Seasonality of common respiratory viruses: Analysis of nationwide time-series data

被引:2
作者
An, Tai Joon [1 ]
Lee, Jangwon [2 ]
Shin, Myoungin [3 ]
Rhee, Chin Kook [4 ]
机构
[1] Catholic Univ Korea, Yeouido St Marys Hosp, Dept Internal Med, Div Pulm & Crit Care Med,Coll Med, Seoul, South Korea
[2] Korea Univ, Dept Stat, Seoul, South Korea
[3] Sejong Univ, Dept Ocean Syst Engn, Seoul, South Korea
[4] Catholic Univ Korea, Seoul St Marys Hosp, Dept Internal Med, Div Pulm & Crit Care Med,Coll Med, Seoul, South Korea
关键词
dynamic time warping; respiratory tract infection; seasonal autoregressive integrated moving average; time-series analysis; viruses; PARAINFLUENZA VIRUS; COVID-19;
D O I
10.1111/resp.14818
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
Background and ObjectiveUnderstanding the seasonal behaviours of respiratory viruses is crucial for preventing infections. We evaluated the seasonality of respiratory viruses using time-series analyses.MethodsThis study analysed prospectively collected nationwide surveillance data on eight respiratory viruses, gathered from the Korean Influenza and Respiratory Surveillance System. The data were collected on a weekly basis by 52 nationwide primary healthcare institutions between 2015 and 2019. We performed Spearman correlation analyses, similarity analyses via dynamic time warping (DTW) and seasonality analyses using seasonal autoregressive integrated moving average (SARIMA).ResultsThe prevalence of rhinovirus (RV, 23.6%-31.4%), adenovirus (AdV, 9.2%-16.6%), human coronavirus (HCoV, 3.0%-6.6%), respiratory syncytial virus (RSV, 11.7%-20.1%), influenza virus (IFV, 11.7%-21.5%), parainfluenza virus (PIV, 9.2%-12.6%), human metapneumovirus (HMPV, 5.6%-6.9%) and human bocavirus (HBoV, 5.0%-6.4%) were derived. Most of them exhibited a high positive correlation in Spearman analyses. In DTW analyses, all virus data from 2015 to 2019, except AdV, exhibited good alignments. In SARIMA, AdV and RV did not show seasonality. Other viruses showed 12-month seasonality. We describe the viruses as winter viruses (HCoV, RSV and IFV), spring/summer viruses (PIV, HBoV), a spring virus (HMPV) and all-year viruses with peak incidences during school periods (RV and AdV).ConclusionThis is the first study to comprehensively analyse the seasonal behaviours of the eight most common respiratory viruses using nationwide, prospectively collected, sentinel surveillance data. imageConclusionThis is the first study to comprehensively analyse the seasonal behaviours of the eight most common respiratory viruses using nationwide, prospectively collected, sentinel surveillance data. image We revealed the seasonal behaviour of respiratory viruses through time-series analysis using nationwide surveillance data from South Korea. Even before and after the COVID-19 pandemic, respiratory viruses were categorized by season: winter (coronavirus, respiratory syncytial virus and influenza virus), spring/summer (parainfluenza and bocavirus), spring (metapneumovirus) and all-year (rhinovirus and adenovirus).image
引用
收藏
页码:985 / 993
页数:9
相关论文
共 50 条
  • [31] NEW METHOD OF TIME-SERIES ANALYSIS AND ITS APPLICATION TO WOLFS SUNSPOT NUMBER DATA
    OHTOMO, N
    TERACHI, S
    TANAKA, Y
    TOKIWANO, K
    KANEKO, N
    JAPANESE JOURNAL OF APPLIED PHYSICS PART 1-REGULAR PAPERS SHORT NOTES & REVIEW PAPERS, 1994, 33 (5A): : 2821 - 2831
  • [32] Period estimation and using multivariable data analysis methods in Lake Balaton time-series
    Kovács J.
    Koroknai Zs.
    Futó I.
    Kovács-Székely I.
    Acta Geod. Geophys. Hung., 2006, 1 (45-54): : 45 - 54
  • [33] Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis
    Hyokyeong Lee
    Asher Moody-Davis
    Utsab Saha
    Brian M Suzuki
    Daniel Asarnow
    Steven Chen
    Michelle Arkin
    Conor R Caffrey
    Rahul Singh
    BMC Genomics, 13
  • [34] Applying trie-structure to improve dynamic time warping on time-series stock data analysis
    Chen, AP
    Chen, YC
    Yeh, CY
    PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, VOLS 1AND 2, 2004, : 31 - 36
  • [35] THE KALMAN FILTER MODEL AND BAYESIAN OUTLIER DETECTION FOR TIME-SERIES ANALYSIS OF BOD DATA
    TIWARI, RC
    DIENES, TP
    ECOLOGICAL MODELLING, 1994, 73 (1-2) : 159 - 165
  • [36] Temporal relationship between antibiotic use and respiratory virus activities in the Republic of Korea: a time-series analysis
    Sukhyun Ryu
    Sojung Kim
    Bryan I. Kim
    Eili Y. Klein
    Young Kyung Yoon
    Byung Chul Chun
    Antimicrobial Resistance & Infection Control, 7
  • [37] Temporal relationship between antibiotic use and respiratory virus activities in the Republic of Korea: a time-series analysis
    Ryu, Sukhyun
    Kim, Sojung
    Kim, Bryan I.
    Klein, Eili Y.
    Yoon, Young Kyung
    Chun, Byung Chul
    ANTIMICROBIAL RESISTANCE AND INFECTION CONTROL, 2018, 7
  • [38] Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data
    Shea, Daniel E.
    Giridharagopal, Rajiv
    Ginger, David S.
    Brunton, Steven L.
    Kutz, J. Nathan
    IEEE ACCESS, 2021, 9 : 83453 - 83466
  • [39] Dynamic selection of machine learning models for time-series data
    Hananya, Rotem
    Katz, Gilad
    INFORMATION SCIENCES, 2024, 665
  • [40] Autoregressive Models Applied to Time-Series Data in Veterinary Science
    Ward, Michael P.
    Iglesias, Rachel M.
    Brookes, Victoria J.
    FRONTIERS IN VETERINARY SCIENCE, 2020, 7