceptibility to the identity and order of past infections general conclusions on the evolutionary and epidemic implications of cohort effects are not possible. Failure to differentiate between cohort effects caused by differences in the set, rather than the order (path), of past infections is a current source of confusion. We review and refine hypotheses for path-dependent cohort effects, which include imprinting. We highlight strategies to measure their underlying causes and emergent consequences.
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Affiliation(s)
- Rachel J Oidtman
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Philip Arevalo
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Qifang Bi
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Lauren McGough
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | | | - Diana Vera Cruz
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Marcos Costa Vieira
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Katelyn M Gostic
- Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
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22
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Rees EM, Waterlow NR, Lowe R, Kucharski AJ. Estimating the duration of seropositivity of human seasonal coronaviruses using seroprevalence studies. Wellcome Open Res 2021; 6:138. [PMID: 34708157 PMCID: PMC8517721 DOI: 10.12688/wellcomeopenres.16701.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 11/20/2022] Open
Abstract
Background: The duration of immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still uncertain, but it is of key clinical and epidemiological importance. Seasonal human coronaviruses (HCoV) have been circulating for longer and, therefore, may offer insights into the long-term dynamics of reinfection for such viruses. Methods: Combining historical seroprevalence data from five studies covering the four circulating HCoVs with an age-structured reverse catalytic model, we estimated the likely duration of seropositivity following seroconversion. Results: We estimated that antibody persistence lasted between 0.9 (95% Credible interval: 0.6 - 1.6) and 3.8 (95% CrI: 2.0 - 7.4) years. Furthermore, we found the force of infection in older children and adults (those over 8.5 [95% CrI: 7.5 - 9.9] years) to be higher compared with young children in the majority of studies. Conclusions: These estimates of endemic HCoV dynamics could provide an indication of the future long-term infection and reinfection patterns of SARS-CoV-2.
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Affiliation(s)
- Eleanor M. Rees
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Naomi R. Waterlow
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Adam J. Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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23
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Vinh DN, Nhat NTD, de Bruin E, Vy NHT, Thao TTN, Phuong HT, Anh PH, Todd S, Quan TM, Thanh NTL, Lien NTN, Ha NTH, Hong TTK, Thai PQ, Choisy M, Nguyen TD, Simmons CP, Thwaites GE, Clapham HE, Chau NVV, Koopmans M, Boni MF. Age-seroprevalence curves for the multi-strain structure of influenza A virus. Nat Commun 2021; 12:6680. [PMID: 34795239 PMCID: PMC8602397 DOI: 10.1038/s41467-021-26948-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/27/2021] [Indexed: 11/21/2022] Open
Abstract
The relationship between age and seroprevalence can be used to estimate the annual attack rate of an infectious disease. For pathogens with multiple serologically distinct strains, there is a need to describe composite exposure to an antigenically variable group of pathogens. In this study, we assay 24,402 general-population serum samples, collected in Vietnam between 2009 to 2015, for antibodies to eleven human influenza A strains. We report that a principal components decomposition of antibody titer data gives the first principal component as an appropriate surrogate for seroprevalence; this results in annual attack rate estimates of 25.6% (95% CI: 24.1% - 27.1%) for subtype H3 and 16.0% (95% CI: 14.7% - 17.3%) for subtype H1. The remaining principal components separate the strains by serological similarity and associate birth cohorts with their particular influenza histories. Our work shows that dimensionality reduction can be used on human antibody profiles to construct an age-seroprevalence relationship for antigenically variable pathogens.
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MESH Headings
- Algorithms
- Antibodies, Viral/blood
- Antibodies, Viral/immunology
- Geography
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Humans
- Immunoglobulin G/blood
- Immunoglobulin G/immunology
- Influenza A Virus, H1N1 Subtype/immunology
- Influenza A Virus, H1N1 Subtype/physiology
- Influenza A Virus, H3N2 Subtype/immunology
- Influenza A Virus, H3N2 Subtype/physiology
- Influenza A virus/classification
- Influenza A virus/immunology
- Influenza A virus/physiology
- Influenza, Human/epidemiology
- Influenza, Human/immunology
- Influenza, Human/virology
- Models, Theoretical
- Seroepidemiologic Studies
- Time Factors
- Vietnam/epidemiology
- Virus Replication/immunology
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Affiliation(s)
- Dao Nguyen Vinh
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Nguyen Thi Duy Nhat
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Erwin de Bruin
- Department of Viroscience, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Nguyen Ha Thao Vy
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Tran Thi Nhu Thao
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Huynh Thi Phuong
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Pham Hong Anh
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Stacy Todd
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
- Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, England
| | - Tran Minh Quan
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | - Nguyen Thi Le Thanh
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
| | | | | | | | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Marc Choisy
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tran Dang Nguyen
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Cameron P Simmons
- Institute of Vector Borne Disease, Monash University, Melbourne, VIC, Australia
| | - Guy E Thwaites
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Hannah E Clapham
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | | | - Marion Koopmans
- Department of Viroscience, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Maciej F Boni
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, USA.
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24
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Hozé N, Diarra I, Sangaré AK, Pastorino B, Pezzi L, Kouriba B, Sagara I, Dabo A, Djimdé A, Thera MA, Doumbo OK, de Lamballerie X, Cauchemez S. Model-based assessment of Chikungunya and O'nyong-nyong virus circulation in Mali in a serological cross-reactivity context. Nat Commun 2021; 12:6735. [PMID: 34795213 PMCID: PMC8602252 DOI: 10.1038/s41467-021-26707-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Serological surveys are essential to quantify immunity in a population but serological cross-reactivity often impairs estimates of the seroprevalence. Here, we show that modeling helps addressing this key challenge by considering the important cross-reactivity between Chikungunya (CHIKV) and O'nyong-nyong virus (ONNV) as a case study. We develop a statistical model to assess the epidemiology of these viruses in Mali. We additionally calibrate the model with paired virus neutralization titers in the French West Indies, a region with known CHIKV circulation but no ONNV. In Mali, the model estimate of ONNV and CHIKV prevalence is 30% and 13%, respectively, versus 27% and 2% in non-adjusted estimates. While a CHIKV infection induces an ONNV response in 80% of cases, an ONNV infection leads to a cross-reactive CHIKV response in only 22% of cases. Our study shows the importance of conducting serological assays on multiple cross-reactive pathogens to estimate levels of virus circulation.
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Affiliation(s)
- Nathanaël Hozé
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS, Paris, France
| | - Issa Diarra
- Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection), Marseille, France
- Malaria Research and Training Center, USTT, Bamako, Mali
| | - Abdoul Karim Sangaré
- Malaria Research and Training Center, USTT, Bamako, Mali
- Centre d'Infectiologie Charles Mérieux, Bamako, Mali
| | - Boris Pastorino
- Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection), Marseille, France
| | - Laura Pezzi
- Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection), Marseille, France
- EA7310, Laboratoire de Virologie, Université de Corse-Inserm, Corte, France
| | - Bourèma Kouriba
- Malaria Research and Training Center, USTT, Bamako, Mali
- Centre d'Infectiologie Charles Mérieux, Bamako, Mali
| | - Issaka Sagara
- Malaria Research and Training Center, USTT, Bamako, Mali
| | - Abdoulaye Dabo
- Malaria Research and Training Center, USTT, Bamako, Mali
| | | | | | | | - Xavier de Lamballerie
- Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection), Marseille, France.
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS, Paris, France
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25
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McLeod DV, Wahl LM, Mideo N. Mosaic vaccination: How distributing different vaccines across a population could improve epidemic control. Evol Lett 2021; 5:458-471. [PMID: 34621533 PMCID: PMC8484727 DOI: 10.1002/evl3.252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/27/2021] [Indexed: 01/19/2023] Open
Abstract
Although vaccination has been remarkably effective against some pathogens, for others, rapid antigenic evolution results in vaccination conferring only weak and/or short‐lived protection. Consequently, considerable effort has been invested in developing more evolutionarily robust vaccines, either by targeting highly conserved components of the pathogen (universal vaccines) or by including multiple immunological targets within a single vaccine (multi‐epitope vaccines). An unexplored third possibility is to vaccinate individuals with one of a number of qualitatively different vaccines, creating a “mosaic” of individual immunity in the population. Here we explore whether a mosaic vaccination strategy can deliver superior epidemiological outcomes to “conventional” vaccination, in which all individuals receive the same vaccine. We suppose vaccine doses can be distributed between distinct vaccine “targets” (e.g., different surface proteins against which an immune response can be generated) and/or immunologically distinct variants at these targets (e.g., strains); the pathogen can undergo antigenic evolution at both targets. Using simple mathematical models, here we provide a proof‐of‐concept that mosaic vaccination often outperforms conventional vaccination, leading to fewer infected individuals, improved vaccine efficacy, and lower individual risks over the course of the epidemic.
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Affiliation(s)
- David V McLeod
- Centre D'Ecologie Fonctionnelle & Evolutive CNRS Montpellier 34090 France
| | - Lindi M Wahl
- Mathematics Western University London ON N6A 5B7 Canada
| | - Nicole Mideo
- Department of Ecology and Evolutionary Biology University of Toronto Toronto ON M5S 3B2 Canada
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26
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Lazova S, Velikova T. DYNAMICS OF CHILDHOOD RESPIRATORY INFECTIONS DURING THE COVID-19 PANDEMIC: THE EFFECT OF QUARANTINE АND BEYOND. CENTRAL ASIAN JOURNAL OF MEDICAL HYPOTHESES AND ETHICS 2021. [DOI: 10.47316/cajmhe.2021.2.3.04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Monitoring epidemic processes and the dynamics of the spread of infectious diseases is essential for predicting their distribution and effective planning in healthcare. The importance of studying seasonal trends in the spread of respiratory viral infections and the specific effects of non-pharmaceutical interventions in nationwide scales and the use of available vaccines stand out even more in the context of the coronavirus disease-19 (COVID-19) pandemic. Even if the dynamics of pediatric respiratory viral infections show some variation at the national and local levels, depending on health regulation, respiratory viral pathogens follow a typical pattern of incidence. Therefore, we hypothesize that anticipated reduction of the incidence of common respiratory viral infections would undoubtedly exert positive effects, such as ease of burdening healthcare that combates the COVID-19 pandemic. However, we suspect a shift in familiar seasonal characteristics of common respiratory viral infections. We also speculate that strict long-term limitations of the natural spread of respiratory viral infections can lead to the development of hard-to-predict epidemiological outliers. Additionally, the tricky balance between humanity’s natural impulse to return to normalcy and control the new and still dynamically evolving infection could lead to new threats from old and well-known pathogens. Finally, we hypothesize that the absence of regular influenza virus circulation may lead to a high mismatch rate and a significant reduction in flu vaccine efficacy.
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27
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Gorse GJ, Rattigan SM, Kirpich A, Simberkoff MS, Bessesen MT, Gibert C, Nyquist AC, Price CS, Gaydos CA, Radonovich LJ, Perl TM, Rodriguez-Barradas MC, Cummings DAT. Influence of Pre-Season Antibodies against Influenza Virus on Risk of Influenza Infection among Health Care Personnel. J Infect Dis 2021; 225:891-902. [PMID: 34534319 DOI: 10.1093/infdis/jiab468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 09/14/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The association of hemagglutination inhibition (HAI) antibodies with protection from influenza among healthcare personnel (HCP) with occupational exposure to influenza viruses has not been well-described. METHODS The Respiratory Protection Effectiveness Clinical Trial was a cluster-randomized, multi-site study that compared medical masks to N95 respirators in preventing viral respiratory infections among HCP in outpatient healthcare settings for 5,180 participant-seasons. Serum HAI antibody titers before each influenza season and influenza virus infection confirmed by polymerase chain reaction were studied over four study years. RESULTS In univariate models, the risk of influenza A(H3N2) and B virus infections was associated with HAI titers to each virus, study year, and site. HAI titers were strongly associated with vaccination. Within multivariate models, each log base 2 increase in titer was associated with 15%, 26% and 33-35% reductions in the hazard of influenza A(H3N2), A(H1N1) and B infections, respectively. Best models included pre-season antibody titers and study year, but not other variables. CONCLUSIONS HAI titers were associated with protection from influenza among HCP with routine exposure to patients with respiratory illness and influenza season contributed to risk. HCP can be reassured about receiving influenza vaccination to stimulate immunity.
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Affiliation(s)
- Geoffrey J Gorse
- Section of Infectious Diseases, Veterans Affairs St. Louis Health Care System, St. Louis, MO, 63106 USA.,Division of Infectious Diseases, Allergy and Immunology, Saint Louis University School of Medicine, St. Louis, MO, 63104 USA
| | - Susan M Rattigan
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - Alexander Kirpich
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA USA
| | - Michael S Simberkoff
- Department of Medicine, Veterans Affairs New York Harbor Healthcare System, New York, NY, USA.,Division of Infectious Diseases, New York University Grossman School of Medicine, New York, NY, USA
| | - Mary T Bessesen
- Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO, 80045 USA.,Division of Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, USA
| | - Cynthia Gibert
- Medical Service, Washington D.C. Veterans Affairs Medical Center, Washington, DC, USA
| | - Ann-Christine Nyquist
- Division of Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, USA.,Department of Pediatrics, Section of Pediatric Infectious Disease and Epidemiology Children's Hospital Colorado, Aurora, CO, USA
| | - Connie Savor Price
- Division of Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, USA.,Infectious Diseases, Denver Health, Denver, CO, USA
| | - Charlotte A Gaydos
- Department of Medicine and Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lewis J Radonovich
- Respiratory Health Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, WV USA
| | - Trish M Perl
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern, Dallas, TX, USA
| | - Maria C Rodriguez-Barradas
- Infectious Diseases Section, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.,Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA.,Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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28
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Rees EM, Waterlow NR, Lowe R, Kucharski AJ. Estimating the duration of seropositivity of human seasonal coronaviruses using seroprevalence studies. Wellcome Open Res 2021; 6:138. [PMID: 34708157 PMCID: PMC8517721 DOI: 10.12688/wellcomeopenres.16701.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2021] [Indexed: 11/20/2022] Open
Abstract
Background: The duration of immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still uncertain, but it is of key clinical and epidemiological importance. Seasonal human coronaviruses (HCoV) have been circulating for longer and, therefore, may offer insights into the long-term dynamics of reinfection for such viruses. Methods: Combining historical seroprevalence data from five studies covering the four circulating HCoVs with an age-structured reverse catalytic model, we estimated the likely duration of seropositivity following seroconversion. Results: We estimated that antibody persistence lasted between 0.9 (95% Credible interval: 0.6 - 1.6) and 3.8 (95% CrI: 2.0 - 7.4) years. Furthermore, we found the force of infection in older children and adults (those over 8.5 [95% CrI: 7.5 - 9.9] years) to be higher compared with young children in the majority of studies. Conclusions: These estimates of endemic HCoV dynamics could provide an indication of the future long-term infection and reinfection patterns of SARS-CoV-2.
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Affiliation(s)
- Eleanor M. Rees
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Naomi R. Waterlow
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Adam J. Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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29
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Owers Bonner KA, Cruz JS, Sacramento GA, de Oliveira D, Nery N, Carvalho M, Costa F, Childs JE, Ko AI, Diggle PJ. Effects of Accounting for Interval-Censored Antibody Titer Decay on Seroincidence in a Longitudinal Cohort Study of Leptospirosis. Am J Epidemiol 2021; 190:893-899. [PMID: 33274738 DOI: 10.1093/aje/kwaa253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 11/14/2020] [Accepted: 11/16/2020] [Indexed: 12/23/2022] Open
Abstract
Accurate measurements of seroincidence are critical for infections undercounted by reported cases, such as influenza, arboviral diseases, and leptospirosis. However, conventional methods of interpreting paired serological samples do not account for antibody titer decay, resulting in underestimated seroincidence rates. To improve interpretation of paired sera, we modeled exponential decay of interval-censored microscopic agglutination test titers using a historical data set of leptospirosis cases traced to a point source exposure in Italy in 1984. We then applied that decay rate to a longitudinal cohort study conducted in a high-transmission setting in Salvador, Brazil (2013-2015). We estimated a decay constant of 0.926 (95% confidence interval: 0.918, 0.934) titer dilutions per month. Accounting for decay in the cohort increased the mean infection rate to 1.21 times the conventionally defined rate over 6-month intervals (range, 1.10-1.36) and 1.82 times that rate over 12-month intervals (range, 1.65-2.07). Improved estimates of infection in longitudinal data have broad epidemiologic implications, including comparing studies with different sampling intervals, improving sample size estimation, and determining risk factors for infection and the role of acquired immunity. Our method of estimating and accounting for titer decay is generalizable to other infections defined using interval-censored serological assays.
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30
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Wang L, Xie D, Yu J, Koroma MM, Qiu M, Duan W, Zhang XF, Dai YC. Serological surveillance of noroviruses in a community-based prospective cohort: a study protocol. BMJ Open 2021; 11:e043228. [PMID: 33664074 PMCID: PMC7934767 DOI: 10.1136/bmjopen-2020-043228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Noroviruses are the leading cause of viral acute gastroenteritis affecting all age groups. Since 2014, the previous rarely reported GII.P17-GII.17 and recombinant GII.P16-GII.2 norovirus emerged, replacing GII.4 predominant genotype, causing increased outbreaks in China and other countries. Meanwhile, GII.4/2012 Sydney strain has re-emerged as the dominant variant in many places in 2015-2018. The role of herd immunity as the driving force during these new emerging or re-emerging noroviruses is poorly defined. Serological surveillance studies on community-based prospective cohort on norovirus are highly needed. METHODS AND ANALYSES This study will include 1000 out of 9798 participants aged 18 years and above from Caofeidian district, Tangshan city, northern China. Baseline data on sociodemographic characteristics and blood samples were collected in 2013-2014. Blood collection will be replicated annually throughout the cohort until 2023. Saliva samples were also collected in 2016. The seroprevalence and seroincidence of blockade antibodies against norovirus genotypes of GII.P17-GII.17, GII.P16-GII.2, the re-emerged GII.4/2012 and potential novel pandemic variants will be evaluated by ELISA. Associations between genotype blockade antibodies and sociodemographic factors and human histo-blood group antigens will be evaluated using univariate and multivariate analysis. The dynamics of herd immunity duration will be estimated in this longitudinal surveillance. ETHICS AND DISSEMINATION The study has been approved by the Ethical Committees of the Staff Hospital of Jidong oil-field of China National Petroleum Corporation. This study will provide insight into the seroprevalence and seroincidence of noroviruses, and their relationships with sociodemographic characteristics and genetic susceptibility. It will also explain herd immunity of the emerged and re-emerged genotypes or variants. The study will further enable an understanding of the mechanism driving the replacement of norovirus genotypes. Research findings will be disseminated in peer-reviewed journals and at scientific meetings.
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Affiliation(s)
- Lu Wang
- Department of Epidemiology,Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Dongjie Xie
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Jingrong Yu
- Department of Epidemiology,Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Mark Momoh Koroma
- Department of Epidemiology,Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Mengsi Qiu
- Department of Epidemiology,Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Wentao Duan
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Xu-Fu Zhang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, Guangdong, China
| | - Ying-Chun Dai
- Department of Epidemiology,Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
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31
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Quandelacy TM, Cummings DAT, Jiang CQ, Yang B, Kwok KO, Dai B, Shen R, Read JM, Zhu H, Guan Y, Riley S, Lessler J. Using serological measures to estimate influenza incidence in the presence of secular trends in exposure and immuno-modulation of antibody response. Influenza Other Respir Viruses 2021; 15:235-244. [PMID: 33108707 PMCID: PMC7902255 DOI: 10.1111/irv.12807] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/24/2020] [Accepted: 08/30/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Influenza infection is often measured by a fourfold antibody titer increase over an influenza season (ie seroconversion). However, this approach may fail when influenza seasons are less distinct as it does not account for transient effects from recent infections. Here, we present a method to determine seroconversion for non-paired sera, adjusting for changes in individuals' antibody titers to influenza due to the transient impact of recent exposures, varied sampling times, and laboratory processes. METHODS We applied our method using data for five H3N2 strains collected from 942 individuals, aged 2-90 years, during the first two study visits of the Fluscape cohort study (2009-2012) in Guangzhou, China. RESULTS After adjustment, apparent seroconversion rates for non-circulating strains decreased while we observed a 20% increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain (A/Brisbane/20/2007) in our study, participants aged under 18, and over 64 had the highest seroconversion rates compared to other age groups. CONCLUSIONS Our results highlight the need for improved methods when using antibody titers as an endpoint in settings where there is no clear influenza "off" season. Methods, like those presented here, that use titers from circulating and non-circulating strains may be key.
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Affiliation(s)
- Talia M. Quandelacy
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
- Present address:
Centers for Disease Control and PreventionSan JuanPuerto Rico
| | - Derek A. T. Cummings
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
- Department of BiologyUniversity of FloridaGainesvilleFLUSA
| | | | - Bingyi Yang
- Department of BiologyUniversity of FloridaGainesvilleFLUSA
| | - Kin On Kwok
- The Jockey Club School of Public Health and Primary CareThe Chinese University of Hong KongHong Kong Special Administrative RegionChina
- Stanley Ho Centre for Emerging Infectious DiseasesHong Kong Special Administrative RegionThe Chinese University of Hong KongShatin, Hong KongChina
- Shenzhen Research InstituteThe Chinese University of Hong KongShenzhenChina
| | - Byran Dai
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | | | - Jonathan M. Read
- Center for Health Informatics Computing and StatisticsLancaster Medical SchoolLancaster UniversityLancasterUK
- Institute of Infection and Global HealthUniversity of LiverpoolLiverpoolUK
| | - Huachen Zhu
- State Key Laboratory of Emerging Infectious DiseasesSchool of Public HealthThe University of Hong KongHong KongChina
- Shantou University Medical CollegeShantouChina
| | - Yi Guan
- Shantou University Medical CollegeShantouChina
- School of Public HealthImperial College LondonLondonUK
| | - Steven Riley
- School of Public HealthImperial College LondonLondonUK
| | - Justin Lessler
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
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32
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Sánchez de Prada L, Sanz Muñoz I, Castrodeza Sanz J, Ortiz de Lejarazu Leonardo R, Eiros Bouza JM. Adjuvanted Influenza Vaccines Elicits Higher Antibody Responses against the A(H3N2) Subtype than Non-Adjuvanted Vaccines. Vaccines (Basel) 2020; 8:E704. [PMID: 33255600 PMCID: PMC7712667 DOI: 10.3390/vaccines8040704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/17/2020] [Accepted: 11/20/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND vaccination is the best approach to prevent influenza infections so far. Serological studies on the effect of different vaccine types are important to address vaccination campaigns and protect our population. In our study, we compared the serological response against influenza A subtypes using the non-adjuvanted influenza vaccine (NAIV) in adults and the elderly and the adjuvanted influenza vaccine (AIV) in the elderly. METHODS We performed a retrospective analysis by hemagglutination inhibition assay (HI) of serum samples right before and 28 days after seasonal influenza vaccination during the 1996-2017 seasons. CONCLUSIONS The AIV presents better performance against the A(H3N2) subtype in the elderly whereas the NAIV induces a better response against A(H1N1)pdm09 in the same group.
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Affiliation(s)
| | - Iván Sanz Muñoz
- Centro Nacional de Gripe de Valladolid, 47009 Valladolid, Spain; (I.S.M.); (R.O.d.L.L.); (J.M.E.B.)
| | | | | | - José María Eiros Bouza
- Centro Nacional de Gripe de Valladolid, 47009 Valladolid, Spain; (I.S.M.); (R.O.d.L.L.); (J.M.E.B.)
- Hospital Universitario Río Hortega de Valladolid, 47012 Valladolid, Spain
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33
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Viboud C, Gostic K, Nelson MI, Price GE, Perofsky A, Sun K, Sequeira Trovão N, Cowling BJ, Epstein SL, Spiro DJ. Beyond clinical trials: Evolutionary and epidemiological considerations for development of a universal influenza vaccine. PLoS Pathog 2020; 16:e1008583. [PMID: 32970783 PMCID: PMC7514029 DOI: 10.1371/journal.ppat.1008583] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The prospect of universal influenza vaccines is generating much interest and research at the intersection of immunology, epidemiology, and viral evolution. While the current focus is on developing a vaccine that elicits a broadly cross-reactive immune response in clinical trials, there are important downstream questions about global deployment of a universal influenza vaccine that should be explored to minimize unintended consequences and maximize benefits. Here, we review and synthesize the questions most relevant to predicting the population benefits of universal influenza vaccines and discuss how existing information could be mined to begin to address these questions. We review three research topics where computational modeling could bring valuable evidence: immune imprinting, viral evolution, and transmission. We address the positive and negative consequences of imprinting, in which early childhood exposure to influenza shapes and limits immune responses to future infections via memory of conserved influenza antigens. However, the mechanisms at play, their effectiveness, breadth of protection, and the ability to "reprogram" already imprinted individuals, remains heavily debated. We describe instances of rapid influenza evolution that illustrate the plasticity of the influenza virus in the face of drug pressure and discuss how novel vaccines could introduce new selective pressures on the evolution of the virus. We examine the possible unintended consequences of broadly protective (but infection-permissive) vaccines on the dynamics of epidemic and pandemic influenza, compared to conventional vaccines that have been shown to provide herd immunity benefits. In conclusion, computational modeling offers a valuable tool to anticipate the benefits of ambitious universal influenza vaccine programs, while balancing the risks from endemic influenza strains and unpredictable pandemic viruses. Moving forward, it will be important to mine the vast amount of data generated in clinical studies of universal influenza vaccines to ensure that the benefits and consequences of these vaccine programs have been carefully modeled and explored.
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Affiliation(s)
- Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
- * E-mail:
| | - Katelyn Gostic
- Dept. of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States
- Dept. of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States
| | - Martha I. Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Graeme E. Price
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States
| | - Amanda Perofsky
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Kaiyuan Sun
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Nídia Sequeira Trovão
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Suzanne L. Epstein
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States
| | - David J. Spiro
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States
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Lebeau G, Vagner D, Frumence É, Ah-Pine F, Guillot X, Nobécourt E, Raffray L, Gasque P. Deciphering SARS-CoV-2 Virologic and Immunologic Features. Int J Mol Sci 2020; 21:E5932. [PMID: 32824753 PMCID: PMC7460647 DOI: 10.3390/ijms21165932] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/11/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 and its associated pathology, COVID-19, have been of particular concerns these last months due to the worldwide burden they represent. The number of cases requiring intensive care being the critical point in this epidemic, a better understanding of the pathophysiology leading to these severe cases is urgently needed. Tissue lesions can be caused by the pathogen or can be driven by an overwhelmed immune response. Focusing on SARS-CoV-2, we and others have observed that this virus can trigger indeed an immune response that can be dysregulated in severe patients and leading to further injury to multiple organs. The purpose of the review is to bring to light the current knowledge about SARS-CoV-2 virologic and immunologic features. Thus, we address virus biology, life cycle, tropism for many organs and how ultimately it will affect several host biological and physiological functions, notably the immune response. Given that therapeutic avenues are now highly warranted, we also discuss the immunotherapies available to manage the infection and the clinical outcomes.
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Affiliation(s)
- Grégorie Lebeau
- Unité de Recherche Études Pharmaco-Immunologiques, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France; (D.V.); (É.F.); (X.G.); (P.G.)
- Laboratoire de Biologie, Secteur Laboratoire d’immunologie Clinique et Expérimentale de la Zone de l’océan Indien (LICE-OI), Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France
| | - Damien Vagner
- Unité de Recherche Études Pharmaco-Immunologiques, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France; (D.V.); (É.F.); (X.G.); (P.G.)
- Unité Mixte de Recherche Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Université de La Réunion, INSERM UMR 1187, CNRS 9192, IRD 249, Platform CYROI, 2 rue Maxime Rivière, 97491 Sainte Clotilde, La Réunion, France
| | - Étienne Frumence
- Unité de Recherche Études Pharmaco-Immunologiques, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France; (D.V.); (É.F.); (X.G.); (P.G.)
- Laboratoire de Biologie, Secteur Laboratoire d’immunologie Clinique et Expérimentale de la Zone de l’océan Indien (LICE-OI), Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France
| | - Franck Ah-Pine
- Service d’anatomo-Pathologie, Centre Hospitalier Universitaire Sud Réunion, 97410 Saint Pierre, France;
| | - Xavier Guillot
- Unité de Recherche Études Pharmaco-Immunologiques, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France; (D.V.); (É.F.); (X.G.); (P.G.)
- Service de Rhumatologie, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France
| | - Estelle Nobécourt
- Service d’endocrinologie Diabétologie, Centre Hospitalier Universitaire Sud Réunion, 97410 Saint Pierre, France;
- Université de Formation et de Recherche Santé, Université de la Réunion, 97400 Saint-Denis, France
| | - Loïc Raffray
- Service de Médecine Interne, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France;
| | - Philippe Gasque
- Unité de Recherche Études Pharmaco-Immunologiques, Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France; (D.V.); (É.F.); (X.G.); (P.G.)
- Laboratoire de Biologie, Secteur Laboratoire d’immunologie Clinique et Expérimentale de la Zone de l’océan Indien (LICE-OI), Centre Hospitalier Universitaire La Réunion Site Félix Guyon, CS11021, 97400 Saint Denis de La Réunion, France
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35
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Yang B, Lessler J, Zhu H, Jiang CQ, Read JM, Hay JA, Kwok KO, Shen R, Guan Y, Riley S, Cummings DAT. Life course exposures continually shape antibody profiles and risk of seroconversion to influenza. PLoS Pathog 2020; 16:e1008635. [PMID: 32702069 PMCID: PMC7377380 DOI: 10.1371/journal.ppat.1008635] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/14/2020] [Indexed: 12/05/2022] Open
Abstract
Complex exposure histories and immune mediated interactions between influenza strains contribute to the life course of human immunity to influenza. Antibody profiles can be generated by characterizing immune responses to multiple antigenically variant strains, but how these profiles vary across individuals and determine future responses is unclear. We used hemagglutination inhibition titers from 21 H3N2 strains to construct 777 paired antibody profiles from people aged 2 to 86, and developed novel metrics to capture features of these profiles. Total antibody titer per potential influenza exposure increases in early life, then decreases in middle age. Increased titers to one or more strains were seen in 97.8% of participants during a roughly four-year interval, suggesting widespread influenza exposure. While titer changes were seen to all strains, recently circulating strains exhibited the greatest titer rise. Higher pre-existing, homologous titers at baseline reduced the risk of seroconversion to recent strains. After adjusting for homologous titer, we also found an increased frequency of seroconversion against recent strains among those with higher immunity to older previously exposed strains. Including immunity to previously exposures also improved the deviance explained by the models. Our results suggest that a comprehensive quantitative description of immunity encompassing past exposures could lead to improved correlates of risk of influenza infection.
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Affiliation(s)
- Bingyi Yang
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Huachen Zhu
- State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, School of Public Health, The University of Hong Kong, Hong Kong SAR, China
- Joint Institute of Virology (Shantou University–The University of Hong Kong), Shantou University, Shantou, Guangdong, China
| | | | - Jonathan M. Read
- Centre for Health Informatics Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - James A. Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China
- Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, Guangdong, China
| | - Ruiyin Shen
- Guangzhou No.12 Hospital, Guangzhou, Guangdong, China
| | - Yi Guan
- State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, School of Public Health, The University of Hong Kong, Hong Kong SAR, China
- Joint Institute of Virology (Shantou University–The University of Hong Kong), Shantou University, Shantou, Guangdong, China
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Derek A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
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36
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Pease (1987): The evolutionary epidemiology of influenza A. Theor Popul Biol 2020; 133:29-32. [DOI: 10.1016/j.tpb.2019.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/20/2019] [Accepted: 12/22/2019] [Indexed: 12/26/2022]
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37
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Hay JA, Minter A, Ainslie KEC, Lessler J, Yang B, Cummings DAT, Kucharski AJ, Riley S. An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver. PLoS Comput Biol 2020; 16:e1007840. [PMID: 32365062 PMCID: PMC7241836 DOI: 10.1371/journal.pcbi.1007840] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/21/2020] [Accepted: 04/01/2020] [Indexed: 11/19/2022] Open
Abstract
We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.
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Affiliation(s)
- James A. Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Amanda Minter
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kylie E. C. Ainslie
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Bingyi Yang
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Derek A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Adam J. Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
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38
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Henderson AD, Aubry M, Kama M, Vanhomwegen J, Teissier A, Mariteragi-Helle T, Paoaafaite T, Teissier Y, Manuguerra JC, Edmunds J, Whitworth J, Watson CH, Lau CL, Cao-Lormeau VM, Kucharski AJ. Zika seroprevalence declines and neutralizing antibodies wane in adults following outbreaks in French Polynesia and Fiji. eLife 2020; 9:48460. [PMID: 31987069 PMCID: PMC6986872 DOI: 10.7554/elife.48460] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 11/20/2019] [Indexed: 12/30/2022] Open
Abstract
It has been commonly assumed that Zika virus (ZIKV) infection confers long-term protection against reinfection, preventing ZIKV from re-emerging in previously affected areas for several years. However, the long-term immune response to ZIKV following an outbreak remains poorly documented. We compared results from eight serological surveys before and after known ZIKV outbreaks in French Polynesia and Fiji, including cross-sectional and longitudinal studies. We found evidence of a decline in seroprevalence in both countries over a two-year period following first reported ZIKV transmission. This decline was concentrated in adults, while high seroprevalence persisted in children. In the Fiji cohort, there was also a significant decline in neutralizing antibody titres against ZIKV, but not against dengue viruses that circulated during the same period.
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Affiliation(s)
- Alasdair D Henderson
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Maite Aubry
- Institut Louis Malardé, Papeete, French Polynesia
| | - Mike Kama
- Fiji Centre for Communicable Disease Control, Suva, Fiji.,The University of the South Pacific, Suva, Fiji
| | | | | | | | | | - Yoann Teissier
- Direction de la Santé de la Polynésie française, Papeete, French Polynesia
| | | | - John Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jimmy Whitworth
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Conall H Watson
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Hirotsu N, Saisho Y, Hasegawa T, Kitano M, Shishido T. Antibody dynamics in Japanese paediatric patients with influenza A infection treated with neuraminidase inhibitors in a randomised trial. Sci Rep 2019; 9:11891. [PMID: 31417163 PMCID: PMC6695405 DOI: 10.1038/s41598-019-47884-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/16/2019] [Indexed: 12/24/2022] Open
Abstract
Neuraminidase inhibitors (NAIs) complement influenza virus infection management by helping to clear virus, alleviate symptoms, and reduce transmission. In a previous randomised study, we examined the effect of 4 NAIs on virus clearance and influenza symptoms in Japanese paediatric patients. In this second analysis, we examined the effects of NAI treatment on antibody responses and virus clearance, and the relationships between antibody responses and patients' infection histories (previous infection; asymptomatic infection via household members of same virus type/subtype; vaccination), and between infection histories and viral kinetics. Haemagglutination inhibition (HI) antibody responses produced HI titres ≥40 by Day 14 of NAI treatment, in parallel with virus clearance (trend test P = 0.001). Comparing patients with and without influenza infection histories (directly or asymptomatic infection via household members) showed that infection history had a marked positive effect on HI antibody responses in patients vaccinated before the current influenza season (before enrolment). Current virus clearance was significantly faster in patients previously infected with the same virus type/subtype than in those not previously infected, and clearance pattern depended on the NAI. Assessment of anti-influenza effects of antiviral drugs and vaccines should consider virus and antibody dynamics in response to vaccination and natural infection histories.
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40
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Hay JA, Laurie K, White M, Riley S. Characterising antibody kinetics from multiple influenza infection and vaccination events in ferrets. PLoS Comput Biol 2019; 15:e1007294. [PMID: 31425503 PMCID: PMC6715255 DOI: 10.1371/journal.pcbi.1007294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/29/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022] Open
Abstract
The strength and breadth of an individual's antibody repertoire is an important predictor of their response to influenza infection or vaccination. Although progress has been made in understanding qualitatively how repeated exposures shape the antibody mediated immune response, quantitative understanding remains limited. We developed a set of mathematical models describing short-term antibody kinetics following influenza infection or vaccination and fit them to haemagglutination inhibition (HI) titres from 5 groups of ferrets which were exposed to different combinations of trivalent inactivated influenza vaccine (TIV with or without adjuvant), A/H3N2 priming inoculation and post-vaccination A/H1N1 inoculation. We fit models with various immunological mechanisms that have been empirically observed but have not previously been included in mathematical models of antibody landscapes, including: titre ceiling effects, antigenic seniority and exposure-type specific cross reactivity. Based on the parameter estimates of the best supported models, we describe a number of key immunological features. We found quantifiable differences in the degree of homologous and cross-reactive antibody boosting elicited by different exposure types. Infection and adjuvanted vaccination generally resulted in strong, broadly reactive responses whereas unadjuvanted vaccination resulted in a weak, narrow response. We found that the order of exposure mattered: priming with A/H3N2 improved subsequent vaccine response, and the second dose of adjuvanted vaccination resulted in substantially greater antibody boosting than the first. Either antigenic seniority or a titre ceiling effect were included in the two best fitting models, suggesting a role for a mechanism describing diminishing antibody boosting with repeated exposures. Although there was considerable uncertainty in our estimates of antibody waning parameters, our results suggest that both short and long term waning were present and would be identifiable with a larger set of experiments. These results highlight the potential use of repeat exposure animal models in revealing short-term, strain-specific immune dynamics of influenza.
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MESH Headings
- Adjuvants, Immunologic/administration & dosage
- Animals
- Antibodies, Viral/blood
- Computational Biology
- Cross Reactions
- Disease Models, Animal
- Ferrets/immunology
- Humans
- Immunization, Secondary
- Influenza A Virus, H1N1 Subtype/immunology
- Influenza A Virus, H3N2 Subtype/immunology
- Influenza Vaccines/administration & dosage
- Influenza, Human/immunology
- Influenza, Human/prevention & control
- Kinetics
- Models, Immunological
- Orthomyxoviridae Infections/immunology
- Orthomyxoviridae Infections/virology
- Vaccines, Inactivated/administration & dosage
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Affiliation(s)
- James A. Hay
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Karen Laurie
- WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Seqirus, 63 Poplar Road, Parkville, Victoria, Australia
| | - Michael White
- Malaria: Parasites and Hosts, Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- * E-mail:
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41
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Ainslie KEC, Haber M, Orenstein WA. Challenges in estimating influenza vaccine effectiveness. Expert Rev Vaccines 2019; 18:615-628. [PMID: 31116070 PMCID: PMC6594904 DOI: 10.1080/14760584.2019.1622419] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/20/2019] [Indexed: 12/25/2022]
Abstract
Introduction: Influenza vaccination is regarded as the most effective way to prevent influenza infection. Due to the rapid genetic changes that influenza viruses undergo, seasonal influenza vaccines must be reformulated and re-administered annually necessitating the evaluation of influenza vaccine effectiveness (VE) each year. The estimation of influenza VE presents numerous challenges. Areas Covered: This review aims to identify, discuss, and, where possible, offer suggestions for dealing with the following challenges in estimating influenza VE: different outcomes of interest against which VE is estimated, study designs used to assess VE, sources of bias and confounding, repeat vaccination, waning immunity, population level effects of vaccination, and VE in at-risk populations. Expert Opinion: The estimation of influenza VE has improved with surveillance networks, better understanding of sources of bias and confounding, and the implementation of advanced statistical methods. Future research should focus on better estimates of the indirect effects of vaccination, the biological effects of vaccination, and how vaccines interact with the immune system. Specifically, little is known about how influenza vaccination impacts an individual's infectiousness, how vaccines wane over time, and the impact of repeated vaccination.
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Affiliation(s)
- Kylie E. C. Ainslie
- Research Associate in Influenza Disease Dynamics, MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Michael Haber
- Professor, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA
| | - Walt A. Orenstein
- Professor, Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, 1462 Clifton Rd NE, Atlanta, GA 30322, USA
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Age-specific differences in the dynamics of protective immunity to influenza. Nat Commun 2019; 10:1660. [PMID: 30971703 PMCID: PMC6458119 DOI: 10.1038/s41467-019-09652-6] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 03/22/2019] [Indexed: 11/30/2022] Open
Abstract
Influenza A viruses evolve rapidly to escape host immunity, causing reinfection. The form and duration of protection after each influenza virus infection are poorly understood. We quantify the dynamics of protective immunity by fitting individual-level mechanistic models to longitudinal serology from children and adults. We find that most protection in children but not adults correlates with antibody titers to the hemagglutinin surface protein. Protection against circulating strains wanes to half of peak levels 3.5–7 years after infection in both age groups, and wanes faster against influenza A(H3N2) than A(H1N1)pdm09. Protection against H3N2 lasts longer in adults than in children. Our results suggest that influenza antibody responses shift focus with age from the mutable hemagglutinin head to other epitopes, consistent with the theory of original antigenic sin, and might affect protection. Imprinting, or primary infection with a subtype, has modest to no effect on the risk of non-medically attended infections in adults. Protective immunity after influenza virus infection is poorly understood. Here, the authors quantify the dynamics of immunity against influenza A virus infections by fitting individual-level mechanistic models to longitudinal serology, and find that the form and dynamics of protection differ between children and adults.
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Tsang TK, Ghebremariam SL, Gresh L, Gordon A, Halloran ME, Katzelnick LC, Rojas DP, Kuan G, Balmaseda A, Sugimoto J, Harris E, Longini IM, Yang Y. Effects of infection history on dengue virus infection and pathogenicity. Nat Commun 2019; 10:1246. [PMID: 30886145 PMCID: PMC6423047 DOI: 10.1038/s41467-019-09193-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 02/21/2019] [Indexed: 12/25/2022] Open
Abstract
The understanding of immunological interactions among the four dengue virus (DENV) serotypes and their epidemiological implications is often hampered by the lack of individual-level infection history. Using a statistical framework that infers full infection history, we analyze a prospective pediatric cohort in Nicaragua to characterize how infection history modulates the risks of DENV infection and subsequent clinical disease. After controlling for age, one prior infection is associated with 54% lower, while two or more are associated with 91% higher, risk of a new infection, compared to DENV-naive children. Children >8 years old have 55% and 120% higher risks of infection and subsequent disease, respectively, than their younger peers. Among children with ≥1 prior infection, intermediate antibody titers increase, whereas high titers lower, the risk of subsequent infection, compared with undetectable titers. Such complex dependency needs to be considered in the design of dengue vaccines and vaccination strategies.
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Affiliation(s)
- Tim K Tsang
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, USA
| | - Samson L Ghebremariam
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, USA
| | - Lionel Gresh
- Sustainable Sciences Institute, Managua, 14007, Nicaragua
| | - Aubree Gordon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - M Elizabeth Halloran
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Leah C Katzelnick
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Diana Patricia Rojas
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, USA
| | - Guillermina Kuan
- Centro de Salud Sócrates Flores Vivas, Ministry of Health, Managua, 12014, Nicaragua
| | - Angel Balmaseda
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y Referencia, Ministry of Health, Managua, 16064, Nicaragua
| | - Jonathan Sugimoto
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Eva Harris
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, CA, 94720, USA.
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32610, USA.
| | - Yang Yang
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32611, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32610, USA.
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