Climate Variability and Nonstationary Dynamics of Mycoplasma pneumoniae Pneumonia in Japan
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{"title"=>"Climate variability and nonstationary dynamics of mycoplasma pneumoniae pneumonia in Japan", "type"=>"journal", "authors"=>[{"first_name"=>"Daisuke", "last_name"=>"Onozuka", "scopus_author_id"=>"16239563400"}, {"first_name"=>"Luis Fernando", "last_name"=>"Chaves", "scopus_author_id"=>"12789694000"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "sgr"=>"84899695243", "doi"=>"10.1371/journal.pone.0095447", "pui"=>"372990497", "scopus"=>"2-s2.0-84899695243", "issn"=>"19326203", "pmid"=>"24740102"}, "id"=>"16ea8c2d-25f2-3320-a858-0cb6efbb4873", "abstract"=>"BACKGROUND: A stationary association between climate factors and epidemics of Mycoplasma pneumoniae (M. pneumoniae) pneumonia has been widely assumed. However, it is unclear whether elements of the local climate that are relevant to M. pneumoniae pneumonia transmission have stationary signatures of climate factors on their dynamics over different time scales.\\n\\nMETHODS: We performed a cross-wavelet coherency analysis to assess the patterns of association between monthly M. pneumoniae cases in Fukuoka, Japan, from 2000 to 2012 and indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO).\\n\\nRESULTS: Monthly M. pneumoniae cases were strongly associated with the dynamics of both the IOD and ENSO for the 1-2-year periodic mode in 2005-2007 and 2010-2011. This association was non-stationary and appeared to have a major influence on the synchrony of M. pneumoniae epidemics.\\n\\nCONCLUSIONS: Our results call for the consideration of non-stationary, possibly non-linear, patterns of association between M. pneumoniae cases and climatic factors in early warning systems.", "link"=>"http://www.mendeley.com/research/climate-variability-nonstationary-dynamics-mycoplasma-pneumoniae-pneumonia-japan", "reader_count"=>14, "reader_count_by_academic_status"=>{"Researcher"=>1, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Researcher"=>1, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Environmental Science"=>4, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>3, "Social Sciences"=>2, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>2}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>4}}, "reader_count_by_country"=>{"Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1466808"], "description"=>"<p>(A) DMI (dipole mode index); (B) MEI (multivariate ENSO index); (C) Niño 12 (ENSO index); (D) Niño 3 (ENSO index); (E) Niño 4 (ENSO index); (F) Niño 34 (ENSO index). Red regions in the upper part of the plots indicate frequencies and times for which the two series share variability. The cone of influence (within which results are not influenced by the edges of the data) and the significant coherent time-frequency regions (p<0.05) are indicated by solid lines. In cross-wavelet phase plots, colors correspond to different lags between the variability in the two series for a given time and frequency, measured in angles from -PI to PI. A value of PI corresponds to a lag of 16 months.</p>", "links"=>[], "tags"=>["Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "oceanography", "Infectious diseases", "Bacterial diseases", "Bacterial pneumonia", "Mycoplasma pneumonia", "Viral diseases", "Viral pneumonia", "coherency", "pneumonia", "climatic"], "article_id"=>1001254, "categories"=>["Biological Sciences"], "users"=>["Daisuke Onozuka", "Luis Fernando Chaves"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095447.g003", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_wavelet_coherency_and_phase_of_the_M_pneumoniae_pneumonia_time_series_with_the_global_climatic_indices_/1001254", "title"=>"Cross-wavelet coherency and phase of the <i>M. pneumoniae</i> pneumonia time series with the global climatic indices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 03:12:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466805"], "description"=>"<p>(A) <i>M. pneumoniae</i> pneumonia cases; (B) temperature; (C) relative humidity; and (D) rainfall.</p>", "links"=>[], "tags"=>["Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "oceanography", "Infectious diseases", "Bacterial diseases", "Bacterial pneumonia", "Mycoplasma pneumonia", "Viral diseases", "Viral pneumonia"], "article_id"=>1001251, "categories"=>["Biological Sciences"], "users"=>["Daisuke Onozuka", "Luis Fernando Chaves"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095447.g001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Monthly_time_series_data_in_Fukuoka_Japan_2000_8211_2012_/1001251", "title"=>"Monthly time series data in Fukuoka, Japan, 2000–2012.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 03:12:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466806"], "description"=>"<p>(A) DMI (dipole mode index); (B) MEI (multivariate ENSO index); (C) Niño 12 (ENSO index); (D) Niño 3 (ENSO index); (E) Niño 4 (ENSO index); (F) Niño 34 (ENSO index).</p>", "links"=>[], "tags"=>["Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "oceanography", "Infectious diseases", "Bacterial diseases", "Bacterial pneumonia", "Mycoplasma pneumonia", "Viral diseases", "Viral pneumonia", "climatic", "indices"], "article_id"=>1001252, "categories"=>["Biological Sciences"], "users"=>["Daisuke Onozuka", "Luis Fernando Chaves"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095447.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Monthly_time_series_data_for_the_global_climatic_indices_2000_to_2012_/1001252", "title"=>"Monthly time series data for the global climatic indices (2000 to 2012).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 03:12:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466812"], "description"=>"<p>(A) Temperature; (B) relative humidity; and (C) rainfall. Red regions in the upper part of the plots indicate frequencies and times for which the two series share variability. The cone of influence (within which results are not influenced by the edges of the data) and the significant coherent time-frequency regions (p<0.05) are indicated by solid lines. In cross-wavelet phase plots, colors correspond to different lags between variability in the two series for a given time and frequency, measured in angles from -PI to PI. A value of PI corresponds to a lag of 16 months.</p>", "links"=>[], "tags"=>["Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "oceanography", "Infectious diseases", "Bacterial diseases", "Bacterial pneumonia", "Mycoplasma pneumonia", "Viral diseases", "Viral pneumonia", "coherency", "pneumonia"], "article_id"=>1001258, "categories"=>["Biological Sciences"], "users"=>["Daisuke Onozuka", "Luis Fernando Chaves"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095447.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_wavelet_coherency_and_phase_of_the_M_pneumoniae_pneumonia_time_series_with_local_weather_indices_/1001258", "title"=>"Cross-wavelet coherency and phase of the <i>M. pneumoniae</i> pneumonia time series with local weather indices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 03:12:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466817", "https://ndownloader.figshare.com/files/1466818"], "description"=>"<div><p>Background</p><p>A stationary association between climate factors and epidemics of <i>Mycoplasma pneumoniae (M. pneumoniae)</i> pneumonia has been widely assumed. However, it is unclear whether elements of the local climate that are relevant to <i>M. pneumoniae</i> pneumonia transmission have stationary signatures of climate factors on their dynamics over different time scales.</p><p>Methods</p><p>We performed a cross-wavelet coherency analysis to assess the patterns of association between monthly <i>M. pneumoniae</i> cases in Fukuoka, Japan, from 2000 to 2012 and indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO).</p><p>Results</p><p>Monthly <i>M. pneumoniae</i> cases were strongly associated with the dynamics of both the IOD and ENSO for the 1–2-year periodic mode in 2005–2007 and 2010–2011. This association was non-stationary and appeared to have a major influence on the synchrony of <i>M. pneumoniae</i> epidemics.</p><p>Conclusions</p><p>Our results call for the consideration of non-stationary, possibly non-linear, patterns of association between <i>M. pneumoniae</i> cases and climatic factors in early warning systems.</p></div>", "links"=>[], "tags"=>["Atmospheric science", "Climatology", "climate change", "marine and aquatic sciences", "oceanography", "Infectious diseases", "Bacterial diseases", "Bacterial pneumonia", "Mycoplasma pneumonia", "Viral diseases", "Viral pneumonia", "variability", "nonstationary", "pneumonia", "japan"], "article_id"=>1001263, "categories"=>["Biological Sciences"], "users"=>["Daisuke Onozuka", "Luis Fernando Chaves"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0095447.s001", "https://dx.doi.org/10.1371/journal.pone.0095447.s002"], "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Climate_Variability_and_Nonstationary_Dynamics_of_Mycoplasma_pneumoniae_Pneumonia_in_Japan/1001263", "title"=>"Climate Variability and Nonstationary Dynamics of <i>Mycoplasma pneumoniae</i> Pneumonia in Japan", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-04-16 03:12:17"}

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