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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2012

Open Access 01-12-2012 | Research article

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

Authors: Anna L Buczak, Phillip T Koshute, Steven M Babin, Brian H Feighner, Sheryl H Lewis

Published in: BMC Medical Informatics and Decision Making | Issue 1/2012

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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.
Appendix
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Literature
1.
Focks DA, Daniels E, Haile DG, Keesling JE: A simulation model of the epidemiology of urban dengue fever: literature analysis, model development, preliminary validation, and samples of simulation results. Am J Trop Med Hyg. 1995, 53 (5): 489-506.PubMed
2.
3.
4.
Ranjit S, Kissoon N: Dengue hemorrhagic fever and shock syndromes. Pediatr Crit Care Med. 2010, 12 (1): 90-100.CrossRef
5.
Guzman MG, Kouri G: Dengue: an update. Lancet Infect Dis. 2002, 2: 33-42. 10.1016/S1473-3099(01)00171-2.CrossRefPubMed
6.
Halsted SB: Dengue. Lancet Infect Dis. 2007, 370: 1644-1652.
7.
US Centers for Disease Control and Prevention: Locally Acquired Dengue – Key West, Florida 2009–2010. MMWR. 2010, 59 (19): 577-581.
8.
Control of Communicable Diseases Manual. Edited by: Heymann DL. 2008, Washington DC: American Public Health Association, 19
9.
Tan GK, Alonso S: Pathogenesis and prevention of dengue virus infection: state of the art. Curr Opin Infect Dis. 2009, 22 (3): 302-308. 10.1097/QCO.0b013e328329ae32.CrossRefPubMed
10.
Fuller DO, Troyo A, Beier JC: El Nino Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa Rica. Environ Res Lett. 2009, 4: 014011-10.1088/1748-9326/4/1/014011.CrossRef
11.
Choudhury MAHZ, Banu S, Islam MA: Forecasting dengue incidence in Dhaka, Bangladesh: a time series analysis. DF Bulletin. 2008, 32: 29-37.
12.
Wongkoon S, Jaroensutasinee M, Jaroensutasinee K: Predicting DHF incidence in northern Thailand using time series analysis technique. World Academy of Science, Engineering and Technology. 2007, 32: 216-220.
13.
Cummings DAT, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K, Burke DS: Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature. 2004, 427: 344-347. 10.1038/nature02225.CrossRefPubMed
14.
Bhandari KP, Raju PLN, Sokhi BS: Application of GIS modeling for dengue fever prone area based on socio-cultural and environmental factors – a case study of Delhi city zone. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B8. 2008, 165-170. Beijing, China
15.
Husin NA, Salim N, Ahmad AR: Modeling of dengue outbreak prediction in Malaysia: a comparison of neural network and nonlinear regression model. IEEE International Symposium on Information Technology (ITSim 2008). 2008, 3: 1-4. Kuala Lumpur, MalaysiaCrossRef
16.
Cetiner BG, Sari M, Aburas HM: Recognition of dengue disease patterns using artificial neural networks. 5th International Advanced Technologies Symposium (IATS’09). 2009, Karabuk, 359-362.
17.
Rachata N, Charoenkwan P, Yooyativong T, Camnongthai K, Lursinsap C, Higachi K: Automatic prediction system of dengue haemorrhagic-fever outbreak risk by using entropy and artificial neural network. IEEE International Symposium on Communications and Information Technologies (ISCIT 2008). 2008, 210-214. 21–23 October 2008, Lao
18.
Fu X, Liew C, Soh H, Lee G, Hung T, Ng L-C: Time-series infectious disease data analysis using SVM and genetic algorithm. 2007 IEEE Congress on Evolutionary Computation (CEC 2007). 2007, 1276-1280. Singapore, 25–28 September 2007
19.
Halide H, Ridd P: A predictive model for dengue hemorrhagic fever epidemics. International J Environ Health Research. 2008, 4: 253-265.CrossRef
20.
Honório NA, Nogueira RMR, Codeco CT, Carvalho MS, Cruz OG, Magalhaes MAFM, de Araujo JMG, de Araujo ESM, Gomes MQ, Pinheiro LS, Pinel CS, de Oliveira L: Spatial evaluation and modeling of dengue seroprevalence and vector density in Rio de Janeiro, Brazil. PLoS Negl Trop Dis. 2009, 3: e545-CrossRefPubMedPubMedCentral
21.
Barbazan P, Guiserix M, Boonyuan W, Tuntaprasart W, Pontier D, Gonzalez J-P: Modelling the effect of temperature on transmission of dengue. Medical and Veterinary Entomology. 2010, 24: 66-73. 10.1111/j.1365-2915.2009.00848.x.CrossRefPubMed
22.
Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Ocean. Technol. 1998, 15: 809-817. 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.CrossRef
23.
Soebiyanto RP, Adimi F, Kiang RK: Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One. 2010, 5: e9450-10.1371/journal.pone.0009450.CrossRefPubMedPubMedCentral
24.
Anyamba A, Linthicum KJ, Mahoney R, Tucker CJ, Kelley PW: Mapping potential risk of Rift Valley Fever outbreaks in African savannas using vegetation index time series data. Photogramm. Engr. Remote Sens. 2002, 68: 137-145.
25.
Hales S, Weinstein P, Souares Y, Woodward A: El Nino and the dynamics of vectorborne disease transmission. Environ Health Perspect. 1999, 107: 99-102.PubMedPubMedCentral
26.
Johansson MA, Cummings DAT, Glass GE: Multiyear climate variability and dengue – El Nino Southern Oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med. 2009, 6: e1000168-10.1371/journal.pmed.1000168.CrossRefPubMedPubMedCentral
27.
Hu W, Clements A, Williams G, Tong S: Dengue fever and El Nino/Southern Oscillation in Queensland, Australia: a time series predictive model. Occup Environ Med. 2010, 67: 307-311. 10.1136/oem.2008.044966.CrossRefPubMed
28.
29.
US National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center: Mirador Earth Science Data Search Tool. 2012, Available:http://​mirador.​gsfc.​nasa.​gov/​ Accessed 26 October 2012,
30.
31.
32.
Kalluri S, Gilruth P, Rogers D, Szczur M: Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog. 2007, 3 (10): e116-10.1371/journal.ppat.0030116.CrossRefPubMedCentral
33.
Ferreira NC, Ferreira LG, Huete AR: Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon. International J Remote Sensing. 31 (3): 745-759.
34.
Xiao X, Zhang Q, Saleska S, Hutyra L, De Camargo P, Wofsy S, Frolking S, Boles S, Keller M, Moore B: Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sensing Environ. 2005, 94: 105-122. 10.1016/j.rse.2004.08.015.CrossRef
35.
36.
37.
38.
39.
40.
Buczak AL, Gifford CM: Fuzzy association rule mining for community crime pattern discovery. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Workshop on Intelligence and Security Informatics. 2010, Washington, D.C: Association for Computing Machinery, July 2010
41.
Kuok CM, Fu A, Wong MH: Mining fuzzy association rules in databases. ACM SIGMOD Record. 1998, 27 (1): 41-46. 10.1145/273244.273257. New York, NYCrossRef
42.
Zadeh LA: Fuzzy Sets. Information and Control. 1965, 8 (3): 338-353. 10.1016/S0019-9958(65)90241-X.CrossRef
43.
Liu B, Hsu W, Ma Y: Integrating classification and association rule mining. Proceedings of 4th International Conference on Knowledge Discovery Data Mining (KDD). 1998, New York: AAAI Press, 80-86.
44.
Agrawal R, Imielinski T, Swami A: Mining association rules between sets of items in large databases. Proceedings of the International Conference on Management of Data. 1993, Washington, DC: Association for Computing Machinery, 207-216.
45.
Srikant R, Agrawal R: Mining quantitative association rules in large relational tables. Proceedings of the International Conference on Management of Data. 1996, Montreal: Association for Computing Machinery, 1-12.
46.
Cawley GC, Talbot NLC: On over-fitting in model selection and subsequent selection bias in performance evaluation. J Machine Learning Res. 2010, 11: 2079-2107.
Metadata
Title
A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data
Authors
Anna L Buczak
Phillip T Koshute
Steven M Babin
Brian H Feighner
Sheryl H Lewis
Publication date
01-12-2012
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2012
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-12-124

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