Prediction of high incidence of dengue in the Philippines.
BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippin...
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ftdoajarticles:oai:doaj.org/article:f2b31a868ac048dcaa0d9715d6162993 2023-05-15T15:16:31+02:00 Prediction of high incidence of dengue in the Philippines. Anna L Buczak Benjamin Baugher Steven M Babin Liane C Ramac-Thomas Erhan Guven Yevgeniy Elbert Phillip T Koshute John Mark S Velasco Vito G Roque Enrique A Tayag In-Kyu Yoon Sheri H Lewis 2014-04-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0002771 https://doaj.org/article/f2b31a868ac048dcaa0d9715d6162993 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC3983113?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002771 https://doaj.org/article/f2b31a868ac048dcaa0d9715d6162993 PLoS Neglected Tropical Diseases, Vol 8, Iss 4, p e2771 (2014) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2014 ftdoajarticles https://doi.org/10.1371/journal.pntd.0002771 2022-12-31T00:20:00Z BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 8 4 e2771 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Anna L Buczak Benjamin Baugher Steven M Babin Liane C Ramac-Thomas Erhan Guven Yevgeniy Elbert Phillip T Koshute John Mark S Velasco Vito G Roque Enrique A Tayag In-Kyu Yoon Sheri H Lewis Prediction of high incidence of dengue in the Philippines. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and ... |
format |
Article in Journal/Newspaper |
author |
Anna L Buczak Benjamin Baugher Steven M Babin Liane C Ramac-Thomas Erhan Guven Yevgeniy Elbert Phillip T Koshute John Mark S Velasco Vito G Roque Enrique A Tayag In-Kyu Yoon Sheri H Lewis |
author_facet |
Anna L Buczak Benjamin Baugher Steven M Babin Liane C Ramac-Thomas Erhan Guven Yevgeniy Elbert Phillip T Koshute John Mark S Velasco Vito G Roque Enrique A Tayag In-Kyu Yoon Sheri H Lewis |
author_sort |
Anna L Buczak |
title |
Prediction of high incidence of dengue in the Philippines. |
title_short |
Prediction of high incidence of dengue in the Philippines. |
title_full |
Prediction of high incidence of dengue in the Philippines. |
title_fullStr |
Prediction of high incidence of dengue in the Philippines. |
title_full_unstemmed |
Prediction of high incidence of dengue in the Philippines. |
title_sort |
prediction of high incidence of dengue in the philippines. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2014 |
url |
https://doi.org/10.1371/journal.pntd.0002771 https://doaj.org/article/f2b31a868ac048dcaa0d9715d6162993 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 8, Iss 4, p e2771 (2014) |
op_relation |
http://europepmc.org/articles/PMC3983113?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002771 https://doaj.org/article/f2b31a868ac048dcaa0d9715d6162993 |
op_doi |
https://doi.org/10.1371/journal.pntd.0002771 |
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PLoS Neglected Tropical Diseases |
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8 |
container_issue |
4 |
container_start_page |
e2771 |
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1766346809901318144 |