Rotavirus Seasonality and Age Effects in a Birth Cohort Study of Southern India
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{"title"=>"Rotavirus Seasonality and Age Effects in a Birth Cohort Study of Southern India", "type"=>"journal", "authors"=>[{"first_name"=>"Rajiv", "last_name"=>"Sarkar", "scopus_author_id"=>"15849568600"}, {"first_name"=>"Gagandeep", "last_name"=>"Kang", "scopus_author_id"=>"7202457874"}, {"first_name"=>"Elena N.", "last_name"=>"Naumova", "scopus_author_id"=>"7005017327"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84881570257", "sgr"=>"84881570257", "pui"=>"369579346", "pmid"=>"23977089", "doi"=>"10.1371/journal.pone.0071616"}, "id"=>"89c8bc7a-3b16-33b4-bd84-50ca821eed3e", "abstract"=>"INTRODUCTION Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. Cohort studies present a unique opportunity to explore complex interactions associated with emergence of seasonal patterns of infectious diseases. METHODS We used data from 452 children participating in a birth cohort study to assess the seasonal patterns of rotavirus diarrhea by creating a weekly time series of rotavirus incidence and fitting a Poisson harmonic regression with biannual peaks. Age and cohort effects were adjusted for by including the weekly counts of number of children in the study and the median age of cohort in a given week. Weekly average temperature, humidity and an interaction term to reflect the joint effect of temperature and humidity were included to consider the effects of meteorological variables. RESULTS In the overall rotavirus time series, two significant peaks within a single year were observed--one in winter and the other in summer. The effect of age was found to be the most significant contributor for rotavirus incidence, showing a strong negative association. Seasonality remained a significant factor, even after adjusting for meteorological parameters, and the age and cohort effects. CONCLUSIONS The methodology for assessing seasonality in cohort studies is not yet developed. This is the first attempt to explore seasonal patterns in a cohort study with a dynamic denominator and rapidly changing immune response on individual and group levels, and provides a highly promising approach for a better understanding of the seasonal patterns of infectious diseases, tracking emergence of pathogenic strains and evaluating the efficacy of intervention programs.", "link"=>"http://www.mendeley.com/research/rotavirus-seasonality-age-effects-birth-cohort-study-southern-india", "reader_count"=>5, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>2, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1173413"], "description"=>"<p>Needle plot showing the weekly distribution of counts of rotavirus diarrhea during the study period.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "counts", "rotavirus", "diarrhea"], "article_id"=>775214, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Needle_plot_showing_the_weekly_distribution_of_counts_of_rotavirus_diarrhea_during_the_study_period_/775214", "title"=>"Needle plot showing the weekly distribution of counts of rotavirus diarrhea during the study period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173414"], "description"=>"<p>The black line represents the total number of child-weeks of follow-up for each week in the time series. The red line depicts the median age of children (in weeks) corresponding to a particular week in the time-series.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "follow-up", "ageing"], "article_id"=>775215, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cumulative_weekly_enrollment_follow_up_and_ageing_of_the_cohort_/775215", "title"=>"Cumulative weekly enrollment, follow-up and ageing of the cohort.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173415"], "description"=>"<p>The horizontal spikes depict the observed incidence of rotavirus diarrhea among children in the cohort. The blue vertical line depicts the predicted incidence derived from the Poisson harmonic regression model.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "incidence", "rotavirus", "diarrhea", "1000"], "article_id"=>775216, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Week_series_of_incidence_of_rotavirus_diarrhea_per_1000_child_weeks_/775216", "title"=>"Week-series of incidence of rotavirus diarrhea (per 1000 child-weeks).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173417"], "description"=>"<p>Observed changes in a relative contribution of rotavirus genotypes indicate temporal clustering characteristic for seasonality in genotype dominance.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "rotavirus"], "article_id"=>775218, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Weekly_distribution_of_different_rotavirus_genotypes_/775218", "title"=>"Weekly distribution of different rotavirus genotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173419"], "description"=>"<p>The colored horizontal spikes (khaki for G1P <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071616#pone.0071616-Buttery1\" target=\"_blank\">[8]</a>, orange for G2P <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071616#pone.0071616-Naumova1\" target=\"_blank\">[4]</a> and green for G1P <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071616#pone.0071616-Naumova1\" target=\"_blank\">[4]</a>) represent the observed strain-specific incidence of rotavirus diarrhea among children in the cohort. The vertical blue line depicts the predicted incidence derived from the Poisson harmonic regression model.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "incidence", "diarrhea", "1000", "rotavirus", "strains", "g1p", "g2p"], "article_id"=>775220, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Week_series_of_incidence_of_diarrhea_per_1000_child_weeks_due_to_major_rotavirus_strains_A_G1P_8_B_G2P_4_C_G1P_4_/775220", "title"=>"Week-series of incidence of diarrhea (per 1000 child-weeks) due to major rotavirus strains (A) G1P [8], (B) G2P [4], (C) G1P [4].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173421"], "description"=>"<p>The horizontal spikes depict the observed monthly values, the red vertical lines represent the smoothed estimates, and the blue vertical lines reflect the predicted counts based on Poisson harmonic regression model with four sine-cosine terms.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "rotavirus", "diarrhea", "bangladesh", "pronounced", "biannual"], "article_id"=>775222, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Seasonality_of_rotavirus_diarrhea_in_Dhaka_Bangladesh_2001_8211_2006_with_pronounced_biannual_peaks_/775222", "title"=>"Seasonality of rotavirus diarrhea in Dhaka, Bangladesh (2001–2006) with pronounced biannual peaks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173422"], "description"=>"<p>Results of the Poisson harmonic regression models for assessment of rotavirus seasonality, and age and cohort effects.</p>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "poisson", "harmonic", "regression", "rotavirus", "cohort"], "article_id"=>775223, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_the_Poisson_harmonic_regression_models_for_assessment_of_rotavirus_seasonality_and_age_and_cohort_effects_/775223", "title"=>"Results of the Poisson harmonic regression models for assessment of rotavirus seasonality, and age and cohort effects.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-16 02:54:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1173424", "https://ndownloader.figshare.com/files/1173425"], "description"=>"<div><p>Introduction</p><p>Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. Cohort studies present a unique opportunity to explore complex interactions associated with emergence of seasonal patterns of infectious diseases.</p><p>Methods</p><p>We used data from 452 children participating in a birth cohort study to assess the seasonal patterns of rotavirus diarrhea by creating a weekly time series of rotavirus incidence and fitting a Poisson harmonic regression with biannual peaks. Age and cohort effects were adjusted for by including the weekly counts of number of children in the study and the median age of cohort in a given week. Weekly average temperature, humidity and an interaction term to reflect the joint effect of temperature and humidity were included to consider the effects of meteorological variables.</p><p>Results</p><p>In the overall rotavirus time series, two significant peaks within a single year were observed – one in winter and the other in summer. The effect of age was found to be the most significant contributor for rotavirus incidence, showing a strong negative association. Seasonality remained a significant factor, even after adjusting for meteorological parameters, and the age and cohort effects.</p><p>Conclusions</p><p>The methodology for assessing seasonality in cohort studies is not yet developed. This is the first attempt to explore seasonal patterns in a cohort study with a dynamic denominator and rapidly changing immune response on individual and group levels, and provides a highly promising approach for a better understanding of the seasonal patterns of infectious diseases, tracking emergence of pathogenic strains and evaluating the efficacy of intervention programs.</p></div>", "links"=>[], "tags"=>["Population biology", "epidemiology", "Infectious disease epidemiology", "Clinical research design", "Longitudinal studies", "Statistical methods", "Pediatric epidemiology", "Infectious diseases", "Viral diseases", "Rotavirus infection", "Gastrointestinal infections", "Public health", "Child health", "Environmental health", "seasonality", "cohort"], "article_id"=>775225, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Rajiv Sarkar", "Gagandeep Kang", "Elena N. Naumova"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071616.s001", "https://dx.doi.org/10.1371/journal.pone.0071616.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rotavirus_Seasonality_and_Age_Effects_in_a_Birth_Cohort_Study_of_Southern_India_/775225", "title"=>"Rotavirus Seasonality and Age Effects in a Birth Cohort Study of Southern India", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-08-16 02:54:43"}

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  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Earth sciences", "average_usage"=>[296, 488, 620, 717, 828, 938, 1038, 1130, 1230, 1328, 1414, 1502, 1592]}, {"subject_area"=>"/Earth sciences/Atmospheric science", "average_usage"=>[287, 443, 560, 684, 777, 894, 989, 1065, 1143, 1235, 1335, 1423, 1477]}, {"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/Pathology and laboratory medicine", "average_usage"=>[267, 466, 592, 709, 806, 901, 989, 1075, 1162, 1254, 1342, 1424, 1486]}, {"subject_area"=>"/People and places/Geographical locations", "average_usage"=>[263, 456, 571, 673, 776, 866, 955, 1040, 1132, 1217, 1310, 1391, 1454]}, {"subject_area"=>"/People and places/Population groupings", "average_usage"=>[265, 456, 578, 682, 782, 881, 965, 1053, 1133, 1223, 1310, 1391, 1457]}]}
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