A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data
| Autor: | Anna L. Buczak, Phillip Koshute, Sheryl Happel Lewis, Brian H. Feighner, Steven M. Babin |
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| Jazyk: | angličtina |
| Předmět: |
Association rule learning
Computer science Population Health Informatics lcsh:Computer applications to medicine. Medical informatics computer.software_genre Fuzzy logic Dengue fever Disease Outbreaks Dengue Predictor variables Statistics Peru medicine Humans Generalizability theory education Association rule mining education.field_of_study Health Policy Temperature Outbreak medicine.disease Computer Science Applications Socioeconomic Factors Epidemiological Monitoring Remote Sensing Technology lcsh:R858-859.7 Data mining Seasons Prediction computer Test data Forecasting Research Article |
| Zdroj: | BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, Vol 12, Iss 1, p 124 (2012) |
| ISSN: | 1472-6947 |
| DOI: | 10.1186/1472-6947-12-124 |
| Popis: | Background Dengue is the most common arboviral disease of humans, with more than one third of the world’s population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy. Methods We describe a novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru. These relationships are in the form of rules. The best set of rules is automatically chosen and forms a classifier. That classifier is then used to predict future dengue incidence as either HIGH (outbreak) or LOW (no outbreak), where these values are defined as being above and below the mean previous dengue incidence plus two standard deviations, respectively. Results Our automated method built three different fuzzy association rule models. Using the first two weekly models, we predicted dengue incidence three and four weeks in advance, respectively. The third prediction encompassed a four-week period, specifically four to seven weeks from time of prediction. Using previously unused test data for the period 4–7 weeks from time of prediction yielded a positive predictive value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a specificity of 0.982. Conclusions We have developed a novel approach for dengue outbreak prediction. The method is general, could be extended for use in any geographical region, and has the potential to be extended to other environmentally influenced infections. The variables used in our method are widely available for most, if not all countries, enhancing the generalizability of our method. |
| Databáze: | OpenAIRE |
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