Current Science

Open Access Open Access  Restricted Access Subscription Access

Data Mining Techniques for Predicting Dengue Outbreak in Geospatial Domain Using Weather Parameters for New Delhi, India


Affiliations
1 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
 

Dengue is a hazardous disease which poses a critical threat to the population of Delhi, India. These cases are steadily reported during and post-monsoon season indicating its correlation with weather parameters. Establishing this relation will help understand the spread of dengue and will allow decision makers take precautionary steps beforehand. Our study explains the adopted multi-regression and Naïve Bayes approach to model the relation between dengue cases and weather parameters, i.e. maximum temperature, rainfall and relative humidity. Both these models have served a great deal in modelling this relationship which has enabled us to forecast a probable dengue outbreak. Our results have shown that sudden and high rainfall accompanied with 30–35°C temperature and high relative humidity contributes to a highly vulnerable weather for the spread of dengue. Also, we have proposed a new application of spherical k-means clustering algorithm to identify zones with similar transmission pattern which gives insight into the distribution of dengue incidences in Delhi. Results show that Central, Civil Lines, Rohini, South and West zones have the highest odds of dengue occurrences.

Keywords

Dengue, Multi-Regression, Naive Bayes, Spherical K-Means, Weather Parameters.
User
Notifications
Font Size

  • Bhatt, S. et al., The global distribution and burden of dengue. Nature, 2013, 496, 504–507.

  • WHO, Dengue: Guidelines for diagnosis, treatment, prevention and control, World Health Organization, 2009.

  • Powell, J. R. and Tabachnick, W. J., History of domestication and spread of Aedes aegypti – a review. Mem. Inst. Oswaldo Cruz, 2013, 108, 11–17.

  • Gubler, D. J., Dengue and dengue hemorrhagic fever. Clin. Microbiol. Rev., 1998, 11, 480–496.

  • Gupta, N., Srivastava, S., Jain, A. and Chaturvedi, U. C., Dengue in India. Indian J. Med. Res., 2012, 136, 373–390.

  • Shepard, D. S. et al., Economic and disease burden of dengue illness in india. Am. J. Trop. Med. Hyg., 2014, 91, 1235–1242.

  • Rao, T. R., Distribution, density and seasonal prevalence of Aedes aegypti in the indian subcontinent and south-east asia. Bull. World Health Organ., 1967, 36, 547.

  • Nimmannitya, S., Gubler, D., Biswas, A., Devgan, V., Gupta, B. and Sharma, S., Guidelines for clinical management of dengue fever, dengue haemorrhagic fever and dengue shock syndrome. Programme, D.o.N.V.B.D.C.).

  • Cecilia, D., Current status of dengue and chikungunya in India. WHO South-East Asia J. Public Health, 2014, 3, 22–27.

  • Vikram, K. et al., An epidemiological study of dengue in Delhi, India. Acta Trop., 2015, 153, 21–27.

  • Kukreti, H. et al., Emergence of an independent lineage of dengue virus type 1 (denv-1) and its co-circulation with predominant denv-3 during the 2006 dengue fever outbreak in Delhi. Int. J. Infect. Dis., 2008, 12, 542–549.

  • Chan, M. and Johansson, M. A., The incubation periods of dengue viruses. PLoS ONE, 2012, 7, e50972.

  • Carrington, L. B., Armijos, M. V., Lambrechts, L. and Scott, T. W., Fluctuations at a low mean temperature accelerate dengue virus transmission by Aedes aegypti, 2013.

  • Lambrechts, L., Paaijmans, K. P., Fansiri, T., Carrington, L.B., Kramer, L. D., Thomas, M. B. and Scott, T. W., Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc. Natl. Acad. Sci. USA, 2011, 108(18), 7460–7465.

  • Brady, O. J. et al, Global temperature constraints on Aedes aegypti and ae. Albopictus persistence and competence for dengue virus transmission. Parasit. Vectors, 2014, 7, 1–4.

  • Nakhapakorn, K. and Tripathi, N. K., An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. Int. J. Health Geogr., 2005, 4, 13.

  • Hii, Y. L., Rocklov, J., Ng, N., Tang, C. S., Pang, F. Y. and Sauerborn, R., Climate variability and increase in intensity and magnitude of dengue incidence in singapore. Glob. Health Action, 2009, 2(1), 2036.

  • Promprou, S., Jaroensutasinee, M. and Jaroensutasinee, K., Climatic factors affecting dengue haemorrhagic fever incidence in southern Thailand. Dengue Bull., 2005, 29, 41.

  • Karim, M., Munshi, S. U., Anwar, N. and Alam, M., Climatic factors influencing dengue cases in Dhaka city: a model for dengue prediction. Indian J. Med. Res., 2012, 136, 32.

  • Fathima, A. S., Manimegalai, D. and Hundewale, N., A review of data mining classification techniques applied for diagnosis and prognosis of the arbovirus-dengue. Int. J. Comput. Sci., 2011, 8(6).

  • Bakar, A. A., Kefli, Z., Abdullah, S. and Sahani, M., Predictive models for dengue outbreak using multiple rulebase classifiers. In Electrical Engineering and Informatics (ICEEI), 2011 International Conference on IEEE, pp. 1–6.

  • Shaukat, K., Masood, N., Mehreen, S. and Azmeen, U., Dengue fever prediction: A data mining problem. J. Data Min. Genom. Proteomics, 2015, 6(181), 2153–0602.

  • Shakil, K. A., Anis, S. and Alam, M., Dengue disease prediction using weka data mining tool. arXiv preprint arXiv:150205167, 2015.

  • Prakash, M. and Kumar, S. C., Hotspot analysis of dengue fever cases in delhi using geospatial techniques. In 2014 Esri India User Conference, New Delhi, India.

  • Lewis, D. D., Naive (bayes) at forty: The independence assumption in information retrieval. In Machine Learning: Ecml-98, Springer, pp. 4–15.

  • Kumar, S. A. and Vijayalakshmi, M., Inference of naive baye’s technique on student assessment data. In Global Trends in Information Systems and Software Applications, Springer, pp. 186–191.

  • Hornik, K., Feinerer, I., Kober, M. and Buchta, C., Spherical k-means clustering. J. Stat. Softw., 2012, 50, 1–22.

  • Brady, O. J. et al., Modelling adult aedes aegypti and aedes albopictus survival at different temperatures in laboratory and field settings. Parasit. Vectors, 2013, 6, 1–12.

  • Hales, S., De Wet, N., Maindonald, J. and Woodward, A., Potential effect of population and climate changes on global distribution of dengue fever: An empirical model. Lancet, 2002, 360, 830–834.


Abstract Views: 388

PDF Views: 118




  • Data Mining Techniques for Predicting Dengue Outbreak in Geospatial Domain Using Weather Parameters for New Delhi, India

Abstract Views: 388  |  PDF Views: 118

Authors

Nikita Agarwal
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Shiva Reddy Koti
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Sameer Saran
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
A. Senthil Kumar
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India

Abstract


Dengue is a hazardous disease which poses a critical threat to the population of Delhi, India. These cases are steadily reported during and post-monsoon season indicating its correlation with weather parameters. Establishing this relation will help understand the spread of dengue and will allow decision makers take precautionary steps beforehand. Our study explains the adopted multi-regression and Naïve Bayes approach to model the relation between dengue cases and weather parameters, i.e. maximum temperature, rainfall and relative humidity. Both these models have served a great deal in modelling this relationship which has enabled us to forecast a probable dengue outbreak. Our results have shown that sudden and high rainfall accompanied with 30–35°C temperature and high relative humidity contributes to a highly vulnerable weather for the spread of dengue. Also, we have proposed a new application of spherical k-means clustering algorithm to identify zones with similar transmission pattern which gives insight into the distribution of dengue incidences in Delhi. Results show that Central, Civil Lines, Rohini, South and West zones have the highest odds of dengue occurrences.

Keywords


Dengue, Multi-Regression, Naive Bayes, Spherical K-Means, Weather Parameters.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi11%2F2281-2291