1
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Perofsky AC, Huddleston J, Hansen CL, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. eLife 2024; 13:RP91849. [PMID: 39319780 PMCID: PMC11424097 DOI: 10.7554/elife.91849] [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] [Indexed: 09/26/2024] Open
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.
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MESH Headings
- Influenza A Virus, H3N2 Subtype/genetics
- Influenza A Virus, H3N2 Subtype/immunology
- United States/epidemiology
- Influenza, Human/epidemiology
- Influenza, Human/virology
- Influenza, Human/immunology
- Humans
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Epidemics
- Antigenic Drift and Shift/genetics
- Child
- Adult
- Neuraminidase/genetics
- Neuraminidase/immunology
- Adolescent
- Child, Preschool
- Antigens, Viral/immunology
- Antigens, Viral/genetics
- Young Adult
- Evolution, Molecular
- Seasons
- Middle Aged
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Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, United States
| | - Chelsea L Hansen
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Kanta Subbarao
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, New York, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, United States
- Department of Genome Sciences, University of Washington, Seattle, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, United States
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2
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Perofsky AC, Huddleston J, Hansen C, Barnes JR, Rowe T, Xu X, Kondor R, Wentworth DE, Lewis N, Whittaker L, Ermetal B, Harvey R, Galiano M, Daniels RS, McCauley JW, Fujisaki S, Nakamura K, Kishida N, Watanabe S, Hasegawa H, Sullivan SG, Barr IG, Subbarao K, Krammer F, Bedford T, Viboud C. Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.02.23296453. [PMID: 37873362 PMCID: PMC10593063 DOI: 10.1101/2023.10.02.23296453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.
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Affiliation(s)
- Amanda C Perofsky
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
| | - Chelsea Hansen
- Fogarty International Center, National Institutes of Health, United States
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
| | - John R Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), United States
| | - Nicola Lewis
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Lynne Whittaker
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Burcu Ermetal
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Ruth Harvey
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Monica Galiano
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Rodney Stuart Daniels
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - John W McCauley
- WHO Collaborating Centre for Reference and Research on Influenza, Crick Worldwide Influenza Centre, The Francis Crick Institute, United Kingdom
| | - Seiichiro Fujisaki
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Kazuya Nakamura
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Noriko Kishida
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Shinji Watanabe
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Hideki Hasegawa
- Influenza Virus Research Center, National Institute of Infectious Diseases, Japan
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Ian G Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Kanta Subbarao
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Australia
| | - Florian Krammer
- Center for Vaccine Research and Pandemic Preparedness (C-VaRPP), Icahn School of Medicine at Mount Sinai, United States
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - Trevor Bedford
- Brotman Baty Institute for Precision Medicine, University of Washington, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, United States
- Department of Genome Sciences, University of Washington, United States
- Howard Hughes Medical Institute, Seattle, United States
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, United States
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3
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Rybarczyk Y, Zalakeviciute R, Ortiz-Prado E. Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach. Heliyon 2024; 10:e25134. [PMID: 38322928 PMCID: PMC10844283 DOI: 10.1016/j.heliyon.2024.e25134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.
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Affiliation(s)
- Yves Rybarczyk
- School of Information and Engineering, Dalarna University, Falun, Sweden
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4
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Chen Z, Liu Y, Yue H, Chen J, Hu X, Zhou L, Liang B, Lin G, Qin P, Feng W, Wang D, Wu D. The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022. Front Public Health 2024; 11:1268073. [PMID: 38259781 PMCID: PMC10800649 DOI: 10.3389/fpubh.2023.1268073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/15/2023] [Indexed: 01/24/2024] Open
Abstract
Objective Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures. Methods The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence. Results The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group. Conclusion Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.
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Affiliation(s)
- Zhitao Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Yanhui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Haiyan Yue
- Guangzhou Meteorological Observatory, Guangzhou, 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, Guangdong, China
| | - Xiangzhi Hu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Lijuan Zhou
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Boheng Liang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Guozhen Lin
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Pengzhe Qin
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Wenru Feng
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Dedong Wang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Di Wu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Institute of Public Health, Guangzhou Medical University and Guangzhou Center for Disease Control and Prevention, Guangzhou, China
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5
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Kabir KMA, Ullah MS, Tanimoto J. Analyzing the Costs and Benefits of Utilizing a Mixed-Strategy Approach in Infectious Disease Control under a Voluntary Vaccination Policy. Vaccines (Basel) 2023; 11:1476. [PMID: 37766152 PMCID: PMC10536573 DOI: 10.3390/vaccines11091476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Infectious diseases pose significant public health risks, necessitating effective control strategies. One such strategy is implementing a voluntary vaccination policy, which grants individuals the autonomy to make their own decisions regarding vaccination. However, exploring different approaches to optimize disease control outcomes is imperative, and involves assessing their associated costs and benefits. This study analyzes the advantages and disadvantages of employing a mixed-strategy approach under a voluntary vaccination policy in infectious disease control. We examine the potential benefits of such an approach by utilizing a vaccination game model that incorporates cost and benefit factors, where lower costs and higher benefits lead to reduced infection rates. Here, we introduce a mixed-strategy framework that combines individual-based risk assessment (IB-RA) and society-based risk assessment (SB-RA) strategies. A novel dynamical equation is proposed that captures the decision-making process of individuals as they choose their strategy based on personal or communal considerations. In addition, we explore the implications of the mixed-strategy approach within the context of social dilemmas. We examine deviations from expected behavior and the concept of social efficiency deficit (SED) by allowing for the evolution of vaccine strategy preferences alongside risk perception. By comprehensively evaluating the financial implications and societal advantages associated with the mixed-strategy approach, decision-makers can allocate resources and implement measures to combat infectious diseases within the framework of a voluntary vaccination policy.
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Affiliation(s)
- K. M. Ariful Kabir
- Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | | | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 8168580, Japan;
- Faculty of Engineering Sciences, Kyushu University, Fukuoka 8168580, Japan
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6
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Han SM, Robert A, Masuda S, Yasaka T, Kanda S, Komori K, Saito N, Suzuki M, Endo A, Baguelin M, Ariyoshi K. Transmission dynamics of seasonal influenza in a remote island population. Sci Rep 2023; 13:5393. [PMID: 37012350 PMCID: PMC10068240 DOI: 10.1038/s41598-023-32537-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (Reff: 1.2-1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4-6 years old and 1.45 (95% CI 1.33, 1.59) 7-12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.
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Affiliation(s)
- Su Myat Han
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
| | - Alexis Robert
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Shingo Masuda
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Takahiro Yasaka
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Satoshi Kanda
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Kazuhiri Komori
- Department of Internal Medicine, Kamigoto Hospital, Kamigoto, Japan
| | - Nobuo Saito
- Department of Microbiology, Faculty of Medicine, Oita University, Yufu, Japan
- Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
| | - Motoi Suzuki
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Akira Endo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Marc Baguelin
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease, London, UK
| | - Koya Ariyoshi
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan
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7
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Yan Q, Cheke RA, Tang S. Coupling an individual adaptive-decision model with a SIRV model of influenza vaccination reveals new insights for epidemic control. Stat Med 2022; 42:716-729. [PMID: 36577149 PMCID: PMC9880662 DOI: 10.1002/sim.9639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 11/08/2022] [Accepted: 12/16/2022] [Indexed: 12/29/2022]
Abstract
Past seasonal influenza epidemics and vaccination experience may affect individuals' decisions on whether to be vaccinated or not, decisions that may be constantly reassessed in relation to recent influenza related experience. To understand the potentially complex interaction between experience and decisions and whether the vaccination rate is likely to reach a critical coverage level or not, we construct an adaptive-decision model. This model is then coupled with an influenza vaccination dynamics (SIRV) model to explore the interaction between individuals' decision-making and an influenza epidemic. Nonlinear least squares estimation is used to obtain the best-fit parameter values in the SIRV model based on data on new influenza-like illness (ILI) cases in Texas. Uncertainty and sensitivity analyses are then carried out to determine the impact of key parameters of the adaptive decision-making model on the ILI epidemic. The results showed that the necessary critical coverage rate of ILI vaccination could not be reached by voluntary vaccination. However, it could be reached in the fourth year if mass media reports improved individuals' memory of past vaccination experience. Individuals' memory of past vaccination experience, the proportion with histories of past vaccinations and the perceived cost of vaccination are important factors determining whether an ILI epidemic can be effectively controlled or not. Therefore, health authorities should guide people to improve their memory of past vaccination experience through media reports, publish timely data on annual vaccination proportions and adjust relevant measures to appropriately reduce vaccination perceived cost, in order to effectively control an ILI epidemic.
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Affiliation(s)
- Qinling Yan
- School of ScienceChang'an UniversityXi'anPeople's Republic of China
| | - Robert A. Cheke
- Natural Resources InstituteUniversity of Greenwich at MedwayChatham MaritimeKentUK
| | - Sanyi Tang
- School of Mathematics and StatisticsShaanxi Normal UniversityXi'anPeople's Republic of China
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8
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Zhang S, Sun Z, He J, Li Z, Han L, Shang J, Hao Y. The influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza activity in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 843:157024. [PMID: 35772553 DOI: 10.1016/j.scitotenv.2022.157024] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
Previous research has extensively studied the seasonalities of human influenza infections and the effect of specific climatic factors in different regions. However, there is limited understanding of the influences of monsoons. This study applied generalized additive model with monthly surveillance data from mainland China to explore the influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza in China. The results suggested two influenza active periods in northern China and three active periods in southern China. The study found that the northerly advancement of East Asian Summer Monsoon (EASM) influences the summer influenza spatio-temporal patterns in both southern and northern China. At the interannual scale, the north-south converse effect of EASM on influenza activity is mainly due to the converse effect of EASM on humidity and precipitation. Within the annual scale, influenza activity in southern China gradually reaches its maximum during the summer exacerbated by the northerly advancement of EASM. Furthermore, the winter epidemic in China is related to the low temperature and humidity influenced by the East Asian Winter Monsoon (EAWM). Moreover, the active period in transition season is related partially to the large rapid temperature change influenced by the transition of EAWM and EASM. Despite the delayed onset and instability, the climatic condition influenced by the East Asian Monsoon is one of the potential key drivers of influenza activity.
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Affiliation(s)
- Shuwen Zhang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhaobin Sun
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China.
| | - Juan He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Ziming Li
- Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, China Meteorological Administration, Beijing 100089, China; Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Ling Han
- State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Jing Shang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Yu Hao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
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9
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Liao Y, Xue S, Xie Y, Zhang Y, Wang D, Zhao T, Du W, Chen T, Miao H, Qin Y, Zheng J, Yang X, Peng Z, Yu J. Characterization of influenza seasonality in China, 2010-2018: Implications for seasonal influenza vaccination timing. Influenza Other Respir Viruses 2022; 16:1161-1171. [PMID: 36062624 PMCID: PMC9530570 DOI: 10.1111/irv.13047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/10/2022] [Accepted: 08/24/2022] [Indexed: 11/29/2022] Open
Abstract
Background Optimizing the timing of influenza vaccination based on regional temporal seasonal influenza illness patterns may make seasonal influenza vaccination more effective in China. Methods We obtained provincial weekly influenza surveillance data for 30 of 31 provinces in mainland China from the Chinese Center for Disease Control and Prevention for the years 2010–2018. Influenza epidemiological regions were constructed by clustering analysis. For each region, we calculated onset date, end date, and duration of seasonal influenza epidemics by the modified mean threshold method. To help identify initial vaccination target populations, we acquired weekly influenza surveillance data for four age groups (0–4, 5–18, 19–59, and ≥60 years) in each region and in 171 cities of wide‐ranging size. We used linear regression analyses to explore the association of epidemic onset dates by age group, city, and epidemiological region and provide evidence for initial target populations for seasonal influenza vaccination. Results We determined that northern, mid, southwestern, southeast regions of mainland China have distinct seasonal influenza epidemic patterns. We found significant regional, temporal, and spatial heterogeneity of seasonal influenza epidemics. There were significant differences by age group and city size in the interval between epidemic onset in the city or age group and regional spread (epidemic lead time), with longer epidemic lead times for 5‐ to 18‐year‐old children and larger cities. Conclusions Knowledge of influenza epidemic characteristics may help optimize local influenza vaccination timing and identify initial target groups for seasonal influenza vaccination in mainland China. Similar analyses may help inform seasonal influenza vaccination strategies in other regions and countries.
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Affiliation(s)
- Yilan Liao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Shan Xue
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Yiran Xie
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanping Zhang
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Dayan Wang
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tong Zhao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Du
- School of Public Health, Southeast University, Nanjing, China
| | - Tao Chen
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Miao
- College of Art and Science, Ohio State University, Columbus, Ohio, USA
| | - Ying Qin
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiandong Zheng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaokun Yang
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhibin Peng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jianxing Yu
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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10
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Barrio RA, Kaski KK, Haraldsson GG, Aspelund T, Govezensky T. A model for social spreading of Covid-19: Cases of Mexico, Finland and Iceland. PHYSICA A 2021; 582:126274. [PMID: 34305295 PMCID: PMC8285360 DOI: 10.1016/j.physa.2021.126274] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/08/2021] [Indexed: 06/13/2023]
Abstract
The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.
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Affiliation(s)
- Rafael A Barrio
- Instituto de Física, Universidad Nacional Autónoma de México, CDMX 01000, Mexico
| | - Kimmo K Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, FI-00076, Finland
- The Alan Turing Institute, 96 Euston Rd, Kings Cross, London, NW1 2DB, UK
| | | | - Thor Aspelund
- Centre for Public Health Sciences, University of Iceland, Reykjavik, Iceland
- The Icelandic Heart Association, Reykjavik, Iceland
| | - Tzipe Govezensky
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, CDMX, 04510, Mexico
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11
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Panahi MH, Parsaeian M, Mansournia MA, Gouya MM, Jafarzadeh Kohneloo A, Hemmati P, Fotouhi A. Detection of influenza epidemics using hidden Markov and Serfling approaches. Transbound Emerg Dis 2020; 68:2446-2454. [PMID: 33152160 DOI: 10.1111/tbed.13912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 10/06/2020] [Accepted: 11/01/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Detection of epidemics is a critical issue in epidemiology of infectious diseases which enable healthcare system to better control it. This study is devoted to investigating the 5-year trend in influenza and severe acute respiratory infection cases in Iran. The epidemics were also detected using the hidden Markov model (HMM) and Serfling model. STUDY DESIGN In this study, we used SARI data reported in the World Health Organization (WHO) FluNet web-based tool from August 2011 to August 2016. METHODS SARI data in Iran from August 2011 to August 2016 were used. We applied the HMM and Serfling model for indicating the two epidemic and non-epidemic phases. The registered outbreak activity recorded on the WHO website was used as the gold standard. The coefficient of determination was reported to compare the goodness of fit of the models. RESULTS Serfling models modified by 30% and 35% of the data had a sensitivity of 91.67% and 95.83%, while for 15%, 20% and 25% were 70.83%, 79.17% and 83.33%, respectively. Sensitivity of HMM and autoregressive HMM (AHMM) was 66.67% and 92.86%. All fitted models have a specificity of over 96%. The R2 for HMM and AHMM was calculated 0.73 and 0.85, respectively, showing better fitness of these models, while R2 was around 50% for different types of Serfling models. CONCLUSIONS Both modified Serfling and HMM were acceptable models in determining the epidemic points for the detection of weekly SARI. The AHMM had better fitness, higher detection power and more accurate detection of the incidence of epidemics than Serfling model and high sensitivity and specificity. In addition to AHMM, Serfling models with 30% and 35% modification can be used to detect epidemics due to approximately the same accuracy but the simplicity of the calculations.
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Affiliation(s)
- Mohammad H Panahi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad A Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad M Gouya
- Center for Communicable Disease Control, Ministry of Health & Medical Education, Tehran, Iran.,Iran University of Medical Sciences, Tehran, Iran
| | - Aarefeh Jafarzadeh Kohneloo
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Payman Hemmati
- Center for Communicable Disease Control, Ministry of Health & Medical Education, Tehran, Iran
| | - Akbar Fotouhi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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12
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Ortiz-Prado E, Simbaña-Rivera K, Gómez-Barreno L, Rubio-Neira M, Guaman LP, Kyriakidis NC, Muslin C, Jaramillo AMG, Barba-Ostria C, Cevallos-Robalino D, Sanches-SanMiguel H, Unigarro L, Zalakeviciute R, Gadian N, López-Cortés A. Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review. Diagn Microbiol Infect Dis 2020; 98:115094. [PMID: 32623267 PMCID: PMC7260568 DOI: 10.1016/j.diagmicrobio.2020.115094] [Citation(s) in RCA: 213] [Impact Index Per Article: 53.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/21/2020] [Accepted: 05/22/2020] [Indexed: 02/06/2023]
Abstract
Coronaviruses are an extensive family of viruses that can cause disease in both animals and humans. The current classification of coronaviruses recognizes 39 species in 27 subgenera that belong to the family Coronaviridae. From those, at least 7 coronaviruses are known to cause respiratory infections in humans. Four of these viruses can cause common cold-like symptoms. Those that infect animals can evolve and become infectious to humans. Three recent examples of these viral jumps include SARS CoV, MERS-CoV and SARS CoV-2 virus. They are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently discovered coronavirus disease during 2019 (COVID-19). COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. The rapid spread of the disease has taken the scientific and medical community by surprise. Latest figures from 20 May 2020 show more than 5 million people had been infected with the virus, causing more than 330,000 deaths in over 210 countries worldwide. The large amount of information received daily relating to COVID-19 is so abundant and dynamic that medical staff, health authorities, academics and the media are not able to keep up with this new pandemic. In order to offer a clear insight of the extensive literature available, we have conducted a comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19).
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Affiliation(s)
- Esteban Ortiz-Prado
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | - Katherine Simbaña-Rivera
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | - Lenin Gómez-Barreno
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | - Mario Rubio-Neira
- Hospital Baca Ortiz, Pediatric and Cardiology Department, Quito, Ecuador.
| | - Linda P Guaman
- Centro de Investigación Biomédica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.
| | - Nikolaos C Kyriakidis
- Grupo de Investigación en Biotecnología Aplicada a Biomedicina (BIOMED), Universidad de la Americas, Quito, Ecuador.
| | - Claire Muslin
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | | | - Carlos Barba-Ostria
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | | | - Hugo Sanches-SanMiguel
- One Health Research Group, Faculty of Medicine, Universidad de Las Americas (UDLA), Quito, Ecuador.
| | - Luis Unigarro
- Intensive Care Unit, Hospital SOLCA Quito, Quito, Ecuador.
| | - Rasa Zalakeviciute
- Grupo de Biodiversidad Medio Ambiente y Salud (BIOMAS), Universidad de Las Américas, Quito, Ecuador; Intelligent and Interactive Systems Lab (SI2 Lab) Universidad de Las Américas (UDLA), Quito, Ecuador.
| | - Naomi Gadian
- University of Southampton, Department of Public Health, Southampton, United Kingdome.
| | - Andrés López-Cortés
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador; Red Latinoamericana de Implementación y Validación de Guías Clínicas Farmacogenómicas (RELIVAF-CYTED), Quito, Ecuador.
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13
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Pepin KM, Pedersen K, Wan XF, Cunningham FL, Webb CT, Wilber MQ. Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations. Integr Comp Biol 2020; 59:1231-1242. [PMID: 31251341 DOI: 10.1093/icb/icz118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM-SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January-March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data.
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Affiliation(s)
- Kim M Pepin
- National Wildlife Research Center, USDA-APHIS, Wildlife Services, Fort Collins, CO 80521-2154, USA
| | - Kerri Pedersen
- USDA-APHIS, Wildlife Services, 920 Main Campus Drive, Suite 200, Raleigh, NC 27606, USA
| | - Xiu-Feng Wan
- Missouri University Center for Research on Influenza Systems Biology (CRISB), University of Missouri, Columbia, MO 65211, USA.,Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MO, USA.,Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.,MU Informatics Institute, University of Missouri, Columbia, MO, USA.,Department of Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Fred L Cunningham
- National Wildlife Research Center, USDA-APHIS, Wildlife Services, Mississippi Field Station, MS 39762, USA
| | - Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Mark Q Wilber
- National Wildlife Research Center, USDA-APHIS, Wildlife Services, Fort Collins, CO 80521-2154, USA.,Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
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14
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Degeling C, Gilbert GL, Tambyah P, Johnson J, Lysaght T. One Health and Zoonotic Uncertainty in Singapore and Australia: Examining Different Regimes of Precaution in Outbreak Decision-Making. Public Health Ethics 2019. [DOI: 10.1093/phe/phz017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
A One Health approach holds great promise for attenuating the risk and burdens of emerging infectious diseases (EIDs) in both human and animal populations. Because the course and costs of EID outbreaks are difficult to predict, One Health policies must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies. Drawing on the outcomes of parallel Delphi surveys conducted with policymakers in Singapore and Australia, we explore the normative dimensions of two different precautionary approaches to EID decision-making—which we call regimes of risk management and organizing uncertainty, respectively. The imperative to act cautiously can be seen as either an epistemic rule or as a decision rule, which has implications for how EID uncertainty is managed. The normative features of each regime, and their implications for One Health approaches to infectious disease risks and outbreaks, are described. As One Health attempts to move upstream to prevent rather than react to emergence of EIDs in humans, we show how the approaches to uncertainty, taken by experts and decision-makers, and their choices about the content and quality of evidence, have implications for who pays the price of precaution, and, thereby, social and global justice.
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Affiliation(s)
- C Degeling
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, University of Wollongong and Sydney Health Ethics, School of Public Health, University of Sydney
| | - G L Gilbert
- Sydney Health Ethics, School of Public Health, University of Sydney and Marie Bashir Institute of Infectious Diseases and Biosecurity
| | - P Tambyah
- Department of Medicine, National University of Singapore and National University Health System
| | - J Johnson
- Sydney Health Ethics, School of Public Health, University of Sydney and Marie Bashir Institute of Infectious Diseases and Biosecurity
| | - T Lysaght
- Centre for Biomedical Ethics, National University of Singapore
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15
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Clay K, Lewis J, Severnini E. What explains cross-city variation in mortality during the 1918 influenza pandemic? Evidence from 438 U.S. cities. ECONOMICS AND HUMAN BIOLOGY 2019; 35:42-50. [PMID: 31071595 DOI: 10.1016/j.ehb.2019.03.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 03/26/2019] [Accepted: 03/31/2019] [Indexed: 06/09/2023]
Abstract
Disparities in cross-city pandemic severity during the 1918 Influenza Pandemic remain poorly understood. This paper uses newly assembled historical data on annual mortality across 438 U.S. cities to explore the determinants of pandemic mortality. We assess the role of three broad factors: i) pre-pandemic population health and poverty, ii) air pollution, and iii) the timing of onset and proximity to military bases. Using regression analysis, we find that cities in the top tercile of the distribution of pre-pandemic infant mortality had 21 excess deaths per 10,000 residents in 1918 relative to cities in the bottom tercile. Similarly, cities in the top tercile of the distribution of proportion of illiterate residents had 21.3 excess deaths per 10,000 residents during the pandemic relative to cities in the bottom tercile. Cities in the top tercile of the distribution of coal-fired electricity generating capacity, an important source of urban air pollution, had 9.1 excess deaths per 10,000 residents in 1918 relative to cities in the bottom tercile. There was no statistically significant relationship between excess mortality and city proximity to World War I bases or the timing of onset. In a counterfactual analysis, the three statistically significant factors accounted for 50 percent of cross-city variation in excess mortality in 1918.
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Affiliation(s)
- Karen Clay
- Heinz College, Carnegie Mellon University, 4800 Forbes Avenue, Pittsburgh, PA, 15213, United States
| | - Joshua Lewis
- Department of Economics, University of Montreal, C.P. 6128 succ. Centre-ville, Montreal, QC, H3C 3J7, United States
| | - Edson Severnini
- Heinz College, Carnegie Mellon University, 4800 Forbes Avenue, Pittsburgh, PA, 15213, United States.
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16
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Choi SB, Kim J, Ahn I. Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries. PLoS One 2019; 14:e0220423. [PMID: 31765386 PMCID: PMC6876883 DOI: 10.1371/journal.pone.0220423] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 11/04/2019] [Indexed: 12/21/2022] Open
Abstract
To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. We identified the time lag between two time-series which were weekly surveillances for ILI, total influenza (Total INF), influenza A (INF A), and influenza B (INF B) viruses between two countries using cross-correlation analysis. In order to forecast ILI, Total INF, INF A, and INF B of next season (after 26 weeks) in the U.S., we developed prediction models using linear regression, auto regressive integrated moving average, and an artificial neural network (ANN). As a result of cross-correlation analysis between the countries located in northern and southern hemisphere, the seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of ANN models for ILI for validation set in 2015-2019 was 0.758 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation between seasonal influenza patterns in the U.S., Australia, and Chile could be used to forecast the next seasonal influenza pattern, which can help to determine influenza vaccine strategy approximately six months ahead in the U.S.
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Affiliation(s)
- Soo Beom Choi
- Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
- Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Juhyeon Kim
- Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
- Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Insung Ahn
- Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
- Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
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17
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Kalligeris EN, Karagrigoriou A, Parpoula C. Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection. Stat Methods Med Res 2019; 29:1639-1649. [PMID: 31478459 DOI: 10.1177/0962280219871587] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014-2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.
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Affiliation(s)
- Emmanouil-Nektarios Kalligeris
- Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Samos, Greece
| | - Alex Karagrigoriou
- Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Samos, Greece
| | - Christina Parpoula
- Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Samos, Greece
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Association of meteorological factors with seasonal activity of influenza A subtypes and B lineages in subtropical western China. Epidemiol Infect 2019; 147:e72. [PMID: 30869001 PMCID: PMC6518542 DOI: 10.1017/s0950268818003485] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The seasonality of individual influenza subtypes/lineages and the association of influenza epidemics with meteorological factors in the tropics/subtropics have not been well understood. The impact of the 2009 H1N1 pandemic on the prevalence of seasonal influenza virus remains to be explored. Using wavelet analysis, the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria and Yamagata were identified, respectively, in Panzhihua during 2006–2015. As a subtropical city in southwestern China, Panzhihua is the first industrial city in the upper reaches of the Yangtze River. The relationship between influenza epidemics and local climatic variables was examined based on regression models. The temporal distribution of influenza subtypes/lineages during the pre-pandemic (2006–2009), pandemic (2009) and post-pandemic (2010–2015) years was described and compared. A total of 6892 respiratory specimens were collected and 737 influenza viruses were isolated. A/H3N2 showed an annual cycle with a peak in summer–autumn, while A/H1N1pdm09, Victoria and Yamagata exhibited an annual cycle with a peak in winter–spring. Regression analyses demonstrated that relative humidity was positively associated with A/H3N2 activity while negatively associated with Victoria activity. Higher prevalence of A/H1N1pdm09 and Yamagata was driven by lower absolute humidity. The role of weather conditions in regulating influenza epidemics could be complicated since the diverse viral transmission modes and mechanism. Differences in seasonality and different associations with meteorological factors by influenza subtypes/lineages should be considered in epidemiological studies in the tropics/subtropics. The development of subtype- and lineage-specific prevention and control measures is of significant importance.
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Ferland R, Froda S. A statistical tool for comparing seasonal ILI surveillance data. Sci Rep 2019; 9:1422. [PMID: 30723245 PMCID: PMC6363783 DOI: 10.1038/s41598-018-38292-x] [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: 09/04/2018] [Accepted: 12/21/2018] [Indexed: 12/02/2022] Open
Abstract
In this paper, we consider the yearly influenza epidemic, as reflected in the seasonal surveillance data compiled by the CDC (Center for Disease Control and Prevention, USA) and we explore a new methodology for comparing specific features of these data. In particular, we focus on the ten HHS (Health and Human Services) regions, and how the incidence data evolves in these regions. In order to perform the comparisons, we consider the relative distribution of weekly new cases over one season and replace the crude data with predicted values. These predictions are obtained after fitting a negative binomial regression model that controls for important covariates. The prediction is computed on a ‘generic’ set of covariate values that takes into account the relative size (population wise) of the regions to be compared. The main results are presented in graphical form, that quickly emphasizes relevant features of the seasonal data and facilitates the comparisons.
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Affiliation(s)
- René Ferland
- Département de mathématiques, UQAM, C.P. 8888, succursale centre-ville, Montréal, Québec, H3C 3P8, Canada
| | - Sorana Froda
- Département de mathématiques, UQAM, C.P. 8888, succursale centre-ville, Montréal, Québec, H3C 3P8, Canada.
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20
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Ye C, Zhu W, Yu J, Li Z, Zhang Y, Wang Y, Gu H, Zou W, Hao L, Hu W. Understanding the complex seasonality of seasonal influenza A and B virus transmission: Evidence from six years of surveillance data in Shanghai, China. Int J Infect Dis 2019; 81:57-65. [PMID: 30684745 DOI: 10.1016/j.ijid.2019.01.027] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/06/2019] [Accepted: 01/17/2019] [Indexed: 10/27/2022] Open
Abstract
OBJECTIVES Understanding the complexity of influenza subtype seasonality is critical to promoting a suitable vaccination program. The aim of this study was to identify and compare the seasonality and epidemiological features of seasonal influenza subtypes after the 2009 A/H1N1 pandemic and to lay a foundation for further investigation into the social and environmental factors affecting seasonal influenza virus transmission. METHODS Influenza-like illness (ILI) case surveillance was conducted in two sentinel hospitals in Pudong New Area, Shanghai between 2012 and 2018. Weekly data on ILI cases were analyzed. A time-series seasonal decomposition analysis was used to reveal the seasonality of influenza and epidemiological features among different subtypes. RESULTS In total, 10977 ILI patients were enrolled of whom 2385 (21.7%) had laboratory-confirmed influenza. Compared to influenza A (16.3%), influenza B (5.4%) was less frequently detected among the ILI patients (p<0.001). Semiannual epidemic peaks were identified in four of the years during the 6-year study period, while only one annual epidemic peak was found in the other two years. An epidemic peak occurred in each winter season, and a secondary peak also occasionally occurred in summer or spring. A/H3N2 predominated in both summer and winter, while A/H1N1, B/Yamagata, and B/Victoria circulated almost exclusively in winter or spring. Two lineages of influenza B seemed to predominate in alternating years. CONCLUSIONS This study highlights the complexity of seasonal influenza virus activity in a subtropical region of China, presenting both semiannual and annual epidemic peaks in different years. The results of this study may provide further insight into possible improvements in the timing of influenza vaccination in Shanghai, China.
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Affiliation(s)
- Chuchu Ye
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China; School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Weiping Zhu
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Jianxing Yu
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuzhou Zhang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Yuanping Wang
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Huozheng Gu
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Wenwei Zou
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Lipeng Hao
- Research Base of Key Laboratory of Surveillance and Early Warning of Infectious Disease, Pudong New Area Center for Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Shanghai, China.
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
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21
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Coletti P, Poletto C, Turbelin C, Blanchon T, Colizza V. Shifting patterns of seasonal influenza epidemics. Sci Rep 2018; 8:12786. [PMID: 30143689 PMCID: PMC6109160 DOI: 10.1038/s41598-018-30949-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 07/24/2018] [Indexed: 12/25/2022] Open
Abstract
Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.
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Affiliation(s)
- Pietro Coletti
- ISI Foundation, Turin, Italy
- Universiteit Hasselt, I-Biostat, 3500, Hasselt, Belgium
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Clément Turbelin
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Thierry Blanchon
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France.
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22
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Domenech de Cellès M, Arduin H, Varon E, Souty C, Boëlle PY, Lévy-Bruhl D, van der Werf S, Soulary JC, Guillemot D, Watier L, Opatowski L. Characterizing and Comparing the Seasonality of Influenza-Like Illnesses and Invasive Pneumococcal Diseases Using Seasonal Waveforms. Am J Epidemiol 2018; 187:1029-1039. [PMID: 29053767 DOI: 10.1093/aje/kwx336] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 10/06/2017] [Indexed: 11/13/2022] Open
Abstract
The seasonalities of influenza-like illnesses (ILIs) and invasive pneumococcal diseases (IPDs) remain incompletely understood. Experimental evidence indicates that influenza-virus infection predisposes to pneumococcal disease, so that a correspondence in the seasonal patterns of ILIs and IPDs might exist at the population level. We developed a method to characterize seasonality by means of easily interpretable summary statistics of seasonal shape-or seasonal waveforms. Nonlinear mixed-effects models were used to estimate those waveforms based on weekly case reports of ILIs and IPDs in 5 regions spanning continental France from July 2000 to June 2014. We found high variability of ILI seasonality, with marked fluctuations of peak amplitudes and peak times, but a more conserved epidemic duration. In contrast, IPD seasonality was best modeled by a markedly regular seasonal baseline, punctuated by 2 winter peaks in late December to early January and January to February. Comparing ILI and IPD seasonal waveforms, we found indication of a small, positive correlation. Direct models regressing IPDs on ILIs provided comparable results, even though they estimated moderately larger associations. The method proposed is broadly applicable to diseases with unambiguous seasonality and is well-suited to analyze spatially or temporally grouped data, which are common in epidemiology.
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Affiliation(s)
| | - Hélène Arduin
- Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases
| | - Emmanuelle Varon
- Assistance publique–Hôpitaux de Paris
- Centre National de Référence des Pneumocoques, Paris, France
| | - Cécile Souty
- Sorbonne Universités, Université Pierre et Marie Curie–UPMC
| | | | | | - Sylvie van der Werf
- Institut Pasteur, Unité de Génétique Moléculaire des Virus à ARN, Département de Virologie, Paris, France
- Centre national de la recherche scientifique
- Université Paris Diderot, Sorbonne Paris Cité, Unité de Génétique Moléculaire des Virus à ARN, Paris, France
| | | | - Didier Guillemot
- Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases
| | - Laurence Watier
- Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases
| | - Lulla Opatowski
- Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases
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23
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Geoghegan JL, Saavedra AF, Duchêne S, Sullivan S, Barr I, Holmes EC. Continental synchronicity of human influenza virus epidemics despite climatic variation. PLoS Pathog 2018; 14:e1006780. [PMID: 29324895 PMCID: PMC5764404 DOI: 10.1371/journal.ppat.1006780] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 11/29/2017] [Indexed: 01/26/2023] Open
Abstract
The factors that determine the pattern and rate of spread of influenza virus at a continental-scale are uncertain. Although recent work suggests that influenza epidemics in the United States exhibit a strong geographical correlation, the spatiotemporal dynamics of influenza in Australia, a country and continent of approximately similar size and climate complexity but with a far smaller population, are not known. Using a unique combination of large-scale laboratory-confirmed influenza surveillance comprising >450,000 entries and genomic sequence data we determined the local-level spatial diffusion of this important human pathogen nationwide in Australia. We used laboratory-confirmed influenza data to characterize the spread of influenza virus across Australia during 2007-2016. The onset of established epidemics varied across seasons, with highly synchronized epidemics coinciding with the emergence of antigenically distinct viruses, particularly during the 2009 A/H1N1 pandemic. The onset of epidemics was largely synchronized between the most populous cities, even those separated by distances of >3000 km and those that experience vastly diverse climates. In addition, by analyzing global phylogeographic patterns we show that the synchronized dissemination of influenza across Australian cities involved multiple introductions from the global influenza population, coupled with strong domestic connectivity, rather than through the distinct radial patterns of geographic dispersal that are driven by work-flow transmission as observed in the United States. In addition, by comparing the spatial structure of influenza A and B, we found that these viruses tended to occupy different geographic regions, and peak in different seasons, perhaps indicative of moderate cross-protective immunity or viral interference effects. The highly synchronized outbreaks of influenza virus at a continental-scale revealed here highlight the importance of coordinated public health responses in the event of the emergence of a novel, human-to-human transmissible, virus.
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Affiliation(s)
- Jemma L. Geoghegan
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
- Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
| | - Aldo F. Saavedra
- Centre for Translational Data Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Sebastián Duchêne
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
| | - Sheena Sullivan
- World Health Organization (WHO) Collaborating Centre for Reference and Research on Influenza, Melbourne, Victoria, Australia
| | - Ian Barr
- World Health Organization (WHO) Collaborating Centre for Reference and Research on Influenza, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, Victoria, Australia
- Faculty of Science and Technology, Federation University Australia, Gippsland Campus, Churchill, Victoria, Australia
| | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
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24
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Performances of statistical methods for the detection of seasonal influenza epidemics using a consensus-based gold standard. Epidemiol Infect 2017; 146:168-176. [PMID: 29208062 DOI: 10.1017/s095026881700276x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Influenza epidemics are monitored using influenza-like illness (ILI) data reported by health-care professionals. Timely detection of the onset of epidemics is often performed by applying a statistical method on weekly ILI incidence estimates with a large range of methods used worldwide. However, performance evaluation and comparison of these algorithms is hindered by: (1) the absence of a gold standard regarding influenza epidemic periods and (2) the absence of consensual evaluation criteria. As of now, performance evaluations metrics are based only on sensitivity, specificity and timeliness of detection, since definitions are not clear for time-repeated measurements such as weekly epidemic detection. We aimed to evaluate several epidemic detection methods by comparing their alerts to a gold standard determined by international expert consensus. We introduced new performance metrics that meet important objective of influenza surveillance in temperate countries: to detect accurately the start of the single epidemic period each year. Evaluations are presented using ILI incidence in France between 1995 and 2011. We found that the two performance metrics defined allowed discrimination between epidemic detection methods. In the context of performance detection evaluation, other metrics used commonly than the standard could better achieve the needs of real-time influenza surveillance.
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25
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Ndegwa LK, Emukule G, Uyeki TM, Mailu E, Chaves SS, Widdowson MA, Lewa BV, Muiruri FK, Omoth P, Fields B, Mott JA. Evaluation of the point-of-care Becton Dickinson Veritor™ Rapid influenza diagnostic test in Kenya, 2013-2014. BMC Infect Dis 2017; 17:60. [PMID: 28077093 PMCID: PMC5225564 DOI: 10.1186/s12879-016-2131-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/15/2016] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND We evaluated the performance of the Becton Dickinson Veritor™ System Flu A + B rapid influenza diagnostic test (RIDT) to detect influenza viruses in respiratory specimens from patients enrolled at five surveillance sites in Kenya, a tropical country where influenza seasonality is variable. METHODS Nasal swab (NS) and nasopharyngeal (NP)/oropharyngeal (OP) swabs were collected from patients with influenza like illness and/or severe acute respiratory infection. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the RIDT using NS specimens were evaluated against nasal swabs tested by real time reverse transcription polymerase chain reaction (rRT-PCR). The performance parameter results were expressed as 95% confidence intervals (CI) calculated using binomial exact methods, with P < 0.05 considered significant. Two-sample Z tests were used to test for differences in sample proportions. Analysis was performed using SAS software version 9.3. RESULTS From July 2013 to July 2014, 3,569 patients were recruited, of which 78.7% were aged <5 years. Overall, 14.4% of NS specimens were influenza-positive by RIDT. RIDT overall sensitivity was 77.1% (95% CI 72.8-81.0%) and specificity was 94.9% (95% CI 94.0-95.7%) compared to rRT-PCR using NS specimens. RIDT sensitivity for influenza A virus compared to rRT-PCR using NS specimens was 71.8% (95% CI 66.7-76.4%) and was significantly higher than for influenza B which was 43.8% (95% CI 33.8-54.2%). PPV ranged from 30%-80% depending on background prevalence of influenza. CONCLUSION Although the variable seasonality of influenza in tropical Africa presents unique challenges, RIDTs may have a role in making influenza surveillance sustainable in more remote areas of Africa, where laboratory capacity is limited.
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Affiliation(s)
- Linus K. Ndegwa
- DGHP, Centers for Disease Control and Prevention, Nairobi, Kenya
- Infection Control African Network (ICAN), Infection prevention network-Kenya (IPNET-K), Mbagathi Road off Mbagathi way, Village Market, PO Box 606, 00621 Nairobi, Kenya
| | - Gideon Emukule
- DGHP, Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Timothy M. Uyeki
- Influenza Division, Centers for Disease Control and Prevention-Atlanta, Georgia, USA
| | - Eunice Mailu
- Kenya Medical Research Institute/Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya
| | - Sandra S. Chaves
- DGHP, Centers for Disease Control and Prevention, Nairobi, Kenya
| | | | | | | | | | - Barry Fields
- DGHP, Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Joshua A. Mott
- Influenza Division, Centers for Disease Control and Prevention-Atlanta, Georgia, USA
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26
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Flahault A, de Castaneda RR, Bolon I. Climate change and infectious diseases. Public Health Rev 2016; 37:21. [PMID: 29450063 PMCID: PMC5810060 DOI: 10.1186/s40985-016-0035-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 10/11/2016] [Indexed: 11/26/2022] Open
Abstract
Global changes are major determinants for infectious diseases, although attributable, part of climate change remains debatable. Vector-borne diseases are prone to be impacted by global warming, although other factors may play a substantial role, evidenced by the dramatic decrease in malaria in the last decades in places where climate change has deep and significant effects. There is now evidence that in some areas of the world, e.g. Horn of Africa, warm El Niño Southern Oscillations (ENSO), which are observed in the South Pacific Ocean, are associated with higher risk of emergence of Rift Valley fever, cholera and malaria and during cold La Niña events, dengue fever, chikungunya and yellow fever. This has been observed for these and other diseases in other parts of the world. For example, seasonal influenza outbreaks have been more intense (i.e. higher number) and more severe (i.e. higher mortality) when concomitant with La Niña events. Since climate scientists have recently observed that climate change is tied to more frequent and more intense ENSO events, we may foresee increases in frequency and severity in emerging infectious diseases in the world.
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Affiliation(s)
- Antoine Flahault
- 1Centre Virchow-Villermé, Descartes School of Medicine, Université Sorbonne Paris Cité, Paris, France.,2Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | - Isabelle Bolon
- 2Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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27
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Emukule GO, Mott JA, Spreeuwenberg P, Viboud C, Commanday A, Muthoka P, Munywoki PK, Nokes DJ, van der Velden K, Paget JW. Influenza activity in Kenya, 2007-2013: timing, association with climatic factors, and implications for vaccination campaigns. Influenza Other Respir Viruses 2016; 10:375-85. [PMID: 27100128 PMCID: PMC4947939 DOI: 10.1111/irv.12393] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2016] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Information on the timing of influenza circulation remains scarce in Tropical regions of Africa. OBJECTIVES We assessed the relationship between influenza activity and several meteorological factors (temperature, specific humidity, precipitation) and characterized the timing of influenza circulation and its implications to vaccination strategies in Kenya. METHODS We analyzed virologically confirmed influenza data for outpatient influenza-like illness (ILI), hospitalized for severe acute respiratory infections (SARI), and cases of severe pneumonia over the period 2007-2013. Using logistic and negative binomial regression methods, we assessed the independent association between climatic variables (lagged up to 4 weeks) and influenza activity. RESULTS There were multiple influenza epidemics occurring each year and lasting a median duration of 2-4 months. On average, there were two epidemics occurring each year in most of the regions in Kenya, with the first epidemic occurring between the months of February and March and the second one between July and November. Specific humidity was independently and negatively associated with influenza activity. Combinations of low temperature (<18°C) and low specific humidity (<11 g/kg) were significantly associated with increased influenza activity. CONCLUSIONS Our study broadens understanding of the relationships between seasonal influenza activity and meteorological factors in the Kenyan context. While rainfall is frequently thought to be associated with influenza circulation in the tropics, the present findings suggest low humidity is more important in Kenya. If annual vaccination were a component of a vaccination strategy in Kenya, the months of April to June are proposed as optimal for associated campaigns.
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Affiliation(s)
- Gideon O Emukule
- Centers for Disease Control and Prevention - Kenya Country Office, Nairobi, Kenya.,Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Joshua A Mott
- Centers for Disease Control and Prevention - Kenya Country Office, Nairobi, Kenya.,Influenza Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, GA, USA.,US Public Health Service, Rockville, MD, USA
| | - Peter Spreeuwenberg
- Netherlands Institute for Health Services research (NIVEL), Utrecht, The Netherlands
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Alexander Commanday
- Influenza Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Patrick K Munywoki
- Kenya Medical Research Institute, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya
| | - David J Nokes
- Kenya Medical Research Institute, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya.,School of Life Sciences, University of Warwick, Coventry, UK
| | - Koos van der Velden
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands
| | - John W Paget
- Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands.,Netherlands Institute for Health Services research (NIVEL), Utrecht, The Netherlands
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28
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Roussel M, Pontier D, Cohen JM, Lina B, Fouchet D. Quantifying the role of weather on seasonal influenza. BMC Public Health 2016; 16:441. [PMID: 27230111 PMCID: PMC4881007 DOI: 10.1186/s12889-016-3114-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 05/12/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Improving knowledge about influenza transmission is crucial to upgrade surveillance network and to develop accurate predicting models to enhance public health intervention strategies. Epidemics usually occur in winter in temperate countries and during the rainy season for tropical countries, suggesting a climate impact on influenza spread. Despite a lot of studies, the role of weather on influenza spread is not yet fully understood. In the present study, we investigated this issue at two different levels. METHODS First, we evaluated how weekly (intra-annual) incidence variations of clinical diseases could be linked to those of climatic factors. We considered that only a fraction of the human population is susceptible at the beginning of a year due to immunity acquired from previous years. Second, we focused on epidemic sizes (cumulated number of clinical reported cases) and looked at how their inter-annual and regional variations could be related to differences in the winter climatic conditions of the epidemic years over the regions. We quantified the impact of fifteen climatic variables in France using the Réseau des GROG surveillance network incidence data over eleven regions and nine years. RESULTS At the epidemic scale, no impact of climatic factors was highlighted. At the intra-annual scale, six climatic variables had a significant impact: average temperature (5.54 ± 1.09 %), absolute humidity (5.94 ± 1.08 %), daily variation of absolute humidity (3.02 ± 1.17 %), sunshine duration (3.46 ± 1.06 %), relative humidity (4.92 ± 1.20 %) and daily variation of relative humidity (4.46 ± 1.24 %). Since in practice the impact of two highly correlated variables is very hard to disentangle, we performed a principal component analysis that revealed two groups of three highly correlated climatic variables: one including the first three highlighted climatic variables on the one hand, the other including the last three ones on the other hand. CONCLUSIONS These results suggest that, among the six factors that appeared to be significant, only two (one per group) could in fact have a real effect on influenza spread, although it is not possible to determine which one based on a purely statistical argument. Our results support the idea of an important role of climate on the spread of influenza.
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Affiliation(s)
- Marion Roussel
- University Lyon 1, CNRS, UMR 5558, Biometry and Evolutionary Biology laboratory, Bât. Grégor Mendel 43 bd du 11 novembre 1918, Villeurbanne Cedex, F-69622, France.
- LabEx ECOFECT, Eco-evolutionary Dynamics of infectious Diseases, University of Lyon, Lyon, France.
| | - Dominique Pontier
- University Lyon 1, CNRS, UMR 5558, Biometry and Evolutionary Biology laboratory, Bât. Grégor Mendel 43 bd du 11 novembre 1918, Villeurbanne Cedex, F-69622, France
- LabEx ECOFECT, Eco-evolutionary Dynamics of infectious Diseases, University of Lyon, Lyon, France
| | | | - Bruno Lina
- Laboratory of Virology, Centre National de Référence des Virus Influenzae, Hospices Civils de Lyon, Lyon, France
- Virpath, EA4610, Faculty of Medecine Lyon Est, University Claude Bernard Lyon 1, Cedex08, Lyon, 69372, France
| | - David Fouchet
- University Lyon 1, CNRS, UMR 5558, Biometry and Evolutionary Biology laboratory, Bât. Grégor Mendel 43 bd du 11 novembre 1918, Villeurbanne Cedex, F-69622, France
- LabEx ECOFECT, Eco-evolutionary Dynamics of infectious Diseases, University of Lyon, Lyon, France
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The Role of Influenza in the Delay between Low Temperature and Ischemic Heart Disease: Evidence from Simulation and Mortality Data from Japan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13050454. [PMID: 27136571 PMCID: PMC4881079 DOI: 10.3390/ijerph13050454] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Revised: 03/22/2016] [Accepted: 04/21/2016] [Indexed: 11/22/2022]
Abstract
Many studies have found that cardiovascular deaths mostly occur within a few days of exposure to heat, whereas cold-related deaths can occur up to 30 days after exposure. We investigated whether influenza infection could explain the delayed cold effects on ischemic heart diseases (IHD) as they can trigger IHD. We hypothesized two pathways between cold exposure and IHD: a direct pathway and an indirect pathway through influenza infection. We created a multi-state model of the pathways and simulated incidence data to examine the observed delayed patterns in cases. We conducted cross-correlation and time series analysis with Japanese daily pneumonia and influenza (P&I) mortality data to help validate our model. Simulations showed the IHD incidence through the direct pathway occurred mostly within 10 days, while IHD through influenza infection peaked at 4–6 days, followed by delayed incidences of up to 20–30 days. In the mortality data from Japan, P&I lagged IHD in cross-correlations. Time series analysis showed strong delayed cold effects in the older population. There was also a strong delay on intense days of influenza which was more noticeable in the older population. Influenza can therefore be a plausible explanation for the delayed association between cold exposure and cardiovascular mortality.
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Arriaga-Pizano L, Ferat-Osorio E, Rodríguez-Abrego G, Mancilla-Herrera I, Domínguez-Cerezo E, Valero-Pacheco N, Pérez-Toledo M, Lozano-Patiño F, Laredo-Sánchez F, Malagón-Rangel J, Nellen-Hummel H, González-Bonilla C, Arteaga-Troncoso G, Cérbulo-Vázquez A, Pastelin-Palacios R, Klenerman P, Isibasi A, López-Macías C. Differential Immune Profiles in Two Pandemic Influenza A(H1N1)pdm09 Virus Waves at Pandemic Epicenter. Arch Med Res 2015; 46:651-8. [PMID: 26696552 PMCID: PMC4914610 DOI: 10.1016/j.arcmed.2015.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 12/01/2015] [Indexed: 11/26/2022]
Abstract
Background and Aims Severe influenza A(H1N1)pdm2009 virus infection cases are characterized by sustained immune activation during influenza pandemics. Seasonal flu data suggest that immune mediators could be modified by wave-related changes. Our aim was to determine the behavior of soluble and cell-related mediators in two waves at the epicenter of the 2009 influenza pandemic. Methods Leukocyte surface activation markers were studied in serum from peripheral blood samples, collected from the 1st (April–May, 2009) and 2nd (October 2009–February 2010) pandemic waves. Patients with confirmed influenza A(H1N1)pdm2009 virus infection (H1N1), influenza-like illness (ILI) or healthy donors (H) were analyzed. Results Serum IL-6, IL-4 and IL-10 levels were elevated in H1N1 patients from the 2nd pandemic wave. Additionally, the frequency of helper and cytotoxic T cells was reduced during the 1st wave, whereas CD69 expression in helper T cells was increased in the 2nd wave for both H1N1 and ILI patients. In contrast, CD62L expression in granulocytes from the ILI group was increased in both waves but in monocytes only in the 2nd wave. Triggering Receptor Expressed on Myeloid cells (TREM)-1 expression was elevated only in H1N1 patients at the 1st wave. Conclusions Our results show that during the 2009 influenza pandemic a T cell activation phenotype is observed in a wave-dependent fashion, with an expanded activation in the 2nd wave, compared to the 1st wave. Conversely, granulocyte and monocyte activation is infection-dependent. This evidence collected at the pandemic epicenter in 2009 could help us understand the differences in the underlying cellular mechanisms that drive the wave-related immune profile behaviors that occur against influenza viruses during pandemics.
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Affiliation(s)
- Lourdes Arriaga-Pizano
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - Eduardo Ferat-Osorio
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico; Gastrointestinal Surgery Service, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | | | - Ismael Mancilla-Herrera
- Infectology and Immunology department, National Institute of Perinatology, SSA, Mexico City, Mexico
| | - Esteban Domínguez-Cerezo
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico; Graduate Program on Immunology, ENCB-IPN, Mexico City, Mexico
| | - Nuriban Valero-Pacheco
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico; Graduate Program on Immunology, ENCB-IPN, Mexico City, Mexico
| | - Marisol Pérez-Toledo
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico; Graduate Program on Immunology, ENCB-IPN, Mexico City, Mexico
| | - Fernando Lozano-Patiño
- Internal Medicine Service, Specialties Hospital of the National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - Fernando Laredo-Sánchez
- Internal Medicine Service, Specialties Hospital of the National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - José Malagón-Rangel
- Internal Medicine Service, Specialties Hospital of the National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - Haiko Nellen-Hummel
- Internal Medicine Service, Specialties Hospital of the National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - César González-Bonilla
- Unit for Epidemiological Surveillance, National Medical Center La Raza, IMSS, Mexico City, Mexico
| | | | | | | | - Paul Klenerman
- Oxford Biomedical Research Centre and Oxford Martin School, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Armando Isibasi
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico
| | - Constantino López-Macías
- Medical Research Unit in Immunochemistry, Specialties Hospital, National Medical Center Siglo XXI, IMSS, Mexico City, Mexico; Visiting Professor of Immunology, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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Tamerius J, Viboud C, Shaman J, Chowell G. Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009. PLoS Comput Biol 2015; 11:e1004337. [PMID: 26291446 PMCID: PMC4546376 DOI: 10.1371/journal.pcbi.1004337] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 05/08/2015] [Indexed: 11/23/2022] Open
Abstract
While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school-related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.
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Affiliation(s)
- James Tamerius
- Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, Iowa, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jeffrey Shaman
- Environmental Health Sciences, Columbia University, New York, New York, United States of America
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
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Seasonality in the migration and establishment of H3N2 Influenza lineages with epidemic growth and decline. BMC Evol Biol 2014; 14:272. [PMID: 25539729 PMCID: PMC4316805 DOI: 10.1186/s12862-014-0272-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 12/12/2014] [Indexed: 01/08/2023] Open
Abstract
Background Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. In this study, we incorporate time-varying migration rates in a Bayesian MCMC framework. We focus on migration within China, and to and from North-America as case studies, then expand the analysis to global communities. Results Incorporating seasonally varying migration rates improves the modeling of migration in our regional case studies, and also in a global context. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineages go extinct and fail to contribute to long term viral evolution, as measured through the trunk of the phylogeny. However, the fraction of the trunk in each community was found to be better determined by its overall human population size. Conclusions Seasonal migration and rapid turnover within regions is sustained by the invasion of 'fertile epidemic grounds' at the end of older epidemics. Thus, the current emphasis on connectivity, including air-travel, should be complemented with a better understanding of the conditions and timing required for successful establishment. Models which account for migration seasonality will improve our understanding of the seasonal drivers of influenza, enhance epidemiological predictions, and ameliorate vaccine updating by identifying strains that not only escape immunity but also have the seasonal opportunity to establish and spread. Further work is also needed on additional conditions that contribute to the persistence and long term evolution of influenza within the human population, such as spatial heterogeneity with respect to climate and seasonality. Electronic supplementary material The online version of this article (doi:10.1186/s12862-014-0272-2) contains supplementary material, which is available to authorized users.
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Barnea O, Huppert A, Katriel G, Stone L. Spatio-temporal synchrony of influenza in cities across Israel: the "Israel is one city" hypothesis. PLoS One 2014; 9:e91909. [PMID: 24622820 PMCID: PMC3951499 DOI: 10.1371/journal.pone.0091909] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2013] [Accepted: 02/18/2014] [Indexed: 11/19/2022] Open
Abstract
We analysed an 11-year dataset (1998-2009) of Influenza-Like Illness (ILI) that was based on surveillance of ∽23% of Israel's population. We examined whether the level of synchrony of ILI epidemics in Israel's 12 largest cities is high enough to view Israel as a single epidemiological unit. Two methods were developed to assess the synchrony: (1) City-specific attack rates were fitted to a simple model in order to estimate the temporal differences in attack rates and spatial differences in reporting rates of ILI. The model showed good fit to the data (R2 = 0.76) and revealed considerable differences in reporting rates of ILI in different cities (up to a factor of 2.2). (2) A statistical test was developed to examine the null hypothesis (H0) that ILI incidence curves in two cities are essentially identical, and was tested using ILI data. Upon examining all possible pairs of incidence curves, 77.4% of pairs were found not to be different (H0 was not rejected). It was concluded that all cities generally have the same attack rate and follow the same epidemic curve each season, although the attack rate changes from season to season, providing strong support for the "Israel is one city" hypothesis. The cities which were the most out of synchronization were Bnei Brak, Beersheba and Haifa, the latter two being geographically remote from all other cities in the dataset and the former geographically very close to several other cities but socially separate due to being populated almost exclusively by ultra-orthodox Jews. Further evidence of assortative mixing of the ultra-orthodox population can be found in the 2001-2002 season, when ultra-orthodox cities and neighborhoods showed distinctly different incidence curves compared to the general population.
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Affiliation(s)
- Oren Barnea
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Amit Huppert
- The Gertner Institute, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Guy Katriel
- Department of Mathematics, ORT Braude College, Karmiel, Israel
| | - Lewi Stone
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
- School of Mathematics and Geospatial Sciences, RMIT University, Melbourne, Victoria, Australia
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The spread process of epidemic influenza in the continental United States, 1968-2008. Spat Spatiotemporal Epidemiol 2014; 8:35-45. [PMID: 24606993 DOI: 10.1016/j.sste.2014.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Revised: 11/12/2013] [Accepted: 01/10/2014] [Indexed: 11/22/2022]
Abstract
Understanding the quantitative disease dynamics of influenza is important in developing strategies to control its spread. This research analyzed the dominant spread process of epidemic influenza in the continental United States over a 41-year period. Spatial autocorrelation and simple correlation were applied to pneumonia and influenza mortality to observe the effect of distance and population on the between-state transmission of seasonal influenza. Annual influenza epidemics exhibited distance-based spatial spread at the peak of activity, but did not undergo significant population-based spread at any point. Geographically-close states (<500 miles) showed higher correlations in the start, peak and end of annual epidemics compared with geographically-distant states. Additionally, significant local clustering was found in the Midwest, Ohio River Valley and Northeastern regions as well as Nevada and Utah throughout an influenza season. This research may be combined with others in order to determine the main epidemic pathways of seasonal influenza in the US.
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Tan Y, Lam TTY, Wu C, Lee SS, Viboud C, Zhang R, Weinberger DM. Increasing similarity in the dynamics of influenza in two adjacent subtropical Chinese cities following the relaxation of border restrictions. J Gen Virol 2013; 95:531-538. [PMID: 24310518 DOI: 10.1099/vir.0.059998-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The drivers of influenza seasonality remain heavily debated, especially in tropical/subtropical regions where influenza activity can peak in winter, during the rainy season, or remain constant throughout the year. We compared the epidemiological and evolutionary patterns of seasonal influenza epidemics in Hong Kong and Shenzhen, two adjacent cities in subtropical southern China. This comparison represents a unique natural experiment, as connectivity between these two cities has increased over the past decade. We found that, whilst summer influenza epidemics in Shenzhen used to peak 1-3 months later than those in Hong Kong, the difference decreased after 2005 (P<0.0001). Phylogenetic analysis revealed that influenza isolates from Shenzhen have become genetically closer to those circulating in Hong Kong over time (P = 0.045). Furthermore, although Shenzhen isolates used to be more distant from the global putative source of influenza viruses than isolates from Hong Kong (P<0.001), this difference has narrowed (P = 0.02). Overall, our study reveals that influenza activities show remarkably distinct epidemiological and evolutionary patterns in adjacent subtropical cities and suggests that human mobility patterns can play a major role in influenza dynamics in the subtropics.
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Affiliation(s)
- Yi Tan
- Stanley Ho Centre for Emerging Infectious Diseases, School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Chunli Wu
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, China
| | - Shui-Shan Lee
- Stanley Ho Centre for Emerging Infectious Diseases, School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Renli Zhang
- Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong, China
| | - Daniel M Weinberger
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06520, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
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Ward J, Raude J. Understanding influenza vaccination behaviors: a comprehensive sociocultural framework. Expert Rev Vaccines 2013; 13:17-29. [DOI: 10.1586/14760584.2014.863156] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Mahamat A, Dussart P, Bouix A, Carvalho L, Eltges F, Matheus S, Miller MA, Quenel P, Viboud C. Climatic drivers of seasonal influenza epidemics in French Guiana, 2006-2010. J Infect 2013; 67:141-7. [PMID: 23597784 PMCID: PMC3718068 DOI: 10.1016/j.jinf.2013.03.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 03/22/2013] [Accepted: 03/25/2013] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Influenza seasonality remains poorly studied in Equatorial regions. Here we assessed the seasonal characteristics and environmental drivers of influenza epidemics in French Guiana, where influenza surveillance was established in 2006. METHODS Sentinel GPs monitored weekly incidence of Influenza-like illnesses (ILI) from January 2006 through December 2010 and collected nasopharyngeal specimens from patients for virological confirmation. Times series analysis was used to investigate relationship between ILI and climatic parameters (rainfall and specific humidity). RESULTS Based on 1533 viruses identified during the study period, we observed marked seasonality in the circulation of influenza virus in the pre-pandemic period, followed by year-round activity in the post-pandemic period, with a peak in the rainy season. ILI incidence showed seasonal autoregressive variation based on ARIMA analysis. Multivariate dynamic regression revealed that a 1 mm increase of rainfall resulted in an increase of 0.33% in ILI incidence one week later, adjusting for specific humidity (SH). Conversely, an increase of 1 g/kg of SH resulted in a decrease of 11% in ILI incidence 3 weeks later, adjusting for rainfall. CONCLUSIONS Increased rainfall and low levels of specific humidity favour influenza transmission in French Guiana.
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Affiliation(s)
- A Mahamat
- Fogarty International Centre, National Institutes of Health, Bethesda, USA.
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Muscatello DJ, Newall AT, Dwyer DE, Macintyre CR. Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach. PLoS One 2013; 8:e64734. [PMID: 23755139 PMCID: PMC3670851 DOI: 10.1371/journal.pone.0064734] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 04/17/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate. AIM To estimate Australian seasonal and pandemic influenza-attributable mortality from 2003 to 2009, and to assess a more flexible influenza mortality estimation approach. METHODS We used a semi-parametric generalized additive model (GAM) to replace the conventional seasonal harmonic terms with a smoothing spline of time ('spline model') to estimate influenza-attributable respiratory, respiratory and circulatory, and all-cause mortality in persons aged <65 and ≥ 65 years. Influenza A(H1N1)pdm09, seasonal influenza A and B virus laboratory detection time series were used as independent variables. Model fit and estimates were compared with those of a harmonic model. RESULTS Compared with the harmonic model, the spline model improved model fit by up to 20%. In <65 year-olds, the estimated respiratory mortality attributable to pandemic influenza A(H1N1)pdm09 was 0.5 (95% confidence interval (CI), 0.3, 0.7) per 100,000; similar to that of the years with the highest seasonal influenza A mortality, 2003 and 2007 (A/H3N2 years). In ≥ 65 year-olds, the highest annual seasonal influenza A mortality estimate was 25.8 (95% CI 22.2, 29.5) per 100,000 in 2003, five-fold higher than the non-statistically significant 2009 pandemic influenza estimate in that age group. Seasonal influenza B mortality estimates were negligible. CONCLUSIONS The spline model achieved a better model fit. The study provides additional evidence that seasonal influenza, particularly A/H3N2, remains an important cause of mortality in Australia and that the epidemic of pandemic influenza A (H1N1)pdm09 virus in 2009 did not result in mortality greater than seasonal A/H3N2 influenza mortality, even in younger age groups.
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Affiliation(s)
- David J Muscatello
- School of Public Health and Community Medicine, University of New South Wales, Kensington, New South Wales, Australia.
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Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog 2013; 9:e1003194. [PMID: 23505366 PMCID: PMC3591336 DOI: 10.1371/journal.ppat.1003194] [Citation(s) in RCA: 321] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Accepted: 12/26/2012] [Indexed: 11/19/2022] Open
Abstract
Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: "cold-dry" and "humid-rainy". For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11-12 g/kg and 18-21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates.
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Bloom-Feshbach K, Alonso WJ, Charu V, Tamerius J, Simonsen L, Miller MA, Viboud C. Latitudinal variations in seasonal activity of influenza and respiratory syncytial virus (RSV): a global comparative review. PLoS One 2013; 8:e54445. [PMID: 23457451 PMCID: PMC3573019 DOI: 10.1371/journal.pone.0054445] [Citation(s) in RCA: 274] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 12/11/2012] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND There is limited information on influenza and respiratory syncytial virus (RSV) seasonal patterns in tropical areas, although there is renewed interest in understanding the seasonal drivers of respiratory viruses. METHODS We review geographic variations in seasonality of laboratory-confirmed influenza and RSV epidemics in 137 global locations based on literature review and electronic sources. We assessed peak timing and epidemic duration and explored their association with geography and study settings. We fitted time series model to weekly national data available from the WHO influenza surveillance system (FluNet) to further characterize seasonal parameters. RESULTS Influenza and RSV activity consistently peaked during winter months in temperate locales, while there was greater diversity in the tropics. Several temperate locations experienced semi-annual influenza activity with peaks occurring in winter and summer. Semi-annual activity was relatively common in tropical areas of Southeast Asia for both viruses. Biennial cycles of RSV activity were identified in Northern Europe. Both viruses exhibited weak latitudinal gradients in the timing of epidemics by hemisphere, with peak timing occurring later in the calendar year with increasing latitude (P<0.03). Time series model applied to influenza data from 85 countries confirmed the presence of latitudinal gradients in timing, duration, seasonal amplitude, and between-year variability of epidemics. Overall, 80% of tropical locations experienced distinct RSV seasons lasting 6 months or less, while the percentage was 50% for influenza. CONCLUSION Our review combining literature and electronic data sources suggests that a large fraction of tropical locations experience focused seasons of respiratory virus activity in individual years. Information on seasonal patterns remains limited in large undersampled regions, included Africa and Central America. Future studies should attempt to link the observed latitudinal gradients in seasonality of viral epidemics with climatic and population factors, and explore regional differences in disease transmission dynamics and attack rates.
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Affiliation(s)
- Kimberly Bloom-Feshbach
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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Viboud C, Nelson MI, Tan Y, Holmes EC. Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120199. [PMID: 23382422 DOI: 10.1098/rstb.2012.0199] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.
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Affiliation(s)
- Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.
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Alonso WJ, McCormick BJJ. EPIPOI: a user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series. BMC Public Health 2012; 12:982. [PMID: 23153033 PMCID: PMC3527308 DOI: 10.1186/1471-2458-12-982] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Accepted: 10/09/2012] [Indexed: 11/19/2022] Open
Abstract
Background There is an increasing need for processing and understanding relevant information generated by the systematic collection of public health data over time. However, the analysis of those time series usually requires advanced modeling techniques, which are not necessarily mastered by staff, technicians and researchers working on public health and epidemiology. Here a user-friendly tool, EPIPOI, is presented that facilitates the exploration and extraction of parameters describing trends, seasonality and anomalies that characterize epidemiological processes. It also enables the inspection of those parameters across geographic regions. Although the visual exploration and extraction of relevant parameters from time series data is crucial in epidemiological research, until now it had been largely restricted to specialists. Methods EPIPOI is freely available software developed in Matlab (The Mathworks Inc) that runs both on PC and Mac computers. Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters. Results EPIPOI is able to handle complex analyses in an accessible way. A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research. Conclusions EPIPOI can assist public health officials and students to explore time series data using a broad range of sophisticated analytical and visualization tools. It also provides an analytical environment where even advanced users can benefit by enabling a higher degree of control over model assumptions, such as those associated with detecting disease outbreaks and pandemics.
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Affiliation(s)
- Wladimir J Alonso
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.
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Huppert A, Barnea O, Katriel G, Yaari R, Roll U, Stone L. Modeling and statistical analysis of the spatio-temporal patterns of seasonal influenza in Israel. PLoS One 2012; 7:e45107. [PMID: 23056192 PMCID: PMC3466289 DOI: 10.1371/journal.pone.0045107] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2012] [Accepted: 08/14/2012] [Indexed: 11/18/2022] Open
Abstract
Background Seasonal influenza outbreaks are a serious burden for public health worldwide and cause morbidity to millions of people each year. In the temperate zone influenza is predominantly seasonal, with epidemics occurring every winter, but the severity of the outbreaks vary substantially between years. In this study we used a highly detailed database, which gave us both temporal and spatial information of influenza dynamics in Israel in the years 1998–2009. We use a discrete-time stochastic epidemic SIR model to find estimates and credible confidence intervals of key epidemiological parameters. Findings Despite the biological complexity of the disease we found that a simple SIR-type model can be fitted successfully to the seasonal influenza data. This was true at both the national levels and at the scale of single cities.The effective reproductive number Re varies between the different years both nationally and among Israeli cities. However, we did not find differences in Re between different Israeli cities within a year. Re was positively correlated to the strength of the spatial synchronization in Israel. For those years in which the disease was more “infectious”, then outbreaks in different cities tended to occur with smaller time lags. Our spatial analysis demonstrates that both the timing and the strength of the outbreak within a year are highly synchronized between the Israeli cities. We extend the spatial analysis to demonstrate the existence of high synchrony between Israeli and French influenza outbreaks. Conclusions The data analysis combined with mathematical modeling provided a better understanding of the spatio-temporal and synchronization dynamics of influenza in Israel and between Israel and France. Altogether, we show that despite major differences in demography and weather conditions intra-annual influenza epidemics are tightly synchronized in both their timing and magnitude, while they may vary greatly between years. The predominance of a similar main strain of influenza, combined with population mixing serve to enhance local and global influenza synchronization within an influenza season.
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Affiliation(s)
- Amit Huppert
- The Gertner Institute, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Oren Barnea
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Guy Katriel
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Department of Mathematics, ORT Braude College, Karmiel, Israel
| | - Rami Yaari
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
- The Porter School of Environmental Studies, Tel-Aviv University, Tel-Aviv, Israel
| | - Uri Roll
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Lewi Stone
- Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
- * E-mail:
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Cheng X, Tan Y, He M, Lam TTY, Lu X, Viboud C, He J, Zhang S, Lu J, Wu C, Fang S, Wang X, Xie X, Ma H, Nelson MI, Kung HF, Holmes EC, Cheng J. Epidemiological dynamics and phylogeography of influenza virus in southern China. J Infect Dis 2012; 207:106-14. [PMID: 22930808 DOI: 10.1093/infdis/jis526] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Understanding the epidemiological dynamics of influenza virus is central to surveillance and vaccine strain selection. It has been suggested that tropical and subtropical regions represent the global source of influenza epidemics. However, our understanding of the epidemiological dynamics of influenza virus in these regions is limited by a relative lack of long-term data. METHODS We analyzed epidemiological and virological data on influenza recorded over a period of 15 years from the metropolitan city of Shenzhen in subtropical southern China. We used wavelet analysis to determine the periodicity of influenza epidemics and molecular phylogeographic analysis to investigate the role of Shenzhen and southern China in the global evolution of influenza virus. RESULTS We show that southern China is unlikely to represent an epicenter of global influenza activity, because activity in Shenzhen is characterized by significant annual cycles, multiple viral introductions every year, limited persistence across epidemic seasons, and viruses that generally are not positioned on the trunk of the global influenza virus phylogeny. CONCLUSIONS We propose that novel influenza viruses emerge and evolve in multiple geographic localities and that the global evolution of influenza virus is complex and does not simply originate from a southern Chinese epicenter.
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Affiliation(s)
- Xiaowen Cheng
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
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Stark JH, Cummings DAT, Ermentrout B, Ostroff S, Sharma R, Stebbins S, Burke DS, Wisniewski SR. Local variations in spatial synchrony of influenza epidemics. PLoS One 2012; 7:e43528. [PMID: 22916274 PMCID: PMC3420894 DOI: 10.1371/journal.pone.0043528] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Accepted: 07/23/2012] [Indexed: 11/18/2022] Open
Abstract
Background Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available. Methodology and Findings We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics. Conclusions These findings highlight the complex nature of influenza spread across multiple geographic scales.
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Affiliation(s)
- James H Stark
- Division of Epidemiology, New York City Department of Health and Mental Hygiene, New York, New York, USA.
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Gozalo PL, Pop-Vicas A, Feng Z, Gravenstein S, Mor V. Effect of influenza on functional decline. J Am Geriatr Soc 2012; 60:1260-7. [PMID: 22724499 DOI: 10.1111/j.1532-5415.2012.04048.x] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To examine the relationship between influenza and activity of daily living (ADL) decline and other clinical indicators in nursing home (NH) residents. DESIGN Retrospective NH-aggregated longitudinal study. SETTING Two thousand three hundred fifty-one NHs in 122 U.S. cities from 1999 to 2005. PARTICIPANTS Long-stay (>90 days) NH residents. MEASUREMENTS Quarterly city-level influenza mortality and state-level influenza severity. Quarterly incidence of Minimum Data Set-derived ADL decline (≥ 4 points), weight loss, new or worsening pressure ulcers (PUs), and infections. Outcome variables chosen as clinical controls were antipsychotic use, restraint use, and persistent pain. RESULTS City-level influenza mortality and state-level influenza severity were associated with higher rates of large (≥ 4 points) ADL decline (mortality β = 0.20, P < .001; severity β = 0.18, P < .001), weight loss (β = 0.19, P < .001; β = 0.24, P < .001), worsening PUs (β = 0.04, P = .08; β = 0.12, P < .001), and infections (β = 0.41, P < .001; β = 0.47, P < .001) but not with restraint use, antipsychotic use, or persistent pain. NH influenza vaccination rates were weakly associated with the outcomes (e.g., β = -0.009, P = .03 for ADL decline, β = 0.008, P = .07 for infections). Compared with the summer quarter of lowest influenza activity, the results for the other quarters translate to an additional 12,284 NH residents experiencing large ADL decline annually, 15,168 experiencing significant weight loss, 6,284 new or worsening PUs, and 29,753 experiencing infections due to influenza. CONCLUSION The results suggest a substantial and potentially costly effect of influenza on NH residents. The effect of influenza vaccination on preventing further ADL decline and other clinical outcomes in NH residents should be studied further.
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Affiliation(s)
- Pedro L Gozalo
- Department of Health Services, Policy and Practice, Brown University, Providence, Rhode Island 02912, USA.
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Stark JH, Sharma R, Ostroff S, Cummings DAT, Ermentrout B, Stebbins S, Burke DS, Wisniewski SR. Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003-2009. PLoS One 2012; 7:e34245. [PMID: 22470544 PMCID: PMC3314628 DOI: 10.1371/journal.pone.0034245] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 02/24/2012] [Indexed: 11/23/2022] Open
Abstract
Background Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system. Methodology and Findings We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.
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Affiliation(s)
- James H Stark
- New York City Department of Health and Mental Hygiene, New York, New York, United States of America.
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Influenza-related mortality in Spain, 1999-2005. GACETA SANITARIA 2012; 26:325-9. [PMID: 22284214 DOI: 10.1016/j.gaceta.2011.09.033] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2011] [Revised: 08/22/2011] [Accepted: 09/09/2011] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005. METHOD We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity. RESULTS Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively. CONCLUSIONS The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.
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Nielsen J, Mazick A, Glismann S, Mølbak K. Excess mortality related to seasonal influenza and extreme temperatures in Denmark, 1994-2010. BMC Infect Dis 2011; 11:350. [PMID: 22176601 PMCID: PMC3264536 DOI: 10.1186/1471-2334-11-350] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2011] [Accepted: 12/16/2011] [Indexed: 11/10/2022] Open
Abstract
Background In temperate zones, all-cause mortality exhibits a marked seasonality, and one of the main causes of winter excess mortality is influenza. There is a tradition of using statistical models based on mortality from respiratory illnesses (Pneumonia and Influenza: PI) or all-cause mortality for estimating the number of deaths related to influenza. Different authors have applied different estimation methodologies. We estimated mortality related to influenza and periods with extreme temperatures in Denmark over the seasons 1994/95 to 2009/10. Methods We applied a multivariable time-series model with all-cause mortality as outcome, activity of influenza-like illness (ILI) and excess temperatures as explanatory variables, controlling for trend, season, age, and gender. Two estimates of excess mortality related to influenza were obtained: (1) ILI-attributable mortality modelled directly on ILI-activity, and (2) influenza-associated mortality based on an influenza-index, designed to mimic the influenza transmission. Results The median ILI-attributable mortality per 100,000 population was 35 (range 6 to 100) per season which corresponds to findings from comparable countries. Overall, 88% of these deaths occurred among persons ≥ 65 years of age. The median influenza-associated mortality per 100,000 population was 26 (range 0 to 73), slightly higher than estimates based on pneumonia and influenza cause-specific mortality as estimated from other countries. Further, there was a tendency of declining mortality over the years. The influenza A(H3N2) seasons of 1995/96 and 1998/99 stood out with a high mortality, whereas the A(H3N2) 2005/6 season and the 2009 A(H1N1) influenza pandemic had none or only modest impact on mortality. Variations in mortality were also related to extreme temperatures: cold winters periods and hot summers periods were associated with excess mortality. Conclusion It is doable to model influenza-related mortality based on data on all-cause mortality and ILI, data that are easily obtainable in many countries and less subject to bias and subjective interpretation than cause-of-death data. Further work is needed to understand the variations in mortality observed across seasons and in particular the impact of vaccination against influenza.
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Affiliation(s)
- Jens Nielsen
- Statens Serum Institut, Department of Epidemiology, Artillerivej 5, DK2300 Copenhagen, Denmark.
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Yang L, Ma S, Chen PY, He JF, Chan KP, Chow A, Ou CQ, Deng AP, Hedley AJ, Wong CM, Peiris JM. Influenza associated mortality in the subtropics and tropics: results from three Asian cities. Vaccine 2011; 29:8909-14. [PMID: 21959328 PMCID: PMC7115499 DOI: 10.1016/j.vaccine.2011.09.071] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2011] [Revised: 08/12/2011] [Accepted: 09/16/2011] [Indexed: 11/19/2022]
Abstract
Influenza has been well documented to significantly contribute to winter increase of mortality in the temperate countries, but its severity in the subtropics and tropics was not recognized until recently and geographical variations of disease burden in these regions remain poorly understood. In this study, we applied a standardized modeling strategy to the mortality and virology data from three Asian cities: subtropical Guangzhou and Hong Kong, and tropical Singapore, to estimate the disease burden of influenza in these cities. We found that influenza was associated with 10.6, 13.4 and 8.3 deaths per 100,000 population in Guangzhou, Hong Kong and Singapore, respectively. The annual rates of excess deaths in the elders were estimated highest in Guangzhou and lowest in Singapore. The excess death rate attributable to A/H1N1 subtype was found slightly higher than the rates attributable to A/H3N2 during the study period of 2004-2006 based on the data from Hong Kong and Guangzhou. Our study revealed a geographical variation in the disease burden of influenza in these subtropical and tropical cities. These results highlight a need to explore the determinants for severity of seasonal influenza.
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Affiliation(s)
- Lin Yang
- Department of Community Medicine and School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Stefan Ma
- Epidemiology & Disease Control Division, Ministry of Health, Singapore
| | - Ping Yan Chen
- Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, China
| | - Jian Feng He
- Guangdong Provincial Center for Disease Control and Prevention, China
| | - King Pan Chan
- Department of Community Medicine and School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Angela Chow
- Communicable Disease Centre, Tan Tock Seng Hospital, Singapore
| | - Chun Quan Ou
- Department of Biostatistics, School of Public Health and Tropical Medicine, Southern Medical University, China
| | - Ai Ping Deng
- Guangdong Provincial Center for Disease Control and Prevention, China
| | - Anthony J. Hedley
- Department of Community Medicine and School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Chit Ming Wong
- Department of Community Medicine and School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - J.S. Malik Peiris
- Department of Microbiology, The University of Hong Kong, Hong Kong Special Administrative Region, China
- HKU Pasteur Research Center, Hong Kong Special Administrative Region, China
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