About: Prediction of High Incidence of Dengue in the Philippines

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Prediction of High Incidence of Dengue in the Philippines
Creator	Yoon, In-Kyu Baugher, Benjamin Elbert, Yevgeniy Guven, Erhan Babin, Steven Tayag, Enrique Mark, John Lewis, Sheri Velasco, S Buczak, Anna Koshute, Phillip Ramac-Thomas, Liane Roque, Vito
Source	Medline; PMC
abstract	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 F(0.5) 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 F(3) 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 Specificity.
has issue date	2014-04-10(xsd:dateTime)
bibo:doi	10.1371/journal.pntd.0002771
bibo:pmid	24722434
has license	cc0
sha1sum (hex)	e572a3a154399f2b500b0f547a7252c94580acf4
schema:url	https://doi.org/10.1371/journal.pntd.0002771
resource representing a document's title	Prediction of High Incidence of Dengue in the Philippines
has PubMed Central identifier	PMC3983113
has PubMed identifier	24722434
schema:publication	PLoS Negl Trop Dis
resource representing a document's body	covid:e572a3a154399f2b500b0f547a7252c94580acf4#body_text
is schema:about of	named entity 'THE PHILIPPINES' named entity 'dengue' named entity 'OUTBREAK' named entity 'HIGH' named entity 'PREDICTION' named entity 'INCIDENCE' named entity 'THE PHILIPPINES' covid:arg/e572a3a154399f2b500b0f547a7252c94580acf4 named entity 'HIGH' named entity 'ASSOCIATED WITH' named entity 'TIMELY' named entity 'ADVANCE' named entity 'DENGUE' named entity 'NEGLECTED' named entity 'DISEASE' named entity 'REDUCE' named entity 'LOW' named entity 'MORBIDITY AND MORTALITY' named entity 'LEVELS' named entity 'ACCURATE' named entity 'INCIDENCE' named entity 'WEEKS' named entity 'BACKGROUND' named entity 'MODELS' named entity 'ORDER' named entity 'DENGUE' named entity 'PREDICT' named entity 'TO PROVIDE' named entity 'PREDICTION' named entity 'Philippines' named entity 'weeks' named entity 'Philippines' named entity 'Philippines' named entity 'Philippines' named entity 'high' named entity 'decision support systems' named entity 'French Polynesia' named entity 'false negatives' named entity 'dengue' named entity 'Singapore' named entity 'systematic literature review' named entity 'disease prevention' named entity 'human health' named entity 'Dengue' named entity 'Structured Query Language' named entity 'Sensitivity, and Specificity' named entity 'dengue' named entity 'Philippines' named entity 'public health departments' named entity 'CDC' named entity 'disease prevention' named entity 'disease outbreaks' named entity 'Philippines' named entity 'predictive modeling' named entity 'dengue' named entity 'sanitation' named entity 'data mining' named entity 'Moderate Resolution Imaging Spectroradiometer' named entity 'dengue' named entity 'mosquitoes' named entity 'fuzzy set theory' named entity 'dengue' named entity 'dengue' named entity 'Joint Typhoon Warning Center' named entity 'socio-economic' named entity 'membership functions' named entity 'flower' named entity 'canopy structure' named entity 'serotypes'

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