Prediction of Dengue Incidence Using Search Query Surveillance
Publication Date
August 02, 2011
Journal
PLOS Neglected Tropical Diseases
Authors
Benjamin M. Althouse, Yih Yng Ng & Derek A. T. Cummings
Volume
5
Issue
8
Pages
e1258
DOI
http://doi.org/10.1371/journal.pntd.0001258
Publisher URL
http://journals.plos.org/plosntds/article?id=10.1371%2Fjournal.pntd.0001258
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21829744
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149016
Europe PMC
http://europepmc.org/abstract/MED/21829744
Web of Science
000294479800016
Scopus
80052395153
Mendeley
http://www.mendeley.com/research/prediction-dengue-incidence-using-search-query-surveillance
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Mendeley | Further Information

{"title"=>"Prediction of dengue incidence using search query surveillance", "type"=>"journal", "authors"=>[{"first_name"=>"Benjamin M.", "last_name"=>"Althouse", "scopus_author_id"=>"54900566600"}, {"first_name"=>"Yih Yng", "last_name"=>"Ng", "scopus_author_id"=>"55700981700"}, {"first_name"=>"Derek A.T.", "last_name"=>"Cummings", "scopus_author_id"=>"9842706300"}], "year"=>2011, "source"=>"PLoS Neglected Tropical Diseases", "identifiers"=>{"pui"=>"362489713", "sgr"=>"80052395153", "pmid"=>"21829744", "scopus"=>"2-s2.0-80052395153", "isbn"=>"1935-2727", "doi"=>"10.1371/journal.pntd.0001258", "issn"=>"19352727"}, "id"=>"6ff5dd05-a048-3804-b0e3-b315f218cc6a", "abstract"=>"BACKGROUND: The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.\\n\\nMETHODS: We gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004-2011) and Bangkok (monthly incidence, 2004-2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using r² and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.\\n\\nRESULTS: The linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an r² = 0.943, and a correlation of 0.869 between fitted and observed. In Singapore, the model has an r² = 0.948, and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.\\n\\nCONCLUSIONS: Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.", "link"=>"http://www.mendeley.com/research/prediction-dengue-incidence-using-search-query-surveillance", "reader_count"=>130, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>34, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>6, "Student > Master"=>23, "Other"=>5, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>34, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>6, "Student > Master"=>23, "Other"=>5, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Agricultural and Biological Sciences"=>32, "Veterinary Science and Veterinary Medicine"=>2, "Chemistry"=>1, "Computer Science"=>21, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>2, "Engineering"=>7, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>3, "Medicine and Dentistry"=>36, "Social Sciences"=>10, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>36}, "Social Sciences"=>{"Social Sciences"=>10}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>3}, "Engineering"=>{"Engineering"=>7}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>21}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "United States"=>5, "Brazil"=>2, "United Kingdom"=>2, "France"=>2, "Germany"=>1, "Indonesia"=>1, "French Polynesia"=>1, "India"=>1}, "group_count"=>18}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/749872"], "description"=>"<p>Table reports the <i>r</i><sup>2</sup>, the correlation between observed dengue incidence and model fitted values, and the Akaike information criterion (AIC) for the full and step-down models in both Singapore and Bangkok. Table also reports correlation between observed 2010 dengue incidence and out-of-sample 2010 predictions from the step-down models for Singapore and Bangkok for unlagged observed incidence and observed incidence lagged by 4 weeks.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "mathematics"], "article_id"=>420244, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258.t001", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Incidence_prediction_model_diagnostics_/420244", "title"=>"Incidence prediction model diagnostics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-08-02 00:04:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/749901"], "description"=>"<p>Table reports the AUC and optimal sensitivities and specificities for the LOO predictions for the SVM models at three threshold levels: the 50th, 75th, and 90th percentiles of dengue cases from 2005-2011 for Singapore and Bangkok, respectively.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "mathematics"], "article_id"=>420277, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258.t002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Threshold_prediction_model_diagnostics_/420277", "title"=>"Threshold prediction model diagnostics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-08-02 00:04:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/377593"], "description"=>"<div><h3>Background</h3><p>The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.</p> <h3>Methods</h3><p>We gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004–2011) and Bangkok (monthly incidence, 2004–2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.</p> <h3>Results</h3><p>The linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an , and a correlation of 0.869 between fitted and observed. In Singapore, the model has an , and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.</p> <h3>Conclusions</h3><p>Internet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.</p> </div>", "links"=>[], "tags"=>["dengue", "incidence", "query"], "article_id"=>134614, "categories"=>["Mathematics", "Cancer", "Biotechnology"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258", "stats"=>{"downloads"=>8, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Prediction_of_Dengue_Incidence_Using_Search_Query_Surveillance/134614", "title"=>"Prediction of Dengue Incidence Using Search Query Surveillance", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-08-02 01:16:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/749610"], "description"=>"<p>Figure displays observed dengue case incidence for years 2005–2011 (black lines) as well as the model fitted to data from 2005–2010 (solid red lines) and 2010 prediction with 95% prediction intervals (dashed red lines and solid red band) for both Singapore (panel A) and Bangkok data (panel C). Panels B and D show the error between the observed incidence and model fit or prediction.</p>", "links"=>[], "tags"=>["observed", "dengue", "incidence"], "article_id"=>419988, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_observed_dengue_incidence_and_model_fit_/419988", "title"=>"Correlation between observed dengue incidence and model fit.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-02 02:46:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/749716"], "description"=>"<p>The performance of the SVM model in Singapore. Red circles indicate a prediction of high incidence at the optimal probability found from the ROC curve at right. Black stars indicate observed high incidence not predicted by the model. Panel A and the corresponding ROC curve at right indicate the median cutoff, panel B the 75th percentile cutoff and panel C the 90th percentile cutoff.</p>", "links"=>[], "tags"=>["svm"], "article_id"=>420094, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_SVM_prediction_in_Singapore_/420094", "title"=>"Summary of SVM prediction in Singapore.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-02 00:01:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/749512"], "description"=>"<p>Figure shows the search terms used in the full models for Singapore and Bangkok (top boxes), as well as the results of the AIC step-down procedure (bottom boxes).</p>", "links"=>[], "tags"=>["step-down"], "article_id"=>419892, "categories"=>["Biotechnology", "Mathematics", "Infectious Diseases"], "users"=>["Benjamin M. Althouse", "Yih Yng Ng", "Derek A. T. Cummings"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0001258.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_step_down_search_term_selection_/419892", "title"=>"Schematic of step-down search term selection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-02 02:44:52"}

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Relative Metric

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