Identification

Author

Buczak AL, Koshute PT, Babin SM, Feighner BH, Lewis SH

Title

A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data

Year

2012

Publication type

Article

Journal

BMC Medical Informatics and Decision Making

Created

2014-01-27 20:42:58+00:00

Modified

2016-06-27 20:40:21.980639+00:00

Details

Volume

12

Number

124

Access

Language

English

URL http://www.biomedcentral.com/1472-6947/12/124/
DOI

10.1186/1472-6947-12-124

Accessed

2016-06-27

Extended information

Abstract

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.