1
|
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.
Collapse
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
| |
Collapse
|
2
|
Johnson SS, Jackson KC, Lofgren ET. Impact of Shifting University Policies During the COVID-19 Pandemic on Self-Reported Employee Social Networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.08.24302489. [PMID: 38370812 PMCID: PMC10871445 DOI: 10.1101/2024.02.08.24302489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Objectives To ascertain if faculty and staff were the link between the two COVID-19 outbreaks in a rural university county, and if the local university's COVID-19 policies affected contact rates of their employees across all its campuses. Methods We conducted two anonymous, voluntary online surveys for faculty and staff of a PAC-12 university on their contact patterns both within and outside the university during the COVID-19 pandemic. One was asked when classes were virtual, and another when classes were in-person but masking. Participants were asked about the individuals they encountered, the type and location of the interactions, what COVID-19 precautions were taken - if any, as well as general questions about their location and COVID-19. Results We received 271 responses from the first survey and 124 responses from the second. The first survey had a median of 3 contacts/respondent, with the second having 7 contacts/respondent (p<0.001). During the first survey, most contacts were family contacts (Spouse, Children), with the second survey period having Strangers and Students having the most contact (p<0.001). Over 50% of the first survey contacts happened at their home, while the second survey had 40% at work and 35% at home. Both respondents and contacts masked 42% and 46% of the time for the two surveys respectively (p<0.01). Conclusion For future pandemics, it would be wise to take employees into account when trying to plan for the safety of university students, employees, and surrounding communities. The main places to be aware of and potentially push infectious disease precautions would be on campus, especially confined spaces like offices or small classrooms, and the home, as these tend to be the largest areas of non-masked close contact.
Collapse
Affiliation(s)
- Stephanie S Johnson
- Paul G. Allen School of Global Health, College of Veterinary Medicine, Washington State University, Pullman, WA
| | - Katelin C Jackson
- Paul G. Allen School of Global Health, College of Veterinary Medicine, Washington State University, Pullman, WA
| | - Eric T Lofgren
- Paul G. Allen School of Global Health, College of Veterinary Medicine, Washington State University, Pullman, WA
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Wang J, Yang C, Chen B. The interplay between disease spreading and awareness diffusion in multiplex networks with activity-driven structure. CHAOS (WOODBURY, N.Y.) 2022; 32:073104. [PMID: 35907746 DOI: 10.1063/5.0087404] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
The interplay between disease and awareness has been extensively studied in static networks. However, most networks in reality will evolve over time. Based on this, we propose a novel epidemiological model in multiplex networks. In this model, the disease spreading layer is a time-varying network generated by the activity-driven model, while the awareness diffusion layer is a static network, and the heterogeneity of individual infection and recovery ability is considered. First, we extend the microscopic Markov chain approach to analytically obtain the epidemic threshold of the model. Then, we simulate the spread of disease and find that stronger heterogeneity in the individual activities of a physical layer can promote disease spreading, while stronger heterogeneity of the virtual layer network will hinder the spread of disease. Interestingly, we find that when the individual infection ability follows Gaussian distribution, the heterogeneity of infection ability has little effect on the spread of disease, but it will significantly affect the epidemic threshold when the individual infection ability follows power-law distribution. Finally, we find the emergence of a metacritical point where the diffusion of awareness is able to control the onset of the epidemics. Our research could cast some light on exploring the dynamics of epidemic spreading in time-varying multiplex networks.
Collapse
Affiliation(s)
- Jiaxin Wang
- School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chun Yang
- School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Chen
- School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China
| |
Collapse
|
5
|
Read JM, Zimmer S, Vukotich C, Schweizer ML, Galloway D, Lingle C, Yearwood G, Calderone P, Noble E, Quadelacy T, Grantz K, Rinaldo C, Gao H, Rainey J, Uzicanin A, Cummings DAT. Influenza and other respiratory viral infections associated with absence from school among schoolchildren in Pittsburgh, Pennsylvania, USA: a cohort study. BMC Infect Dis 2021; 21:291. [PMID: 33752625 PMCID: PMC7983083 DOI: 10.1186/s12879-021-05922-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 02/18/2021] [Indexed: 11/22/2022] Open
Abstract
Background Information on the etiology and age-specific burden of respiratory viral infections among school-aged children remains limited. Though school aged children are often recognized as driving the transmission of influenza as well as other respiratory viruses, little detailed information is available on the distribution of respiratory infections among children of different ages within this group. Factors other than age including gender and time spent in school may also be important in determining risk of infection but have been little studied in this age group. Methods We conducted a cohort study to determine the etiology of influenza like illness (ILI) among 2519 K–12 students during the 2012–13 influenza season. We obtained nasal swabs from students with ILI-related absences. Generalized linear mixed-effect regressions determined associations of outcomes, including ILI and laboratory-confirmed respiratory virus infection, with school grade and other covariates. Results Overall, 459 swabs were obtained from 552 ILI–related absences. Respiratory viruses were found in 292 (63.6%) samples. Influenza was found in 189 (41.2%) samples. With influenza B found in 134 (70.9%). Rates of influenza B were significantly higher in grades 1 (10.1, 95% CI 6.8–14.4%), 2 (9.7, 6.6–13.6%), 3 (9.3, 6.3–13.2%), and 4 (9.9, 6.8–13.8%) than in kindergarteners (3.2, 1.5–6.0%). After accounting for grade, sex and self-reported vaccination status, influenza B infection risk was lower among kindergarteners in half-day programs compared to kindergarteners in full-day programs (OR = 0.19; 95% CI 0.08–0.45). Conclusions ILI and influenza infection is concentrated in younger schoolchildren. Reduced infection by respiratory viruses is associated with a truncated school day for kindergarteners but this finding requires further investigation in other grades and populations. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-05922-1.
Collapse
Affiliation(s)
- Jonathan M Read
- Center for Health Informatics Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK.,Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| | - Shanta Zimmer
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,Department of Medicine, University of Colorado School of Medicine, Denver, CO, USA
| | - Charles Vukotich
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Mary Lou Schweizer
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David Galloway
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Carrie Lingle
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.,Toledo Lucas County Health Department, Toledo, OH, USA
| | - Gaby Yearwood
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Patti Calderone
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eva Noble
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Talia Quadelacy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kyra Grantz
- Department of Biology, University of Florida, Gainesville, FL, USA.,University of Florida, Emerging Pathogens Institute, Gainesville, FL, USA
| | - Charles Rinaldo
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Hongjiang Gao
- Department of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jeanette Rainey
- Department of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Amra Uzicanin
- Department of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Derek A T Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. .,Department of Biology, University of Florida, Gainesville, FL, USA. .,University of Florida, Emerging Pathogens Institute, Gainesville, FL, USA.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Kucharski AJ, Lessler J, Cummings DAT, Riley S. Timescales of influenza A/H3N2 antibody dynamics. PLoS Biol 2018; 16:e2004974. [PMID: 30125272 PMCID: PMC6117086 DOI: 10.1371/journal.pbio.2004974] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 08/30/2018] [Accepted: 08/07/2018] [Indexed: 11/18/2022] Open
Abstract
Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants' histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.
Collapse
Affiliation(s)
- Adam J. Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Derek A. T. Cummings
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| |
Collapse
|
9
|
Affiliation(s)
- Adam J. Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Marc Baguelin
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Respiratory Diseases Department, Public Health England, London, United Kingdom
| |
Collapse
|
10
|
Jiang CQ, Lessler J, Kim L, Kwok KO, Read JM, Wang S, Tan L, Hast M, Zhu H, Guan Y, Riley S, Cummings DAT. Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study. Int J Epidemiol 2017; 46:e16. [PMID: 26875566 PMCID: PMC6251537 DOI: 10.1093/ije/dyv353] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2015] [Indexed: 02/05/2023] Open
Affiliation(s)
| | - Justin Lessler
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Lina Kim
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kin On Kwok
- School of Public Health, University of Hong Kong, Hong Kong, China
| | - Jonathan M Read
- Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| | - Shuying Wang
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
| | - Lijiu Tan
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
| | - Marisa Hast
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Huachen Zhu
- School of Public Health, University of Hong Kong, Hong Kong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Yi Guan
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
- Shantou University Medical College, Shantou, Guangdong, China
| | - Steven Riley
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
- School of Public Health, Imperial College London, London, UK and
| | - Derek AT Cummings
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- College of Liberal Arts and Sciences, University of Florida, Gainesville, FL, USA
- *Corresponding author. University of Florida. College of Liberal Arts and Sciences, 220 Bartram Hall, Gainesville, FL, USA. E-mail:
| |
Collapse
|
11
|
Evidence for history-dependence of influenza pandemic emergence. Sci Rep 2017; 7:43623. [PMID: 28252671 PMCID: PMC5333635 DOI: 10.1038/srep43623] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 01/26/2017] [Indexed: 12/04/2022] Open
Abstract
Influenza A viruses have caused a number of global pandemics, with considerable mortality in humans. Here, we analyse the time periods between influenza pandemics since 1700 under different assumptions to determine whether the emergence of new pandemic strains is a memoryless or history-dependent process. Bayesian model selection between exponential and gamma distributions for these time periods gives support to the hypothesis of history-dependence under eight out of nine sets of modelling assumptions. Using the fitted parameters to make predictions shows a high level of variability in the modelled number of pandemics from 2010–2110. The approach we take here relies on limited data, so is uncertain, but it provides cheap, safe and direct evidence relating to pandemic emergence, a field where indirect measurements are often made at great risk and cost.
Collapse
|
12
|
Tamerius J, Steadman J, Tamerius J. Synchronicity of influenza activity within Phoenix, AZ during the 2015-2016 seasonal epidemic. BMC Infect Dis 2017; 17:109. [PMID: 28143437 PMCID: PMC5286821 DOI: 10.1186/s12879-017-2197-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 01/09/2017] [Indexed: 11/18/2022] Open
Abstract
Background Variability in the timing of influenza epidemics has been observed across global and regional scales, but this variability has not been studied extensively at finer spatial scales. As such, the aim of this study was to test whether influenza cases were synchronized across sites and/or age-groups within a major city. Methods We used influenza cases identified by rapid influenza tests from a network of clinics across Phoenix, AZ during the 2015–2016 influenza A season. We used a combination of KS tests and a bootstrapping approach to evaluate whether the temporal distribution of cases varied by site and/or age group. Results Our analysis indicates that the timing of influenza cases during the 2015–2016 seasonal influenza epidemic were generally synchronized across sites and age groups. That said, we did observe some statistically significant differences in the timing of cases across some sites, and by site and age group. We found no evidence that influenza activity consistently begins or peaks earlier in children than in adults. Conclusions To our knowledge, this is the first study to investigate differences in the intra-urban timing of influenza using influenza-specific case data. We were able to show evidence that influenza cases are not entirely synchronized across an urban area, but the differences we observed were relatively minor. It is important to understand the geographic scale at which influenza is synchronized in order to gain a better understanding of local transmission dynamics, and to determine the appropriate geographic scale that influenza surveillance data should be aggregated for prediction and warning systems.
Collapse
Affiliation(s)
- James Tamerius
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, USA.
| | | | | |
Collapse
|
13
|
Truelove S, Zhu H, Lessler J, Riley S, Read JM, Wang S, Kwok KO, Guan Y, Jiang CQ, Cummings DAT. A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A. Influenza Other Respir Viruses 2016; 10:518-524. [PMID: 27406695 PMCID: PMC5059953 DOI: 10.1111/irv.12408] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2016] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Serum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses. METHODS A total of 151 study participants from near Guangzhou, China, were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results. RESULTS We observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, ρ=.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (>80%) for predicting NT with HI. CONCLUSIONS While we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, although attention should be given to the effect of age on these assays.
Collapse
Affiliation(s)
- Shaun Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Huachen Zhu
- State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong SAR, China
- 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 SAR, China
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Steven Riley
- School of Public Health, Imperial College, London, UK
| | - Jonathan M Read
- Department of Epidemiology and Public Health, Institute of Infection and Global Health, University of Liverpool, Neston, UK
| | - Shuying Wang
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
| | - Kin On Kwok
- 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 SAR, China
| | - Yi Guan
- State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong SAR, China
- 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 SAR, China
| | | | - Derek A T Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biology, University of Florida, Gainesville, FL, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
| |
Collapse
|
14
|
Wu C, Wang MH, Lu X, Chong KC, He J, Yau CY, Hui M, Cheng X, Yang L, Zee BCY, Zhang R, He ML. Concurrent epidemics of influenza A/H3N2 and A/H1N1pdm in Southern China: A serial cross-sectional study. J Infect 2015; 72:369-76. [PMID: 26747013 DOI: 10.1016/j.jinf.2015.12.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Revised: 12/25/2015] [Accepted: 12/26/2015] [Indexed: 02/05/2023]
Abstract
OBJECTIVES This study aimed to elucidate the antibody response pattern of multiple influenza subtypes through a 4-year serological study of a general population in Shenzhen, Southern China. METHODS A serial cross-sectional serological survey was conducted at eight time points between 2009 and 2012. A total number of 5876 subjects were recruited from all age groups. The influenza subtypes tested were A/H1N1, A/H3N2, B/Yamagata, B/Victoria, and A/H1N1pdm. Genetic sequencing and phylogenetic analysis were performed on 127 H3 genes and 28 H1pdm genes. RESULTS We found concurrent epidemics of A/H3N2 and A/H1N1pdm with simultaneous peak times at March 2011. A/H3N2 was the dominant subtype. Ten residue substitutions (S61N, T64I, K78E, K160N, N161S, A214S, T228A, A229V, V239I, N294K, and N328S) were found in the H3 gene that might underlie its epidemic. The elderly group showed an antibody response cycle that was weaker in magnitude and slower in peak time than in younger groups. CONCLUSIONS The study provides a broad transmission picture and epidemiological characteristics of the major flu subtypes. The findings suggest that it may be necessary to include the A/H1N1pdm strain to the current trivalent or quadrivalent vaccine design. The delayed antibody response cycle in the elderly group indicates the need for better protection of elderly people that might be achieved by an earlier vaccination at a higher dose.
Collapse
Affiliation(s)
- Chunli Wu
- Major Infectious Disease Control Key Laboratory, The Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Maggie Haitian Wang
- Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China; The CUHK Shenzhen Research Institute, Shenzhen, China
| | - Xing Lu
- Major Infectious Disease Control Key Laboratory, The Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Ka Chun Chong
- Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China; The CUHK Shenzhen Research Institute, Shenzhen, China
| | - Jason He
- College of Letter and Science, University of California at Berkeley, CA, USA
| | - Chun-Yip Yau
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mark Hui
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xiaowen Cheng
- Major Infectious Disease Control Key Laboratory, The Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Li Yang
- Division of Digestive Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Benny Chung-Ying Zee
- Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China; The CUHK Shenzhen Research Institute, Shenzhen, China
| | - Renli Zhang
- Major Infectious Disease Control Key Laboratory, The Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
| | - Ming-Liang He
- The CUHK Shenzhen Research Institute, Shenzhen, China; Stanley Ho Center for Emerging Infectious Diseases, and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, China; Department of Biomedical Science, The City University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
15
|
Wang Z, Andrews MA, Wu ZX, Wang L, Bauch CT. Coupled disease-behavior dynamics on complex networks: A review. Phys Life Rev 2015; 15:1-29. [PMID: 26211717 PMCID: PMC7105224 DOI: 10.1016/j.plrev.2015.07.006] [Citation(s) in RCA: 163] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 06/24/2015] [Accepted: 06/25/2015] [Indexed: 01/30/2023]
Abstract
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.
Collapse
Affiliation(s)
- Zhen Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan.
| | - Michael A Andrews
- Department of Mathematics and Statistics, University of Guelph, Guelph, ON, N1G 2W1, Canada.
| | - Zhi-Xi Wu
- Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, Gansu 730000, China.
| | - Lin Wang
- School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China.
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
| |
Collapse
|
16
|
Kucharski AJ, Andreasen V, Gog JR. Capturing the dynamics of pathogens with many strains. J Math Biol 2015; 72:1-24. [PMID: 25800537 PMCID: PMC4698306 DOI: 10.1007/s00285-015-0873-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Revised: 03/05/2015] [Indexed: 12/20/2022]
Abstract
Pathogens that consist of multiple antigenic variants are a serious public health concern. These infections, which include dengue virus, influenza and malaria, generate substantial morbidity and mortality. However, there are considerable theoretical challenges involved in modelling such infections. As well as describing the interaction between strains that occurs as a result cross-immunity and evolution, models must balance biological realism with mathematical and computational tractability. Here we review different modelling approaches, and suggest a number of biological problems that are potential candidates for study with these methods. We provide a comprehensive outline of the benefits and disadvantages of available frameworks, and describe what biological information is preserved and lost under different modelling assumptions. We also consider the emergence of new disease strains, and discuss how models of pathogens with multiple strains could be developed further in future. This includes extending the flexibility and biological realism of current approaches, as well as interface with data.
Collapse
Affiliation(s)
- Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Viggo Andreasen
- Department of Mathematics and Physics, Roskilde University, 4000, Roskilde, Denmark
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK
| |
Collapse
|
17
|
Vinh DN, Boni MF. Statistical identifiability and sample size calculations for serial seroepidemiology. Epidemics 2015; 12:30-9. [PMID: 26342240 PMCID: PMC4558460 DOI: 10.1016/j.epidem.2015.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2014] [Revised: 02/12/2015] [Accepted: 02/24/2015] [Indexed: 11/30/2022] Open
Abstract
We investigate whether disease dynamics can be inferred by repeated serum collections. Measuring antibody waning is critical for inference in serological time series. Collecting 200 samples every 2 months allows for inference of transmission parameters. Low-level seasonality is difficult to detect statistically.
Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease. The inferred dynamics will vary depending on the reporting patterns and surveillance system for the disease in question, and the inference will miss mild or underreported epidemics. To eliminate the variation introduced by differing reporting patterns and to capture asymptomatic or subclinical infection, inferential methods can be applied to serological data sets instead of case reporting data. To reconstruct complete disease dynamics, one would need to collect a serological time series. In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum. We refer to this study design as a serial seroepidemiology (SSE) design, and we base the analysis on our epidemiological knowledge of influenza. We consider a study duration of three to four years, during which a single antigenic type of influenza would be circulating, and we evaluate our ability to reconstruct disease dynamics based on serological data alone. We show that the processes of reinfection, antibody generation, and antibody waning confound each other and are not always statistically identifiable, especially when dynamics resemble a non-oscillating endemic equilibrium behavior. We introduce some constraints to partially resolve this confounding, and we show that transmission rates and basic reproduction numbers can be accurately estimated in SSE study designs. Seasonal forcing is more difficult to identify as serology-based studies only detect oscillations in antibody titers of recovered individuals, and these oscillations are typically weaker than those observed for infected individuals. To accurately estimate the magnitude and timing of seasonal forcing, serum samples should be collected every two months and 200 or more samples should be included in each collection; this sample size estimate is sensitive to the antibody waning rate and the assumed level of seasonal forcing.
Collapse
Affiliation(s)
- Dao Nguyen Vinh
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Viet Nam
| | - Maciej F Boni
- Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Viet Nam; Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
| |
Collapse
|
18
|
Kucharski AJ, Lessler J, Read JM, Zhu H, Jiang CQ, Guan Y, Cummings DAT, Riley S. Estimating the life course of influenza A(H3N2) antibody responses from cross-sectional data. PLoS Biol 2015; 13:e1002082. [PMID: 25734701 PMCID: PMC4348415 DOI: 10.1371/journal.pbio.1002082] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 01/16/2015] [Indexed: 02/05/2023] Open
Abstract
The immunity of a host population against specific influenza A strains can influence a number of important biological processes, from the emergence of new virus strains to the effectiveness of vaccination programmes. However, the development of an individual's long-lived antibody response to influenza A over the course of a lifetime remains poorly understood. Accurately describing this immunological process requires a fundamental understanding of how the mechanisms of boosting and cross-reactivity respond to repeated infections. Establishing the contribution of such mechanisms to antibody titres remains challenging because the aggregate effect of immune responses over a lifetime are rarely observed directly. To uncover the aggregate effect of multiple influenza infections, we developed a mechanistic model capturing both past infections and subsequent antibody responses. We estimated parameters of the model using cross-sectional antibody titres to nine different strains spanning 40 years of circulation of influenza A(H3N2) in southern China. We found that "antigenic seniority" and quickly decaying cross-reactivity were important components of the immune response, suggesting that the order in which individuals were infected with influenza strains shaped observed neutralisation titres to a particular virus. We also obtained estimates of the frequency and age distribution of influenza infection, which indicate that although infections became less frequent as individuals progressed through childhood and young adulthood, they occurred at similar rates for individuals above age 30 y. By establishing what are likely to be important mechanisms driving epochal trends in population immunity, we also identified key directions for future studies. In particular, our results highlight the need for longitudinal samples that are tested against multiple historical strains. This could lead to a better understanding of how, over the course of a lifetime, fast, transient antibody dynamics combine with the longer-term immune responses considered here.
Collapse
Affiliation(s)
- Adam J. Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- * E-mail:
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Jonathan M. Read
- Department of Epidemiology and Population Health, Institute of Infection and Global Health, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Huachen Zhu
- International Institute of Infection and Immunity, Shantou University Medical College, Shantou, Guangdong, China
- State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, University of Hong Kong, Hong Kong SAR, China
| | | | - Yi Guan
- International Institute of Infection and Immunity, Shantou University Medical College, Shantou, Guangdong, China
- State Key Laboratory of Emerging Infectious Diseases and Centre of Influenza Research, University of Hong Kong, Hong Kong SAR, China
| | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| |
Collapse
|
19
|
Gong YW, Song YR, Jiang GP. Epidemic spreading in metapopulation networks with heterogeneous infection rates. PHYSICA A 2014; 416:208-218. [PMID: 32288090 PMCID: PMC7125748 DOI: 10.1016/j.physa.2014.08.056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2014] [Revised: 07/20/2014] [Indexed: 05/31/2023]
Abstract
In this paper, we study epidemic spreading in metapopulation networks wherein each node represents a subpopulation symbolizing a city or an urban area and links connecting nodes correspond to the human traveling routes among cities. Differently from previous studies, we introduce a heterogeneous infection rate to characterize the effect of nodes' local properties, such as population density, individual health habits, and social conditions, on epidemic infectivity. By means of a mean-field approach and Monte Carlo simulations, we explore how the heterogeneity of the infection rate affects the epidemic dynamics, and find that large fluctuations of the infection rate have a profound impact on the epidemic threshold as well as the temporal behavior of the prevalence above the epidemic threshold. This work can refine our understanding of epidemic spreading in metapopulation networks with the effect of nodes' local properties.
Collapse
Affiliation(s)
- Yong-Wang Gong
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- School of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China
| | - Yu-Rong Song
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Guo-Ping Jiang
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| |
Collapse
|
20
|
Cauchemez S, Ferguson NM, Fox A, Mai LQ, Thanh LT, Thai PQ, Thoang DD, Duong TN, Minh Hoa LN, Tran Hien N, Horby P. Determinants of influenza transmission in South East Asia: insights from a household cohort study in Vietnam. PLoS Pathog 2014; 10:e1004310. [PMID: 25144780 PMCID: PMC4140851 DOI: 10.1371/journal.ppat.1004310] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 06/30/2014] [Indexed: 11/18/2022] Open
Abstract
To guide control policies, it is important that the determinants of influenza transmission are fully characterized. Such assessment is complex because the risk of influenza infection is multifaceted and depends both on immunity acquired naturally or via vaccination and on the individual level of exposure to influenza in the community or in the household. Here, we analyse a large household cohort study conducted in 2007–2010 in Vietnam using innovative statistical methods to ascertain in an integrative framework the relative contribution of variables that influence the transmission of seasonal (H1N1, H3N2, B) and pandemic H1N1pdm09 influenza. Influenza infection was diagnosed by haemagglutination-inhibition (HI) antibody assay of paired serum samples. We used a Bayesian data augmentation Markov chain Monte Carlo strategy based on digraphs to reconstruct unobserved chains of transmission in households and estimate transmission parameters. The probability of transmission from an infected individual to another household member was 8% (95% CI, 6%, 10%) on average, and varied with pre-season titers, age and household size. Within households of size 3, the probability of transmission from an infected member to a child with low pre-season HI antibody titers was 27% (95% CI 21%–35%). High pre-season HI titers were protective against infection, with a reduction in the hazard of infection of 59% (95% CI, 44%–71%) and 87% (95% CI, 70%–96%) for intermediate (1∶20–1∶40) and high (≥1∶80) HI titers, respectively. Even after correcting for pre-season HI titers, adults had half the infection risk of children. Twenty six percent (95% CI: 21%, 30%) of infections may be attributed to household transmission. Our results highlight the importance of integrated analysis by influenza sub-type, age and pre-season HI titers in order to infer influenza transmission risks in and outside of the household. Influenza causes an estimated three to five million severe illnesses worldwide each year. In order to guide control policies it is important to determine the key risk factors for transmission. This is often done by studying transmission in households but in the past, analysis of such data has suffered from important simplifying assumptions (for example not being able to account for the effect of biological markers of protection like pre-season antibody titers). We have developed new statistical methods that address these limitations and applied them to a large household cohort study conducted in 2007–2010 in Vietnam. By comparing a large range of model variants, we have obtained unique insights into the patterns and determinants of transmission of seasonal (H1N1, H3N2, B) and pandemic H1N1pdm09 influenza in South East Asia. This includes estimating the proportion of cases attributed to household transmission, the average household transmission probability, the protection afforded by pre-season HI titers, and the effect of age on infection risk after correcting for pre-season HI titers.
Collapse
Affiliation(s)
- Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- * E-mail:
| | - Neil M. Ferguson
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Annette Fox
- Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Hanoi, Vietnam
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia
| | - Le Quynh Mai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Le Thi Thanh
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | | | - Tran Nhu Duong
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Le Nguyen Minh Hoa
- Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Hanoi, Vietnam
| | | | - Peter Horby
- Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Hanoi, Vietnam
| |
Collapse
|
21
|
Wang L, Li X. Spatial epidemiology of networked metapopulation: an overview. CHINESE SCIENCE BULLETIN-CHINESE 2014; 59:3511-3522. [PMID: 32214746 PMCID: PMC7088704 DOI: 10.1007/s11434-014-0499-8] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 03/21/2014] [Indexed: 12/05/2022]
Abstract
An emerging disease is one infectious epidemic caused by a newly transmissible pathogen, which has either appeared for the first time or already existed in human populations, having the capacity to increase rapidly in incidence as well as geographic range. Adapting to human immune system, emerging diseases may trigger large-scale pandemic spreading, such as the transnational spreading of SARS, the global outbreak of A(H1N1), and the recent potential invasion of avian influenza A(H7N9). To study the dynamics mediating the transmission of emerging diseases, spatial epidemiology of networked metapopulation provides a valuable modeling framework, which takes spatially distributed factors into consideration. This review elaborates the latest progresses on the spatial metapopulation dynamics, discusses empirical and theoretical findings that verify the validity of networked metapopulations, and the sketches application in evaluating the effectiveness of disease intervention strategies as well.
Collapse
Affiliation(s)
- Lin Wang
- 1Adaptive Networks and Control Laboratory, Department of Electronic Engineering, Fudan University, Shanghai, 200433 China
- 2Centre for Chaos and Complex Networks, Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China
- 3Present Address: School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Xiang Li
- 1Adaptive Networks and Control Laboratory, Department of Electronic Engineering, Fudan University, Shanghai, 200433 China
| |
Collapse
|
22
|
Read JM, Lessler J, Riley S, Wang S, Tan LJ, Kwok KO, Guan Y, Jiang CQ, Cummings DAT. Social mixing patterns in rural and urban areas of southern China. Proc Biol Sci 2014; 281:20140268. [PMID: 24789897 PMCID: PMC4024290 DOI: 10.1098/rspb.2014.0268] [Citation(s) in RCA: 107] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Accepted: 03/25/2014] [Indexed: 02/05/2023] Open
Abstract
A dense population, global connectivity and frequent human-animal interaction give southern China an important role in the spread and emergence of infectious disease. However, patterns of person-to-person contact relevant to the spread of directly transmitted infections such as influenza remain poorly quantified in the region. We conducted a household-based survey of travel and contact patterns among urban and rural populations of Guangdong, China. We measured the character and distance from home of social encounters made by 1821 individuals. Most individuals reported 5-10 h of contact with around 10 individuals each day; however, both distributions have long tails. The distribution of distance from home at which contacts were made is similar: most were within a kilometre of the participant's home, while some occurred further than 500 km away. Compared with younger individuals, older individuals made fewer contacts which tended to be closer to home. There was strong assortativity in age-based contact rates. We found no difference between the total number or duration of contacts between urban and rural participants, but urban participants tended to make contacts closer to home. These results can improve mathematical models of infectious disease emergence, spread and control in southern China and throughout the region.
Collapse
Affiliation(s)
- Jonathan M. Read
- Department of Epidemiology and Public Health, Institute of Infection and Global Health, University of Liverpool, Leahurst Campus, Neston CH64 7TE, UK
- e-mail:
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Steven Riley
- School of Public Health, Imperial College, London, UK
| | - Shuying Wang
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong 510620, People's Republic of China
| | - Li Jiu Tan
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong 510620, People's Republic of China
| | - Kin On Kwok
- School of Public Health, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Yi Guan
- Department of Microbiology, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- International Institute of Infection and Immunity, Shantou University Medical College, Shantou, Guangdong 515031, People's Republic of China
| | - Chao Qiang Jiang
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong 510620, People's Republic of China
| | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| |
Collapse
|
23
|
How human location-specific contact patterns impact spatial transmission between populations? Sci Rep 2013; 3:1468. [PMID: 23511929 PMCID: PMC3601479 DOI: 10.1038/srep01468] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 03/01/2013] [Indexed: 11/16/2022] Open
Abstract
The structured-population model has been widely used to study the spatial transmission of epidemics in human society. Many seminal works have demonstrated the impact of human mobility on the epidemic threshold, assuming that the contact pattern of individuals is mixing homogeneously. Inspired by the recent evidence of location-related factors in reality, we introduce two categories of location-specific heterogeneous human contact patterns into a phenomenological model based on the commuting and contagion processes, which significantly decrease the epidemic threshold and thus favor the outbreak of diseases. In more detail, we find that a monotonic mode presents for the variance of disease prevalence in dependence on the contact rates under the destination-driven contact scenario; while under the origin-driven scenario, enhancing the contact rate counterintuitively weakens the disease prevalence in some parametric regimes. The inclusion of heterogeneity of human contacts is expected to provide valuable support to public health implications.
Collapse
|
24
|
White LF, Archer B, Pagano M. Estimating the reproductive number in the presence of spatial heterogeneity of transmission patterns. Int J Health Geogr 2013; 12:35. [PMID: 23890514 PMCID: PMC3735474 DOI: 10.1186/1476-072x-12-35] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 07/11/2013] [Indexed: 12/03/2022] Open
Abstract
Background Estimates of parameters for disease transmission in large-scale infectious disease outbreaks are often obtained to represent large groups of people, providing an average over a potentially very diverse area. For control measures to be more effective, a measure of the heterogeneity of the parameters is desirable. Methods We propose a novel extension of a network-based approach to estimating the reproductive number. With this we can incorporate spatial and/or demographic information through a similarity matrix. We apply this to the 2009 Influenza pandemic in South Africa to understand the spatial variability across provinces. We explore the use of five similarity matrices to illustrate their impact on the subsequent epidemic parameter estimates. Results When treating South Africa as a single entity with homogeneous transmission characteristics across the country, the basic reproductive number, R0, (and imputation range) is 1.33 (1.31, 1.36). When fitting a new model for each province with no inter-province connections this estimate varies little (1.23-1.37). Using the proposed method with any of the four similarity measures yields an overall R0 that varies little across the four new models (1.33 to 1.34). However, when allowed to vary across provinces, the estimated R0 is greater than one consistently in only two of the nine provinces, the most densely populated provinces of Gauteng and Western Cape. Conclusions Our results suggest that the spatial heterogeneity of influenza transmission was compelling in South Africa during the 2009 pandemic. This variability makes a qualitative difference in our understanding of the epidemic. While the cause of this fluctuation might be partially due to reporting differences, there is substantial evidence to warrant further investigation.
Collapse
Affiliation(s)
- Laura F White
- Department of Biostatistics, Boston University School of Public Health, 801 Massachussetts Ave, Boston, MA 02118, USA.
| | | | | |
Collapse
|
25
|
Van Kerkhove MD, Broberg E, Engelhardt OG, Wood J, Nicoll A. The consortium for the standardization of influenza seroepidemiology (CONSISE): a global partnership to standardize influenza seroepidemiology and develop influenza investigation protocols to inform public health policy. Influenza Other Respir Viruses 2013; 7:231-4. [PMID: 23280042 PMCID: PMC5779825 DOI: 10.1111/irv.12068] [Citation(s) in RCA: 27] [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] [Accepted: 11/15/2012] [Indexed: 12/03/2022] Open
Abstract
CONSISE - The consortium for the Standardization of Influenza Seroepidemiology - is a global partnership to develop influenza investigation protocols and standardize seroepidemiology to inform health policy. This international partnership was formed in 2011 and was created out of a need, identified during the 2009 H1N1 pandemic, for timely seroepidemiological data to better estimate pandemic virus infection severity and attack rates to inform policy decisions. CONSISE has developed into a consortium of two interactive working groups: epidemiology and laboratory, with a steering committee composed of individuals from several organizations. CONSISE has had two international meetings with more planned for 2013. We seek additional members from public health agencies, academic institutions and other interested parties.
Collapse
Affiliation(s)
- Maria D Van Kerkhove
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | | | | | | | | |
Collapse
|
26
|
Kucharski AJ, Gog JR. The role of social contacts and original antigenic sin in shaping the age pattern of immunity to seasonal influenza. PLoS Comput Biol 2012; 8:e1002741. [PMID: 23133346 PMCID: PMC3486889 DOI: 10.1371/journal.pcbi.1002741] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 08/31/2012] [Indexed: 11/23/2022] Open
Abstract
Recent serological studies of seasonal influenza A in humans suggest a striking characteristic profile of immunity against age, which holds across different countries and against different subtypes of influenza. For both H1N1 and H3N2, the proportion of the population seropositive to recently circulated strains peaks in school-age children, reaches a minimum between ages 35–65, then rises again in the older ages. This pattern is little understood. Variable mixing between different age classes can have a profound effect on disease dynamics, and is hence the obvious candidate explanation for the profile, but using a mathematical model of multiple influenza strains, we see that age dependent transmission based on mixing data from social contact surveys cannot on its own explain the observed pattern. Instead, the number of seropositive individuals in a population may be a consequence of ‘original antigenic sin’; if the first infection of a lifetime dominates subsequent immune responses, we demonstrate that it is possible to reproduce the observed relationship between age and seroprevalence. We propose a candidate mechanism for this relationship, by which original antigenic sin, along with antigenic drift and vaccination, results in the age profile of immunity seen in empirical studies. The way in which a population builds immunity to influenza affects outbreak size and the emergence of new strains. However, although age-specific immunity has been widely discussed for the 2009 influenza pandemic, the age profile of immunity to seasonal influenza remains little understood. In contrast to many infections, the proportion of people immune to recent strains peaks in school-age children then reaches a minimum between ages 35–65, before rising again in older age groups. Our results suggest that rather than variable mixing between different age groups being solely responsible, the pattern may be shaped by an effect known as ‘original antigenic sin’, by which the first infection of a lifetime dictates subsequent immune responses: instead of developing antibodies to every new virus that is encountered, the immune system may reuse the response to a similar virus it has already seen. The framework we describe, which extends theoretical models to allow for comparison with data, also opens the possibility of investigating the mechanisms behind patterns of immunity to other evolving pathogens.
Collapse
Affiliation(s)
- Adam J Kucharski
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
| | | |
Collapse
|
27
|
Lessler J, Riley S, Read JM, Wang S, Zhu H, Smith GJD, Guan Y, Jiang CQ, Cummings DAT. Evidence for antigenic seniority in influenza A (H3N2) antibody responses in southern China. PLoS Pathog 2012; 8:e1002802. [PMID: 22829765 PMCID: PMC3400560 DOI: 10.1371/journal.ppat.1002802] [Citation(s) in RCA: 157] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2011] [Accepted: 06/01/2012] [Indexed: 11/19/2022] Open
Abstract
A key observation about the human immune response to repeated exposure to influenza A is that the first strain infecting an individual apparently produces the strongest adaptive immune response. Although antibody titers measure that response, the interpretation of titers to multiple strains – from the same sera – in terms of infection history is clouded by age effects, cross reactivity and immune waning. From July to September 2009, we collected serum samples from 151 residents of Guangdong Province, China, 7 to 81 years of age. Neutralization tests were performed against strains representing six antigenic clusters of H3N2 influenza circulating between 1968 and 2008, and three recent locally circulating strains. Patterns of neutralization titers were compared based on age at time of testing and age at time of the first isolation of each virus. Neutralization titers were highest for H3N2 strains that circulated in an individual's first decade of life (peaking at 7 years). Further, across strains and ages at testing, statistical models strongly supported a pattern of titers declining smoothly with age at the time a strain was first isolated. Those born 10 or more years after a strain emerged generally had undetectable neutralization titers to that strain (<1∶10). Among those over 60 at time of testing, titers tended to increase with age. The observed pattern in H3N2 neutralization titers can be characterized as one of antigenic seniority: repeated exposure and the immune response combine to produce antibody titers that are higher to more ‘senior’ strains encountered earlier in life. The human immune response to an influenza infection is not the same for every infection. It has often been observed that we tend to have the highest antibody titer (and presumably our strongest immune response) against strains of influenza that we were exposed to early in life. In this study, we obtained blood samples from 151 people between 7 and 81 years of age and tested the samples for the concentration of antibodies to many different (H3N2) strains. We chose strains according to when they first circulated, starting with a strain isolated just after the 1968 pandemic and going all the way through to very recent strains. We found that a participant's age at the time a strain first circulated was very predictive of the strength of their antibody against that strain. Not just for the first strain they were likely to have seen, but also for the second, third and all subsequent strains circulating during their lifetime. This suggests to us that antibody titers to influenza A H3N2 follow a pattern of antigenic seniority, suggesting that we produce progressively fewer specific antibodies to each subsequent infection as we age.
Collapse
Affiliation(s)
- Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Steven Riley
- School of Public Health, Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College, London, United Kingdom
- * E-mail:
| | - Jonathan M. Read
- Department of Epidemiology and Population Health, Institute of Infection and Global Health, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Shuying Wang
- Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China
| | - Huachen Zhu
- International Institute of Infection and Immunity, Shantou University Medical College, Shantou, Guangdong, China
- Department of Microbiology, The University of Hong Kong, Hong Kong SAR, China
| | - Gavin J. D. Smith
- Laboratory of Virus Evolution, Program in Emerging Infectious Diseases, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Yi Guan
- International Institute of Infection and Immunity, Shantou University Medical College, Shantou, Guangdong, China
- Department of Microbiology, The University of Hong Kong, Hong Kong SAR, China
| | | | - Derek A. T. Cummings
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| |
Collapse
|