Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data
Publication Date
November 19, 2013
Journal
PLOS Medicine
Authors
Hongjie Yu, Wladimir J. Alonso, Luzhao Feng, Yi Tan, et al
Volume
10
Issue
11
Pages
e1001552
DOI
https://dx.plos.org/10.1371/journal.pmed.1001552
Publisher URL
http://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1001552
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24348203
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3864611
Europe PMC
http://europepmc.org/abstract/MED/24348203
Web of Science
000330371600009
Scopus
84888999503
Mendeley
http://www.mendeley.com/research/avian-influenza-preparing-pandemic
Events
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Mendeley | Further Information

{"title"=>"Avian influenza: Preparing for a pandemic", "type"=>"generic", "authors"=>[{"first_name"=>"Gregory", "last_name"=>"Juckett", "scopus_author_id"=>"6602361964"}], "year"=>2006, "source"=>"American Family Physician", "identifiers"=>{"scopus"=>"2-s2.0-33748577651", "issn"=>"0002838X", "pui"=>"44371624", "sgr"=>"33748577651", "isbn"=>"0002-838X", "pmid"=>"17089907", "doi"=>"10.1371/journal.pmed.1001552"}, "id"=>"c0e04bf1-864b-3a38-9f82-1d0c8c567e89", "abstract"=>"Since 1996 to 2006 there have been regular outbreaks of influenza. The genetics of virus plays an important role in its virulence mechanism. In this era of the impending threat on the influenza pandemic it is imperative that there be exchange of knowledge regarding the virus and its virulence. This article tries to address the above motive. An exhaustive review of literature has been provided regarding this viral threat.", "link"=>"http://www.mendeley.com/research/avian-influenza-preparing-pandemic", "reader_count"=>47, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Librarian"=>1, "Student > Doctoral Student"=>3, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Librarian"=>1, "Student > Doctoral Student"=>3, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Medicine and Dentistry"=>13, "Agricultural and Biological Sciences"=>14, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>1, "Psychology"=>4, "Social Sciences"=>4, "Computer Science"=>6, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Social Sciences"=>{"Social Sciences"=>4}, "Psychology"=>{"Psychology"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Colombia"=>1, "Denmark"=>1, "United Kingdom"=>2, "Italy"=>1}, "group_count"=>4}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1287617"], "description"=>"a<p>Number of cities and hospitals participating in surveillance.</p>b<p>Indicates year-round influenza surveillance before 2009 (all provinces switched to year-round surveillance in the post-2009 pandemic period).</p><p>C, cold temperate; GRP, gross regional product; MT, mid-temperate; ST, subtropical; T, tropical; WT, warm temperate;</p>", "links"=>[], "tags"=>["30", "provinces", "influenza", "sampling"], "article_id"=>856128, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.t001", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Background_characteristics_of_the_30_provinces_involved_in_influenza_surveillance_and_information_on_influenza_sampling_intensity_2005_8211_2011_China_/856128", "title"=>"Background characteristics of the 30 provinces involved in influenza surveillance and information on influenza sampling intensity, 2005–2011, China.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287615"], "description"=>"<p>measured by the proportion of influenza B among all influenza positive isolates each season. Median proportion of influenza B over seven seasons is displayed for each province, with grey horizontal bars indicating ±2 standard deviations. Symbol size is proportional to the average number of influenza virus isolates sampled each season. Colors represent different climatic zones (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). Purple lines represent linear regression of influenza B proportion against latitude (dashed line, unweighted regression; solid line, regression weighted by sample size).</p>", "links"=>[], "tags"=>["gradient", "dominance", "influenza"], "article_id"=>856126, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g005", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Latitudinal_gradient_in_the_dominance_of_influenza_B_in_China_/856126", "title"=>"Latitudinal gradient in the dominance of influenza B in China,", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287612"], "description"=>"<p>(Left) Timing of annual influenza peaks, in weeks. Timing is color coded by season. (Center) Amplitude of annual periodicity, ranging from low (yellow) to high (red), as indicated in the legend. Amplitude is relative to the mean of the weekly influenza time series in each province. (Right) Importance of semi-annual periodicities, measured by the ratio of the amplitude of the semi-annual periodicity to the sum of the amplitudes of annual and semi-annual periodicities. Yellow indicates strongly annual influenza epidemics, while red indicates marked semi-annual activity. See also <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001552#pmed-1001552-g004\" target=\"_blank\">Figure 4</a>.</p>", "links"=>[], "tags"=>["periodicity", "influenza", "epidemics"], "article_id"=>856123, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g003", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_periodicity_and_timing_of_influenza_A_top_panels_and_B_bottom_panels_epidemics_in_China_/856123", "title"=>"Estimates of periodicity and timing of influenza A (top panels) and B (bottom panels) epidemics in China.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287611"], "description"=>"<p>(A) Time series of weekly standardized influenza cases, sorted by increasing latitude from bottom to top. Dashed vertical lines represent the influenza A/H1N1pdm pandemic period, Apr 2009–Apr 2010. (B) Average seasonal distribution of influenza cases (excluding the pandemic period), plotted as the proportion of viruses isolated in each week of the year. Provinces conducting year-round surveillance are denoted by an asterix. Week 0 is the first week of October of each year.</p>", "links"=>[], "tags"=>["influenza", "epidemiological", "chinese", "oct", "2005-dec"], "article_id"=>856122, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmaps_of_influenza_epidemiological_data_by_Chinese_province_Oct_2005_Dec_2011_/856122", "title"=>"Heatmaps of influenza epidemiological data by Chinese province, Oct 2005-Dec 2011.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287625", "https://ndownloader.figshare.com/files/1287626", "https://ndownloader.figshare.com/files/1287627", "https://ndownloader.figshare.com/files/1287628", "https://ndownloader.figshare.com/files/1287629", "https://ndownloader.figshare.com/files/1287630", "https://ndownloader.figshare.com/files/1287631", "https://ndownloader.figshare.com/files/1287632", "https://ndownloader.figshare.com/files/1287633"], "description"=>"<div><p>Background</p><p>The complexity of influenza seasonal patterns in the inter-tropical zone impedes the establishment of effective routine immunization programs. China is a climatologically and economically diverse country, which has yet to establish a national influenza vaccination program. Here we characterize the diversity of influenza seasonality in China and make recommendations to guide future vaccination programs.</p><p>Methods and Findings</p><p>We compiled weekly reports of laboratory-confirmed influenza A and B infections from sentinel hospitals in cities representing 30 Chinese provinces, 2005–2011, and data on population demographics, mobility patterns, socio-economic, and climate factors. We applied linear regression models with harmonic terms to estimate influenza seasonal characteristics, including the amplitude of annual and semi-annual periodicities, their ratio, and peak timing. Hierarchical Bayesian modeling and hierarchical clustering were used to identify predictors of influenza seasonal characteristics and define epidemiologically-relevant regions. The annual periodicity of influenza A epidemics increased with latitude (mean amplitude of annual cycle standardized by mean incidence, 140% [95% CI 128%–151%] in the north versus 37% [95% CI 27%–47%] in the south, <i>p</i><0.0001). Epidemics peaked in January–February in Northern China (latitude ≥33°N) and April–June in southernmost regions (latitude <27°N). Provinces at intermediate latitudes experienced dominant semi-annual influenza A periodicity with peaks in January–February and June–August (periodicity ratio >0.6 in provinces located within 27.4°N–31.3°N, slope of latitudinal gradient with latitude −0.016 [95% CI −0.025 to −0.008], <i>p</i><0.001). In contrast, influenza B activity predominated in colder months throughout most of China. Climate factors were the strongest predictors of influenza seasonality, including minimum temperature, hours of sunshine, and maximum rainfall. Our main study limitations include a short surveillance period and sparse influenza sampling in some of the southern provinces.</p><p>Conclusions</p><p>Regional-specific influenza vaccination strategies would be optimal in China; in particular, annual campaigns should be initiated 4–6 months apart in Northern and Southern China. Influenza surveillance should be strengthened in mid-latitude provinces, given the complexity of seasonal patterns in this region. More broadly, our findings are consistent with the role of climatic factors on influenza transmission dynamics.</p><p><i>Please see later in the article for the Editors' Summary</i></p></div>", "links"=>[], "tags"=>["influenza", "seasonality", "patterns", "china", "implications", "vaccination", "spatio-temporal", "modeling"], "article_id"=>856136, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001552.s001", "https://dx.doi.org/10.1371/journal.pmed.1001552.s002", "https://dx.doi.org/10.1371/journal.pmed.1001552.s003", "https://dx.doi.org/10.1371/journal.pmed.1001552.s004", "https://dx.doi.org/10.1371/journal.pmed.1001552.s005", "https://dx.doi.org/10.1371/journal.pmed.1001552.s006", "https://dx.doi.org/10.1371/journal.pmed.1001552.s007", "https://dx.doi.org/10.1371/journal.pmed.1001552.s008", "https://dx.doi.org/10.1371/journal.pmed.1001552.s009"], "stats"=>{"downloads"=>8, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_Regional_Influenza_Seasonality_Patterns_in_China_and_Implications_for_Vaccination_Strategies_Spatio_Temporal_Modeling_of_Surveillance_Data_/856136", "title"=>"Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287613"], "description"=>"<p>Plots represent estimates of seasonal parameters as a function of latitude for influenza A (left panels) and influenza B (right panels). Top panels: Amplitude of annual periodicity, relative to the mean standardized influenza time series. Middle panels: Peak timing in weeks. Bottom panels: Contribution of the semi-annual cycle, measured by the ratio of the amplitude of the semi-annual cycle to the sum of the amplitudes of annual and semi-annual cycles. Open circles represent point estimates from seasonal regression models and horizontal dashed lines represent 95% confidence intervals based on 1,000 block-bootstrap samples. Symbol size is proportional to the average number of influenza virus isolates sampled each season, while colors represent different climatic zones (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). Purple lines represent linear regression of seasonal parameters against latitude (dashed line, unweighted regression; solid line, regression weighted by the inverse of the variance of province-specific seasonal estimates); R<sup>2</sup> and <i>p</i>-values are indicated on the graphs.</p>", "links"=>[], "tags"=>["gradients", "seasonality", "influenza", "epidemics"], "article_id"=>856124, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Latitudinal_gradients_in_seasonality_of_influenza_A_and_B_epidemics_in_China_/856124", "title"=>"Latitudinal gradients in seasonality of influenza A and B epidemics in China.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287610"], "description"=>"<p>Dots indicate the location of the capital city in each province. A total of 193 hospitals participate in disease surveillance, representing 88 cities. Colors illustrate different climatic domains (black, cold-temperate; blue, mid-temperate; green, warm-temperate; orange, subtropical; red, tropical). Different symbols indicate the type of surveillance scheme (circles, year-round surveillance; triangles, Oct through Mar surveillance).</p>", "links"=>[], "tags"=>["chinese", "provinces", "conducting", "influenza"], "article_id"=>856121, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_of_Chinese_provinces_conducting_influenza_surveillance_n_30_/856121", "title"=>"Map of Chinese provinces conducting influenza surveillance (<i>n</i> = 30).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287618"], "description"=>"<p>Putative predictors include climate, geographic, population, and sampling factors. Seasonal characteristics were based on linear seasonal regression models fitted to province-specific weekly laboratory surveillance data (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001552#pmed-1001552-g004\" target=\"_blank\">Figure 4</a>). Point estimates and standard errors provided in the table are based on hierarchical Bayesian regression of seasonal characteristics against province-specific covariates, with observation variances estimated by bootstrap resampling of seasonal parameters.</p>a<p>Amplitude of semi-annual cycle divided by the sum of amplitudes of semi-annual and annual cycles.</p>b<p>5% threshold criterion.</p>c<p>No covariate remained in final model.</p>d<p>Annual number of passengers leaving or entering the province, divided by province-specific population size.</p>", "links"=>[], "tags"=>["influenza", "seasonal", "30", "chinese"], "article_id"=>856129, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictors_of_influenza_seasonal_characteristics_by_virus_type_influenza_A_B_and_total_in_30_Chinese_provinces_2005_8211_2011_/856129", "title"=>"Predictors of influenza seasonal characteristics by virus type (influenza A, B, and total) in 30 Chinese provinces, 2005–2011.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-19 03:18:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1287616"], "description"=>"<p>(A) Definition of three influenza epidemiological regions based on hierarchical clustering (colored rectangles; Ward's method, Euclidian distance between weekly standardized influenza time series). Province labels are color-coded by climatic region (black, cold-temperate; blue, mid-temperate; green, warm temperate; orange, subtropical; red, tropical). (B) Map of the three epidemiological regions identified in (A). (C) Climate predictors of the two main regions identified in (A) (subtropical regions 1 and 2 versus temperate region 3), based on stepwise discriminant analysis. (D) Climate predictors of subtropical regions 1 and 2, based on stepwise discriminant analysis.</p>", "links"=>[], "tags"=>["epidemiological", "regions"], "article_id"=>856127, "categories"=>["Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Hongjie Yu", "Wladimir J. Alonso", "Luzhao Feng", "Yi Tan", "Yuelong Shu", "Weizhong Yang", "Cécile Viboud"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001552.g006", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Influenza_epidemiological_regions_and_climate_predictors_/856127", "title"=>"Influenza epidemiological regions and climate predictors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-19 03:18:04"}

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  • {"unique-ip"=>"21", "full-text"=>"22", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"30", "full-text"=>"34", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"29", "full-text"=>"27", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"13", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}, {"subject_area"=>"/Medicine and health sciences/Epidemiology", "average_usage"=>[263, 452, 568, 671, 758, 847, 921, 1023, 1101, 1187, 1264, 1340, 1394]}, {"subject_area"=>"/Medicine and health sciences/Immunology", "average_usage"=>[262, 463, 587, 693, 790, 882, 965, 1051, 1135, 1218, 1300, 1376, 1438]}, {"subject_area"=>"/Medicine and health sciences/Infectious diseases", "average_usage"=>[297, 523, 655, 765, 866, 971, 1070, 1159, 1256, 1337, 1424, 1496, 1568]}, {"subject_area"=>"/Medicine and health sciences/Public and occupational health", "average_usage"=>[291, 491, 610, 723, 817, 906, 991, 1071, 1153, 1238, 1324, 1411, 1478]}, {"subject_area"=>"/People and places", "average_usage"=>[270, 456, 573, 676, 773, 867, 955, 1037, 1120, 1205, 1286, 1363, 1436]}, {"subject_area"=>"/People and places/Geographical locations", "average_usage"=>[263, 456, 571, 673, 776, 866, 955, 1040, 1132, 1217, 1310, 1391, 1454]}]}
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