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
|
Bolton KJ, McCaw JM, Dafilis MP, McVernon J, Heffernan JM. Seasonality as a driver of pH1N12009 influenza vaccination campaign impact. Epidemics 2023; 45:100730. [PMID: 38056164 DOI: 10.1016/j.epidem.2023.100730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/18/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.
Collapse
Affiliation(s)
- Kirsty J Bolton
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
| | - James M McCaw
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Mathew P Dafilis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Jodie McVernon
- Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Parkville, Australia
| | - Jane M Heffernan
- Centre for Disease Modelling, Mathematics & Statistics, York University, Canada
| |
Collapse
|
3
|
Goldstein E. Mortality associated with Omicron and influenza infections in France before and during the COVID-19 pandemic. Epidemiol Infect 2023; 151:e148. [PMID: 37622317 PMCID: PMC10540177 DOI: 10.1017/s0950268823001358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/30/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
For many deaths associated with influenza and Omicron infections, those viruses are not detected. We applied previously developed methodology to estimate the contribution of influenza and Omicron infections to all-cause mortality in France for the 2014-2015 through the 2018-2019 influenza seasons, and the period between week 33, 2022 and week 12, 2023. For the 2014-2015 through the 2018-2019 seasons, influenza was associated with annual average of 15,654 (95% CI (13,013, 18,340)) deaths, while between week 33, 2022 and week 12, 2023, we estimated 7,851 (5,213, 10,463) influenza-associated deaths and 32,607 (20,794, 44,496) SARS-CoV-2 associated deaths. For many Omicron-associated deaths for cardiac disease, mental&behavioural disorders, and other causes, Omicron infections are not characterised as a contributing cause of death - for example, between weeks 33-52 in 2022, we estimated 23,983 (15,307, 32,620) SARS-CoV-2-associated deaths in France, compared with 12,811 deaths with COVID-19 listed on death certificate. Our results suggest the need for boosting influenza vaccination coverage in different population groups in France, and for wider detection of influenza infections in respiratory illness episodes (including pneumonia) in combination with the use of antiviral medications. For Omicron epidemics, wider detection of Omicron infections in persons with underlying health conditions is needed.
Collapse
Affiliation(s)
- Edward Goldstein
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
4
|
Tegally H, Khan K, Huber C, de Oliveira T, Kraemer MUG. Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves. J Travel Med 2022; 29:taac134. [PMID: 36367200 PMCID: PMC9793401 DOI: 10.1093/jtm/taac134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Human mobility changed in unprecedented ways during the SARS-CoV-2 pandemic. In March and April 2020, when lockdowns and large travel restrictions began in most countries, global air-travel almost entirely halted (92% decrease in commercial global air travel in the months between February and April 2020). Initial recovery in global air travel started around July 2020 and subsequently nearly tripled between May and July 2021. Here, we aim to establish a preliminary link between global mobility patterns and the synchrony of SARS-CoV-2 epidemic waves across the world. METHODS We compare epidemic peaks and human global mobility in two time periods: November 2020 to February 2021 (when just over 70 million passengers travelled) and November 2021 to February 2022 (when more than 200 million passengers travelled). We calculate the time interval during which continental epidemic peaks occurred for both of these time periods, and we calculate the pairwise correlations of epidemic waves between all pairs of countries for the same time periods. RESULTS We find that as air travel increases at the end of 2021, epidemic peaks around the world are more synchronous with one another, both globally and regionally. Continental epidemic peaks occur globally within a 20 day interval at the end of 2021 compared with 73 days at the end of 2020, and epidemic waves globally are more correlated with one another at the end of 2021. CONCLUSIONS This suggests that the rebound in human mobility dictates the synchrony of global and regional epidemic waves. In line with theoretical work, we show that in a more connected world, epidemic dynamics are more synchronized.
Collapse
Affiliation(s)
- Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
5
|
Lee SS, Viboud C, Petersen E. Understanding the rebound of influenza in the post COVID-19 pandemic period holds important clues for epidemiology and control. Int J Infect Dis 2022; 122:1002-1004. [PMID: 35932966 PMCID: PMC9349026 DOI: 10.1016/j.ijid.2022.08.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Shui Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinse University of Hong Kong, Hong Kong, China,International Society for Infectious Diseases
| | - Cecile Viboud
- Fogarty International Center, National Institute of Health, Bethesda, USA
| | - Eskild Petersen
- International Society for Infectious Diseases,Institute for Clinical Medicine, Faculty of Health Sciences, University of Aarhus, Denmark,European Society for Clinical Microbiology and Infectious Diseases [ESCMID] Task Force for Emerging Infections, Basel, Switzerland
| |
Collapse
|
6
|
Wang C, Yang YN, Xi L, Yang LL, Du J, Zhang ZS, Lian XY, Cui Y, Li HJ, Zhang WX, Liu B, Cui F, Lu QB. Dynamics of influenza-like illness under urbanization procedure and COVID-19 pandemic in the sub-center of Beijing during 2013-2021. J Med Virol 2022; 94:3801-3810. [PMID: 35451054 PMCID: PMC9088387 DOI: 10.1002/jmv.27803] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 12/02/2022]
Abstract
Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.
Collapse
Affiliation(s)
- Chao Wang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Yan-Na Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Lu Xi
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Li-Li Yang
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Juan Du
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Zhong-Song Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Xin-Yao Lian
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Yan Cui
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Hong-Jun Li
- Institute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Diseases Prevention and Control, Beijing, People's Republic of China
| | - Wan-Xue Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Bei Liu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Fuqiang Cui
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| | - Qing-Bin Lu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing, People's Republic of China
| |
Collapse
|
7
|
Bota A, Holmberg M, Gardner L, Rosvall M. Socioeconomic and environmental patterns behind H1N1 spreading in Sweden. Sci Rep 2021; 11:22512. [PMID: 34795338 PMCID: PMC8602374 DOI: 10.1038/s41598-021-01857-4] [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: 04/16/2021] [Accepted: 11/03/2021] [Indexed: 11/25/2022] Open
Abstract
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.
Collapse
Affiliation(s)
- András Bota
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden. .,Embedded Intelligent Systems Lab, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187, Luleå, Sweden.
| | - Martin Holmberg
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Martin Rosvall
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden
| |
Collapse
|
8
|
Influenza virus-flow from insects to humans as causative for influenza seasonality. Biol Direct 2020; 15:17. [PMID: 33036642 PMCID: PMC7545380 DOI: 10.1186/s13062-020-00272-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 09/25/2020] [Indexed: 11/10/2022] Open
Abstract
Virus biomass outweighs human biomass, and insects biomass outweighs human biomass. Insects are regularly habited by viruses as well as humans, humans are further inhabited via insects. A model of viral flow is described and specified to explain influenza virus seasonality, which, in temperate climate, usually evolves when insects have mostly disappeared. With this hypothesis a coherent description of regular seasonal influenza and other seasonal respiratory virus infections in temperate climates is possible. The incidence of influenza under different circumstances e.g. temperature, humidity, or tropical conditions and different aspects like synchronicity of infections or in respect to evolutionary conditions do sustain this hypothesis if the behaviour of insects is considered.
Collapse
|
9
|
Balasubramani GK, Nowalk MP, Sax TM, Suyama J, Bobyock E, Rinaldo CR, Martin ET, Monto AS, Jackson ML, Gaglani MJ, Flannery B, Chung JR, Zimmerman RK. Influenza vaccine effectiveness among outpatients in the US Influenza Vaccine Effectiveness Network by study site 2011-2016. Influenza Other Respir Viruses 2020; 14:380-390. [PMID: 32298048 PMCID: PMC7298285 DOI: 10.1111/irv.12741] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 11/29/2022] Open
Abstract
Background Influenza vaccination is recommended for all US residents aged ≥6 months. Vaccine effectiveness (VE) varies by age, circulating influenza strains, and the presence of high‐risk medical conditions. We examined site‐specific VE in the US Influenza VE Network, which evaluates annual influenza VE at ambulatory clinics in geographically diverse sites. Methods Analyses were conducted on 27 180 outpatients ≥6 months old presenting with an acute respiratory infection (ARI) with cough of ≤7‐day duration during the 2011‐2016 influenza seasons. A test‐negative design was used with vaccination status defined as receipt of ≥1 dose of any influenza vaccine according to medical records, registries, and/or self‐report. Influenza infection was determined by reverse‐transcription polymerase chain reaction. VE estimates were calculated using odds ratios from multivariable logistic regression models adjusted for age, sex, race/ethnicity, time from illness onset to enrollment, high‐risk conditions, calendar time, and vaccination status‐site interaction. Results For all sites combined, VE was statistically significant every season against all influenza and against the predominant circulating strains (VE = 19%‐50%) Few differences among four sites in the US Flu VE Network were evident in five seasons. However, in 2015‐16, overall VE in one site was 24% (95% CI = −4%‐44%), while VE in two other sites was significantly higher (61%, 95% CI = 49%‐71%; P = .002, and 53%, 95% CI = 33,67; P = .034). Conclusion With few exceptions, site‐specific VE estimates aligned with each other and overall VE estimates. Observed VE may reflect inherent differences in community characteristics of the sites and highlights the importance of diverse settings for studying influenza vaccine effectiveness.
Collapse
Affiliation(s)
- Goundappa K Balasubramani
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mary Patricia Nowalk
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Theresa M Sax
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joe Suyama
- Department of Emergency Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Emily Bobyock
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles R Rinaldo
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Emily T Martin
- School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Arnold S Monto
- School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael L Jackson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Manjusha J Gaglani
- Baylor Scott & White Health, Texas A&M University College of Medicine, Temple, TX, USA
| | | | - Jessie R Chung
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Richard K Zimmerman
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|