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Williams KV, Krauland MG, Harrison LH, Williams JV, Roberts MS, Zimmerman RK. Influenza Vaccination, Household Composition, and Race-Based Differences in Influenza Incidence: An Agent-Based Modeling Study. Am J Public Health 2025; 115:209-216. [PMID: 39541556 PMCID: PMC11715590 DOI: 10.2105/ajph.2024.307878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
Objectives. To estimate the effect of influenza vaccination disparities. Methods. We compared symptomatic influenza cases between Black and White races in 2 scenarios: (1) race- and age-specific vaccination coverage and (2) equal vaccination coverage. We also compared differences in household composition between races. We used the Framework for Reconstructing Epidemiological Dynamics, an agent-based model that assigns US Census‒based age, race, households, and geographic location to agents (individual people), in US counties of varying racial and age composition. Results. Influenza cases were highest in counties with higher proportions of children. Cases were up to 30% higher in Black agents with both race-based and race-equal vaccination coverage. Compared with corresponding categories of White households, cases in Black households without children were lower and with children were higher. Conclusions. Racial disparities in influenza cases persisted after equalizing vaccination coverage. The proportion of children in the population contributed to the number of influenza cases regardless of race. Differences in household composition may provide insight into racial differences and offer an opportunity to improve vaccination coverage to reduce influenza burden for both races. (Am J Public Health. 2025;115(2):209-216. https://doi.org/10.2105/AJPH.2024.307878).
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Affiliation(s)
- Katherine V Williams
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
| | - Mary G Krauland
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
| | - Lee H Harrison
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
| | - John V Williams
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
| | - Mark S Roberts
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
| | - Richard K Zimmerman
- Katherine V. Williams and Richard K. Zimmerman are with the Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA. Mary G. Krauland and Mark S. Roberts are with the Department of Health Policy and Management and Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh. Lee H. Harrison is with the Center for Genomic Epidemiology, Division of Infectious Diseases, University of Pittsburgh School of Medicine. John V. Williams is with the Department of Pediatrics, Division of Pediatric Infectious Disease, University of Pittsburgh School of Medicine
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Chen X, Chen H, Tao F, Chen Y, Zhou Y, Cheng J, Wang X. Global analysis of influenza epidemic characteristics in the first two seasons after lifting the nonpharmaceutical interventions for COVID-19. Int J Infect Dis 2025; 151:107372. [PMID: 39710136 DOI: 10.1016/j.ijid.2024.107372] [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: 10/21/2024] [Revised: 12/11/2024] [Accepted: 12/17/2024] [Indexed: 12/24/2024] Open
Abstract
OBJECTIVES The COVID-19 pandemic significantly disrupted the global influenza seasonal patterns due to nonpharmaceutical interventions. This study aims to describe the influenza seasonal characteristics in the first two seasons after lifting COVID-19 NPIs and assess shifts before, during, and after the pandemic. METHODS We analyzed country-specific weekly influenza data (2011-2024) from WHO FluNet and collected COVID-19 NPI timing from official announcements. The study was divided into pre-pandemic, pandemic, and post-pandemic phases, estimating epidemic onset, peak week, peak intensity, and duration by climate zones. RESULTS In temperate countries, peak intensity after the pandemic decreased by 8.4 %, while duration increased by 1.8 weeks, and onset and peak were delayed by 18.5 and 22.8 weeks compared to regular seasonal pattern before the pandemic. Subtropical countries experienced a 17.2 % decrease in peak intensity, a 2.4-week decrease in duration, and delays in onset and peak by 13.5 and 2.3 weeks. Tropical countries had a 10 % decrease in peak intensity, a 3-week reduction in duration, and a 6.6-week delay in onset with no significant change in peak time. CONCLUSION Influenza seasonality shifted significantly after the pandemic, with epidemic durations returning to typical patterns but peak intensities remained low. Robust surveillance after an infectious disease pandemic is crucial to inform prevention and control strategies.
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Affiliation(s)
- Xiaowei Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Honghong Chen
- Minhang District Center for Disease Control and Prevention, Shanghai, China
| | - Fangfang Tao
- Institute of Infectious Disease Prevention and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Yinzi Chen
- Institute of Infectious Disease Prevention and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Ying Zhou
- Shanghai Institute of Aviation Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Xiling Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Shanghai Key Laboratory of Meteorology and Health, Shanghai, China.
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Zeng Z, Liu Y, Jin W, Liang J, Chen J, Chen R, Li Q, Guan W, Liang L, Wu Q, Lai Y, Deng X, Lin Z, Hon C, Yang Z. Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/Victoria during the COVID-19 pandemic in Guangdong, China. Infect Dis Poverty 2024; 13:56. [PMID: 39090685 PMCID: PMC11295596 DOI: 10.1186/s40249-024-01218-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/21/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019 (COVID-19) and influenza. Nonetheless, with COVID-19 restrictions lifted, an unanticipated outbreak of the influenza B/Victoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong, southern China. The mechanism underlying this phenomenon remains unknown. To better prepare for potential influenza outbreaks during COVID-19 pandemic, we studied the molecular epidemiology and phylogenetics of influenza A(H3N2) and B/Victoria that circulated during the COVID-19 pandemic in this region. METHODS From January 1, 2018 to December 31, 2022, we collected throat swabs from 173,401 patients in Guangdong who had acute respiratory tract infections. Influenza viruses in the samples were tested using reverse transcription-polymerase chain reaction, followed by subtype identification and sequencing of hemagglutinin (HA) and neuraminidase (NA) genes. Phylogenetic and genetic diversity analyses were performed on both genes from 403 samples. A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the correlation. RESULTS During the early period of COVID-19 control, various influenza viruses were nearly undetectable in respiratory specimens. When control measures were relaxed in January 2020, the influenza infection rate peaked at 4.94% (39/789) in December 2021, with the influenza B/Victoria accounting for 87.18% (34/39) of the total influenza cases. Six months later, the influenza infection rate again increased and peaked at 11.34% (255/2248) in June 2022; influenza A/H3N2 accounted for 94.51% (241/255) of the total influenza cases in autumn 2022. The diverse geographic distribution of HA genes of B/Victoria and A/H3N2 had drastically reduced, and most strains originated from China. The rate of B/Victoria HA evolution (3.11 × 10-3, P < 0.05) was 1.7 times faster than before the COVID-19 outbreak (1.80 × 10-3, P < 0.05). Likewise, the H3N2 HA gene's evolution rate was 7.96 × 10-3 (P < 0.05), which is 2.1 times faster than the strains' pre-COVID-19 evolution rate (3.81 × 10-3, P < 0.05). CONCLUSIONS Despite the extraordinarily low detection rate of influenza infection, concealed influenza transmission may occur between individuals during strict COVID-19 control. This ultimately leads to the accumulation of viral mutations and accelerated evolution of H3N2 and B/Victoria viruses. Monitoring the evolution of influenza may provide insights and alerts regarding potential epidemics in the future.
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Affiliation(s)
- Zhiqi Zeng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China
| | - Yong Liu
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China
- Kingmed Virology Diagnostic and Translational Center, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
| | - Wenxiang Jin
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China
- Kingmed Virology Diagnostic and Translational Center, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, China
| | - Jingyi Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
- Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
| | - Jinbin Chen
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Ruihan Chen
- Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China
| | - Qianying Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
| | - Wenda Guan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
| | - Lixi Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
| | - Qiubao Wu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China
| | - Yuanfang Lai
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China
| | - Xiaoyan Deng
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China.
| | - Zhengshi Lin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China.
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China.
| | - Chitin Hon
- Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, China.
- Guangzhou Laboratory, Guangzhou, China.
| | - Zifeng Yang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510180, P.R. China.
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, People's Republic of China.
- Guangzhou Laboratory, Guangzhou, China.
- Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovative Engineering, Macau University of Science and Technology, Macau SAR, China.
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Lee K, Williams KV, Englund JA, Sullivan SG. The Potential Benefits of Delaying Seasonal Influenza Vaccine Selections for the Northern Hemisphere: A Retrospective Modeling Study in the United States. J Infect Dis 2024; 230:131-140. [PMID: 39052711 DOI: 10.1093/infdis/jiad541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/14/2023] [Accepted: 11/28/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Antigenic similarity between vaccine viruses and circulating viruses is crucial for achieving high vaccine effectiveness against seasonal influenza. New non-egg-based vaccine production technologies could revise current vaccine formulation schedules. We aim to assess the potential benefit of delaying seasonal influenza vaccine virus selection decisions. METHODS We identified seasons where season-dominant viruses presented increasing prevalence after vaccine formulation had been decided in February for the Northern Hemisphere, contributing to their antigenic discrepancy with vaccine viruses. Using a SEIR (susceptible-exposed-infectious-recovered) model of seasonal influenza in the United States, we evaluated the impact of updating vaccine decisions with more antigenically similar vaccine viruses on the influenza burden in the United States. RESULTS In 2014-2015 and 2019-2020, the season-dominant A(H3N2) subclade and B/Victoria clade, respectively, presented increasing prevalence after vaccine decisions were already made for the Northern Hemisphere. Our model showed that the updated A(H3N2) vaccine could have averted 5000-65 000 influenza hospitalizations in the United States in 2014-2015, whereas updating the B/Victoria vaccine component did not substantially change influenza burden in the 2019-2020 season. CONCLUSIONS With rapid vaccine production, revising current timelines for vaccine selection could result in substantial epidemiological benefits, particularly when additional data could help improve the antigenic match between vaccine and circulating viruses.
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Affiliation(s)
- Kyueun Lee
- Comparative Health Outcomes Policy and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle
| | - Katherine V Williams
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Janet A Englund
- Seattle Children's Research Institute, Department of Pediatrics, University of Washington, Seattle
| | - Sheena G Sullivan
- World Health Organization Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital
- Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Victoria, Australia
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5
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Del Riccio M, Caini S, Bonaccorsi G, Lorini C, Paget J, van der Velden K, Meijer A, Haag M, McGovern I, Zanobini P. Global analysis of respiratory viral circulation and timing of epidemics in the pre-COVID-19 and COVID-19 pandemic eras, based on data from the Global Influenza Surveillance and Response System (GISRS). Int J Infect Dis 2024; 144:107052. [PMID: 38636684 DOI: 10.1016/j.ijid.2024.107052] [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: 01/17/2024] [Revised: 03/30/2024] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVES The COVID-19 pandemic significantly changed respiratory viruses' epidemiology due to non-pharmaceutical interventions and possible viral interactions. This study investigates whether the circulation patterns of respiratory viruses have returned to pre-pandemic norms by comparing their peak timing and duration during the first three SARS-CoV-2 seasons to pre-pandemic times. METHODS Global Influenza Surveillance and Response System data from 194 countries (2014-2023) was analyzed for epidemic peak timing and duration, focusing on pre-pandemic and pandemic periods across both hemispheres and the intertropical belt. The analysis was restricted to countries meeting specific data thresholds to ensure robustness. RESULTS In 2022/2023, the northern hemisphere experienced earlier influenza and respiratory syncytial virus (RSV) peaks by 1.9 months (P <0.001). The duration of influenza epidemics increased by 2.2 weeks (P <0.001), with RSV showing a similar trend. The southern hemisphere's influenza peak shift was not significant (P = 0.437). Intertropical regions presented no substantial change in peak timing but experienced a significant reduction in the duration for human metapneumovirus and adenovirus (7.2 and 6.5 weeks shorter, respectively, P <0.001). CONCLUSIONS The pandemic altered the typical patterns of influenza and RSV, with earlier peaks in 2022 in temperate areas. These findings highlight the importance of robust surveillance data to inform public health strategies on evolving viral dynamics in the years to come.
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Affiliation(s)
- Marco Del Riccio
- Department of Health Sciences, University of Florence, Florence, Italy; Department of Primary and Community Care, Radboud University Medical Centre, HB Nijmegen, The Netherlands
| | - Saverio Caini
- Netherlands Institute for Health Services Research, CR Utrecht, The Netherlands.
| | | | - Chiara Lorini
- Department of Health Sciences, University of Florence, Florence, Italy
| | - John Paget
- Netherlands Institute for Health Services Research, CR Utrecht, The Netherlands
| | - Koos van der Velden
- Department of Primary and Community Care, Radboud University Medical Centre, HB Nijmegen, The Netherlands
| | - Adam Meijer
- National Institute for Public Health and the Environment, BA Bilthoven, The Netherlands
| | | | - Ian McGovern
- Center for Outcomes Research and Epidemiology, Seqirus Inc, Cambridge, USA
| | - Patrizio Zanobini
- Department of Health Sciences, University of Florence, Florence, Italy
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Hoy G, Maier HE, Kuan G, Sánchez N, López R, Meyers A, Plazaola M, Ojeda S, Balmaseda A, Gordon A. Increased influenza severity in children in the wake of SARS-CoV-2. Influenza Other Respir Viruses 2023; 17:e13178. [PMID: 37492240 PMCID: PMC10363782 DOI: 10.1111/irv.13178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/27/2023] Open
Abstract
The SARS-CoV-2 pandemic and subsequent interruption of influenza circulation has lowered population immunity to influenza, especially among children with few prepandemic exposures. Using data from a prospective pediatric cohort study based in Managua, Nicaragua, we compared the incidence and severity of influenza A/H3N2 and influenza B/Victoria between 2022 and two prepandemic seasons. We found a higher incidence of A/H3N2 in older children in 2022 compared with pre-2020 and a higher proportion of severe influenza in 2022, primarily among children aged 0-4, suggesting an influence of the SARS-CoV-2 pandemic on influenza incidence and severity in children.
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Affiliation(s)
- Gregory Hoy
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Hannah E. Maier
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | - Guillermina Kuan
- Sustainable Sciences InstituteManaguaNicaragua
- Centro de Salud Sócrates Flores VivasMinistry of HealthManaguaNicaragua
| | | | - Roger López
- Sustainable Sciences InstituteManaguaNicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y ReferenciaMinistry of HealthManaguaNicaragua
| | - Alyssa Meyers
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
| | | | | | - Angel Balmaseda
- Sustainable Sciences InstituteManaguaNicaragua
- Laboratorio Nacional de Virología, Centro Nacional de Diagnóstico y ReferenciaMinistry of HealthManaguaNicaragua
| | - Aubree Gordon
- Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborMichiganUSA
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Tokito T, Kido T, Muramatsu K, Tokutsu K, Okuno D, Yura H, Takemoto S, Ishimoto H, Takazono T, Sakamoto N, Obase Y, Ishimatsu Y, Fujino Y, Yatera K, Fushimi K, Matsuda S, Mukae H. Impact of Administering Intravenous Azithromycin within 7 Days of Hospitalization for Influenza Virus Pneumonia: A Propensity Score Analysis Using a Nationwide Administrative Database. Viruses 2023; 15:1142. [PMID: 37243228 PMCID: PMC10222596 DOI: 10.3390/v15051142] [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: 04/16/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023] Open
Abstract
The potential antimicrobial and anti-inflammatory effectiveness of azithromycin against severe influenza is yet unclear. We retrospectively investigated the effect of intravenous azithromycin administration within 7 days of hospitalization in patients with influenza virus pneumonia and respiratory failure. Using Japan's national administrative database, we enrolled and classified 5066 patients with influenza virus pneumonia into severe, moderate, and mild groups based on their respiratory status within 7 days of hospitalization. The primary endpoints were total, 30-day, and 90-day mortality rates. The secondary endpoints were the duration of intensive-care unit management, invasive mechanical ventilation, and hospital stay. The inverse probability of the treatment weighting method with estimated propensity scores was used to minimize data collection bias. Use of intravenous azithromycin was proportional to the severity of respiratory failure (mild: 1.0%, moderate: 3.1%, severe: 14.8%). In the severe group, the 30-day mortality rate was significantly lower with azithromycin (26.49% vs. 36.65%, p = 0.038). In the moderate group, the mean duration of invasive mechanical ventilation after day 8 was shorter with azithromycin; there were no significant differences in other endpoints between the severe and moderate groups. These results suggest that intravenous azithromycin has favorable effects in patients with influenza virus pneumonia using mechanical ventilation or oxygen.
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Affiliation(s)
- Takatomo Tokito
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Takashi Kido
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Keiji Muramatsu
- Department of Preventive Medicine and Community Health, University of Occupational and Environmental Health, Japan, Kitakyushu 807-0804, Japan
| | - Kei Tokutsu
- Department of Preventive Medicine and Community Health, University of Occupational and Environmental Health, Japan, Kitakyushu 807-0804, Japan
| | - Daisuke Okuno
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Hirokazu Yura
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Shinnosuke Takemoto
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Hiroshi Ishimoto
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Takahiro Takazono
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
- Department of Infectious Diseases, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Noriho Sakamoto
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Yasushi Obase
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Yuji Ishimatsu
- Department of Nursing, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
| | - Yoshihisa Fujino
- Department of Environmental Epidemiology, Institute of Industrial Ecological Science, University of Occupational and Environmental Health, Japan, Kitakyushu 807-0804, Japan
| | - Kazuhiro Yatera
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-0804, Japan
| | - Kiyohide Fushimi
- Department of Health Policy and Informatics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Japan, Tokyo 113-8519, Japan
| | - Shinya Matsuda
- Department of Preventive Medicine and Community Health, University of Occupational and Environmental Health, Japan, Kitakyushu 807-0804, Japan
| | - Hiroshi Mukae
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8501, Japan
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Hoy G, Maier HE, Kuan G, Sánchez N, López R, Meyers A, Plazaola M, Ojeda S, Balmaseda A, Gordon A. Increased Influenza Severity in Children in the Wake of SARS-CoV-2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.11.23286858. [PMID: 36993385 PMCID: PMC10055452 DOI: 10.1101/2023.03.11.23286858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The SARS-CoV-2 pandemic and subsequent interruption of influenza circulation has lowered population immunity to influenza, especially among children with few pre-pandemic exposures. We compared the incidence and severity of influenza A/H3N2 and influenza B/Victoria between 2022 and two pre-pandemic seasons and found an increased frequency of severe influenza in 2022.
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Rolfes MA, Talbot HK, McLean HQ, Stockwell MS, Ellingson KD, Lutrick K, Bowman NM, Bendall EE, Bullock A, Chappell JD, Deyoe JE, Gilbert J, Halasa NB, Hart KE, Johnson S, Kim A, Lauring AS, Lin JT, Lindsell CJ, McLaren SH, Meece JK, Mellis AM, Moreno Zivanovich M, Ogokeh CE, Rodriguez M, Sano E, Silverio Francisco RA, Schmitz JE, Vargas CY, Yang A, Zhu Y, Belongia EA, Reed C, Grijalva CG. Household Transmission of Influenza A Viruses in 2021-2022. JAMA 2023; 329:482-489. [PMID: 36701144 PMCID: PMC9880862 DOI: 10.1001/jama.2023.0064] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
IMPORTANCE Influenza virus infections declined globally during the COVID-19 pandemic. Loss of natural immunity from lower rates of influenza infection and documented antigenic changes in circulating viruses may have resulted in increased susceptibility to influenza virus infection during the 2021-2022 influenza season. OBJECTIVE To compare the risk of influenza virus infection among household contacts of patients with influenza during the 2021-2022 influenza season with risk of influenza virus infection among household contacts during influenza seasons before the COVID-19 pandemic in the US. DESIGN, SETTING, AND PARTICIPANTS This prospective study of influenza transmission enrolled households in 2 states before the COVID-19 pandemic (2017-2020) and in 4 US states during the 2021-2022 influenza season. Primary cases were individuals with the earliest laboratory-confirmed influenza A(H3N2) virus infection in a household. Household contacts were people living with the primary cases who self-collected nasal swabs daily for influenza molecular testing and completed symptom diaries daily for 5 to 10 days after enrollment. EXPOSURES Household contacts living with a primary case. MAIN OUTCOMES AND MEASURES Relative risk of laboratory-confirmed influenza A(H3N2) virus infection in household contacts during the 2021-2022 season compared with prepandemic seasons. Risk estimates were adjusted for age, vaccination status, frequency of interaction with the primary case, and household density. Subgroup analyses by age, vaccination status, and frequency of interaction with the primary case were also conducted. RESULTS During the prepandemic seasons, 152 primary cases (median age, 13 years; 3.9% Black; 52.0% female) and 353 household contacts (median age, 33 years; 2.8% Black; 54.1% female) were included and during the 2021-2022 influenza season, 84 primary cases (median age, 10 years; 13.1% Black; 52.4% female) and 186 household contacts (median age, 28.5 years; 14.0% Black; 63.4% female) were included in the analysis. During the prepandemic influenza seasons, 20.1% (71/353) of household contacts were infected with influenza A(H3N2) viruses compared with 50.0% (93/186) of household contacts in 2021-2022. The adjusted relative risk of A(H3N2) virus infection in 2021-2022 was 2.31 (95% CI, 1.86-2.86) compared with prepandemic seasons. CONCLUSIONS AND RELEVANCE Among cohorts in 5 US states, there was a significantly increased risk of household transmission of influenza A(H3N2) in 2021-2022 compared with prepandemic seasons. Additional research is needed to understand reasons for this association.
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Affiliation(s)
- Melissa A. Rolfes
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | | | | | | | | | | | | | | | | | - Jessica E. Deyoe
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | | | | | - Sheroi Johnson
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Ahra Kim
- Vanderbilt University Medical Center, Nashville, Tennessee
| | | | | | | | | | | | - Alexandra M. Mellis
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Constance E. Ogokeh
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | - Ellen Sano
- Columbia University, New York City, New York
| | | | | | | | - Amy Yang
- University of North Carolina at Chapel Hill
| | - Yuwei Zhu
- Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Carrie Reed
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
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Tsang TK, Huang X, Guo Y, Lau EHY, Cowling BJ, Ip DKM. Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis. JMIR Public Health Surveill 2023. [DOI: 10.2196/41329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Background
Influenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in the community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear.
Objective
The aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza.
Methods
We conducted a systematic review of the published literature on the relationship between school absenteeism and influenza activity in the community. We categorized the types of school absenteeism and influenza activity in the community to determine the correlation between these data streams. We also extracted this correlation with different lags in community surveillance to determine the potential of using school absenteeism as a leading indicator of influenza activity.
Results
Among the 35 identified studies, 22 (63%), 12 (34%), and 8 (23%) studies monitored all-cause, illness-specific, and influenza-like illness (ILI)–specific absents, respectively, and 16 (46%) used quantitative approaches and provided 33 estimates on the temporal correlation between school absenteeism and influenza activity in the community. The pooled estimate of correlation between school absenteeism and community surveillance without lag, with 1-week lag, and with 2-week lag were 0.44 (95% CI 0.34, 0.53), 0.29 (95% CI 0.15, 0.42), and 0.21 (95% CI 0.11, 0.31), respectively. The correlation between influenza activity in the community and ILI-specific absenteeism was higher than that between influenza activity in community all-cause absenteeism. Among the 19 studies that used qualitative approaches, 15 (79%) concluded that school absenteeism was in concordance with, coincided with, or was associated with community surveillance. Of the 35 identified studies, only 6 (17%) attempted to predict influenza activity in the community from school absenteeism surveillance.
Conclusions
There was a moderate correlation between school absenteeism and influenza activity in the community. The smaller correlation between school absenteeism and community surveillance with lag, compared to without lag, suggested that careful application was required to use school absenteeism as a leading indicator of influenza epidemics. ILI-specific absenteeism could monitor influenza activity more closely, but the required resource or school participation willingness may require careful consideration to weight against the associated costs. Further development is required to use and optimize the use of school absenteeism to predict influenza activity. In particular, the potential of using more advanced statistical models and validation of the predictions should be explored.
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Kolosova NP, Ilyicheva TN, Unguryan VV, Danilenko AV, Svyatchenko SV, Onhonova GS, Goncharova NI, Kosenko MN, Gudymo AS, Marchenko VY, Shvalov AN, Susloparov IM, Tregubchak TV, Gavrilova EV, Maksyutov RA, Ryzhikov AB. Re-Emergence of Circulation of Seasonal Influenza during COVID-19 Pandemic in Russia and Receptor Specificity of New and Dominant Clade 3C.2a1b.2a.2 A(H3N2) Viruses in 2021-2022. Pathogens 2022; 11:1388. [PMID: 36422639 PMCID: PMC9698969 DOI: 10.3390/pathogens11111388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2023] Open
Abstract
The circulation of seasonal influenza in 2020-2021 around the world was drastically reduced after the start of the COVID-19 pandemic and the implementation of mitigation strategies. The influenza virus circulation reemerged in 2021-2022 with the global spread of the new genetic clade 3C.2a1b.2a.2 of A(H3N2) viruses. The purpose of this study was to characterize influenza viruses in the 2021-2022 season in Russia and to analyze the receptor specificity properties of the 3C.2a1b.2a.2 A(H3N2) viruses. Clinical influenza samples were collected at the local Sanitary-and-Epidemiological Centers of Rospotrebnadzor. Whole genome sequencing was performed using NGS. The receptor specificity of hemagglutinin was evaluated using molecular modeling and bio-layer interferometry. Clinical samples from 854 cases of influenza A and B were studied; A(H3N2) viruses were in the majority of the samples. All genetically studied A(H3N2) viruses belonged to the new genetic clade 3C.2a1b.2a.2. Molecular modeling analysis suggested a higher affinity of hemagglutinin of 3C.2a1b.2a.2. A(H3N2) viruses to the α2,6 human receptor. In vitro analysis using a trisaccharide 6'-Sialyl-N-acetyllactosamine receptor analog did not resolve the differences in the receptor specificity of 3C.2a1b.2a.2 clade viruses from viruses belonging to the 3C.2a1b.2a.1 clade. Further investigation of the A(H3N2) viruses is required for the evaluation of their possible adaptive advantages. Constant monitoring and characterization of influenza are critical for epidemiological analysis.
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Affiliation(s)
- Natalia P. Kolosova
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Tatiana N. Ilyicheva
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Vasily V. Unguryan
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
- Department of Physics, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Alexey V. Danilenko
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Svetlana V. Svyatchenko
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Galina S. Onhonova
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Natalia I. Goncharova
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Maksim N. Kosenko
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Andrey S. Gudymo
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Vasiliy Y. Marchenko
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Alexander N. Shvalov
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Ivan M. Susloparov
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Tatiana V. Tregubchak
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Elena V. Gavrilova
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Rinat A. Maksyutov
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
| | - Alexander B. Ryzhikov
- State Research Centre of Virology and Biotechnology “Vector”, Rospotrebnadzor, Koltsovo, Novosibirsk 630559, Russia
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Guo Z, Zhang L, Liu J, Liu M. Impact of COVID-19 Prevention and Control on the Influenza Epidemic in China: A Time Series Study. HEALTH DATA SCIENCE 2022; 2022:9830159. [PMID: 38487480 PMCID: PMC10880177 DOI: 10.34133/2022/9830159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/06/2022] [Indexed: 03/17/2024]
Abstract
Background. COVID-19 prevention and control measures might affect influenza epidemic in China since the nonpharmaceutical interventions (NPIs) and behavioral changes contain transmission of both SARS-CoV-2 and influenza virus. We aimed to explore the impact of COVID-19 prevention and control measures on influenza using data from the National Influenza Surveillance Network.Methods. The percentage of influenza-like illness (ILI%) in southern and northern China from 2010 to 2022 was collected from the National Influenza Surveillance Network. Weekly ILI% observed value from 2010 to 2019 was used to calculate estimated annual percentage change (EAPC) of ILI% with 95% confidence intervals (CIs). Time series analysis was applied to estimate weekly ILI% predicted values in 2020/2021 and 2021/2022 season. Impact index was used to explore the impact of COVID-19 prevention and control on influenza during nonpharmaceutical intervention and vaccination stages.Results. China influenza activity was affected by the COVID-19 pandemic and different prevention and control measures during 2020-2022. In 2020/2021 season, weekly ILI% observed value in both southern and northern China was at a low epidemic level, and there was no obvious epidemic peak in winter and spring. In 2021/2022 season, weekly ILI% observed value in southern and northern China showed a small peak in summer and epidemic peak in winter and spring. The weekly ILI% observed value was generally lower than the predicted value in southern and northern China during 2020-2022. The median of impact index of weekly ILI% was 15.11% in north and 22.37% in south in 2020/2021 season and decreased significantly to 2.20% in north and 3.89% in south in 2021/2022 season.Conclusion. In summary, there was a significant decrease in reported ILI in China during the 2020-2022 COVID-19 pandemic, particularly in winter and spring. Reduction of influenza virus infection might relate to everyday Chinese public health COVID-19 interventions. The confirmation of this relationship depends on future studies.
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Affiliation(s)
- Zirui Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Min Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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Buckrell S, Ben Moussa M, Bui T, Rahal A, Schmidt K, Lee L, Bastien N, Bancej C. National Influenza Annual Report, Canada, 2021-2022: A brief, late influenza epidemic. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2022; 48:473-483. [PMID: 38125392 PMCID: PMC10730107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Canadian seasonal influenza circulation had been suppressed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic. This suppression was reported globally and generated concern that the return of community influenza circulation could be intense and that co-circulation of influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was possible and potentially severe. Community circulation of influenza returned to Canada during the 2021-2022 influenza season. The influenza epidemic began in week 16 (mid-April 2022) and lasted only nine weeks. This epidemic was driven by influenza A(H3N2) and was exceptionally late in the season, low in intensity and short in length. Community co-circulation of influenza and SARS-CoV-2 was observed in Canada for the first time during the 2021-2022 seasonal influenza epidemic. The unusual characteristics of the 2021-2022 influenza epidemic suggest that a breadth of factors moderate transmission dynamics of the two viruses. Concerns of an intense seasonal influenza epidemic did not come to fruition during the 2021-2022 season; therefore, high influenza susceptibility remains, as does predisposition to larger influenza epidemics. Ongoing circulation of SARS-CoV-2 creates uncertainty about dynamics of future influenza epidemics, but influenza vaccination remains a key public health intervention available to protect Canadians. Public health authorities need to remain vigilant, maintain surveillance and continue to plan for both heightened seasonal influenza circulation and for the potential for endemic co-circulation of influenza and SARS-CoV-2.
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Affiliation(s)
- Steven Buckrell
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Myriam Ben Moussa
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Tammy Bui
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Abbas Rahal
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Kara Schmidt
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Liza Lee
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
| | - Nathalie Bastien
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB
| | - Christina Bancej
- Centre for Immunization and Respiratory Infectious Diseases, Public Health Agency of Canada, Ottawa, ON
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