Skip to main content
Book cover

Colombian Conference on Computing

CCC 2017: Advances in Computing pp 472–484Cite as

Kernel-Based Machine Learning Models for the Prediction of Dengue and Chikungunya Morbidity in Colombia

Part of the Communications in Computer and Information Science book series (CCIS,volume 735)

Abstract

Dengue and Chikungunya fever are two viral diseases of great public health concern in Colombia and other tropical countries as they are both transmitted by Aedes mosquitoes, which are endemic to this area. In recent years, there have been unprecedented outbreaks of these infections. Therefore, the development of computational models to forecast the number of cases based on available epidemiological data would benefit public surveillance health systems to take effective actions regarding the prevention and mitigation of these events. In this work, we present the application of machine learning algorithms to predict the morbidity dynamics of dengue and chikungunya in Colombia using time-series-forecasting methods. Available weekly incidence for dengue (2007–2016) and chikungunya (2014–2016) from the National Health Institute of Colombia was gathered and employed as input to generate and validate the models. Kernel Ridge Regression and Gaussian Processes were used at forecasting the number of cases of both diseases considering horizons of one and four weeks. In order to assess the performance of the algorithms, rolling-origin cross-validation was carried out, and the mean absolute percentage errors (MAPE), mean absolute errors (MAE), R2 and the percentages of explained variance calculated for each model. Kernel Ridge regression with one-step ahead horizon was found to be superior to other models in forecasting both dengue and chikungunya number of cases per week. However, the power of prediction for dengue incidence was higher as there is more epidemiological data available for this disease compared to chikungunya. The results are promising and urge further research and development to achieve a tool which could be used by public health officials to manage more adequately the epidemiological dynamics of these diseases.

Keywords

  • Machine learning
  • Forecasting
  • Dengue
  • Chikungunya
  • Kernel Ridge regression
  • Gaussian Processes

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-66562-7_34
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-66562-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

References

  1. Morbidity prediction github repository (2017). https://github.com/williamcaicedo/morbidityPrediction. Accessed 25 Mar 2017

  2. Althouse, B.M., Ng, Y.Y., Cummings, D.A.T.: Prediction of dengue incidence using search query surveillance. PLoS Negl. Trop. Dis. 5(8), 1–7 (2011). http://dx.doi.org/10.1371

    CrossRef  Google Scholar 

  3. Caicedo-Torres, W., Payares, F.: A machine learning model for occupancy rates and demand forecasting in the hospitality industry. In: Montes-y-Gómez, M., Escalante, H.J., Segura, A., Murillo, J.D. (eds.) IBERAMIA 2016. LNCS, vol. 10022, pp. 201–211. Springer, Cham (2016). doi:10.1007/978-3-319-47955-2_17

    CrossRef  Google Scholar 

  4. Cawley, G.C., Talbot, N.L.C.: Reduced rank kernel ridge regression. Neural Process. Lett. 16(3), 293–302 (2002). http://dx.doi.org/10.1023/A: 1021798002258

    CrossRef  MATH  Google Scholar 

  5. Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. J. Mach. Learn. Res. 6, 1019–1041 (2005)

    MathSciNet  MATH  Google Scholar 

  6. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). http://dx.doi.org/10.1007/BF00994018

    MATH  Google Scholar 

  7. Cruz, J.A., Wishart, D.S.: Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2, 59–77 (2006). https://era.library.ualberta.ca/files/1v53jx76c/Cancer_Informatics_2_2007_59.pdf

    Google Scholar 

  8. Eastin, M.D., Delmelle, E., Casas, I., Wexler, J., Self, C.: Intra-and interseasonal autoregressive prediction of dengue outbreaks using local weather and regional climate for a tropical environment in colombia. Am. J. Trop. Med. Hyg. 91(3), 598–610 (2014)

    CrossRef  Google Scholar 

  9. Escobar, L.E., Qiao, H., Peterson, A.T.: Forecasting chikungunya spread in the Americas via data-driven empirical approaches. Parasites Vectors 9(1), 112 (2016). http://dx.doi.org/10.1186/s13071-016-1403-y

    CrossRef  Google Scholar 

  10. Flasche, S., Jit, M., Rodríguez-Barraquer, I., Coudeville, L., Recker, M., Koelle, K., Milne, G., Hladish, T.J., Perkins, T.A., Cummings, D.A., et al.: The long-term safety, public health impact, and cost-effectiveness of routine vaccination with a recombinant, live-attenuated dengue vaccine (dengvaxia): a model comparison study. PLoS Med. 13(11), e1002181 (2016)

    CrossRef  Google Scholar 

  11. Gilliland, M., Sglavo, U., Tashman, L.: Business Forecasting: Practical Problems and Solutions. Wiley, Hoboken (2016). http://dx.doi.org/10.1002/9781119244592

    Google Scholar 

  12. Golding, N., Wilson, A.L., Moyes, C.L., Cano, J., Pigott, D.M., Velayudhan, R., Brooker, S.J., Smith, D.L., Hay, S.I., Lindsay, S.W.: Integrating vector control across diseases. BMC Med. 13(1), 249 (2015). http://dx.doi.org/10.1186/s12916-015-0491-4

    CrossRef  Google Scholar 

  13. Hesterberg, T., Choi, N.H., Meier, L., Fraley, C., et al.: Least angle and 1 penalized regression: a review. Stat. Surv. 2, 61–93 (2008)

    CrossRef  MathSciNet  MATH  Google Scholar 

  14. Hoerl, A.E., Kennard, R.W.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 42(1), 80–86 (2000). http://amstat.tandfonline.com/doi/abs/10.1080/00401706.2000.10485983

    CrossRef  MATH  Google Scholar 

  15. Kucharz, E.J., Cebula-Byrska, I.: Chikungunya fever. Eur. J. Intern. Med. 23(4), 325–329 (2012). http://www.sciencedirect.com/science/article/pii/S0953620512000337

    Google Scholar 

  16. Mair, C., Kadoda, G., Lefley, M., Phalp, K., Schofield, C., Shepperd, M., Webster, S.: An investigation of machine learning based prediction systems. J. Syst. Softw. 53(1), 23–29 (2000). http://www.sciencedirect.com/science/article/pii/S0164121200000054

    CrossRef  Google Scholar 

  17. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  18. Rasmussen, C.E.: Gaussian Processes in Machine Learning, pp. 63–71. Springer, Heidelberg (2004). http://dx.doi.org/10.1007/978-3-540-28650-9_4

  19. Rasmussen, C.E., Williams, C.K.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006). 2(3), 4

    MATH  Google Scholar 

  20. Robert, C.: Machine learning, a probabilistic perspective. CHANCE 27(2), 62–63 (2014). http://dx.doi.org/10.1080/09332480.2014.914768

    CrossRef  Google Scholar 

  21. Rodríguez, J., Correa, C.: Predicción temporal de la epidemia de dengue en colombia: dinámica probabilista de la epidemia. Revista de Salud Pública 11(3), 443–453 (2009). http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0124-00642009000300013&nrm=iso

  22. Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2002)

    Google Scholar 

  23. Silawan, T., Singhasivanon, P., Kaewkungwal, J., Nimmanitya, S., Suwonkerd, W.: Temporal patterns and forecast of dengue infection in Northeastern Thailand. SE Asian J. Trop. Med. Public Health 39(1), 90 (2008)

    Google Scholar 

  24. Simmons, C.P., Farrar, J.J., van Vinh Chau, N., Wills, B.: Dengue. N. Engl. J. Med. 366(15), 1423–1432 (2012). pMID: 22494122. http://dx.doi.org/10.1056/NEJMra1110265

  25. Smalley, C., Erasmus, J.H., Chesson, C.B., Beasley, D.W.: Status of research and development of vaccines for chikungunya. Vaccine 34(26), 2976–2981 (2016)

    CrossRef  Google Scholar 

  26. Solomon, T., Mallewa, M.: Dengue and other emerging flaviviruses. J. Infect. 42(2), 104–115 (2001). http://www.sciencedirect.com/science/article/pii/S0163445301908023

    CrossRef  Google Scholar 

  27. Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3(1), 9–44 (1988). http://dx.doi.org/10.1007/BF00115009

    Google Scholar 

  28. Vannice, K.S., Durbin, A., Hombach, J.: Status of vaccine research and development of vaccines for dengue. Vaccine 34(26), 2934–2938 (2016)

    CrossRef  Google Scholar 

  29. Walker, T., Jeffries, C.L., Mansfield, K.L., Johnson, N.: Mosquito cell lines: history, isolation, availability and application to assess the threat of arboviral transmission in the united kingdom. Parasites Vectors 7(1), 382 (2014). http://dx.doi.org/10.1186/1756-3305-7-382

    CrossRef  Google Scholar 

  30. Williams, C.K., Rasmussen, C.E.: Gaussian processes for regression. In: Advances in Neural Information Processing Systems, pp. 514–520 (1996)

    Google Scholar 

  31. World Health Organization: Dengue guidelines for diagnosis, treatment, prevention and control: new edition (2009). http://www.who.int/tdr/publications/documents/dengue-diagnosis.pdf?ua=1

  32. World Health Organization: World health organization - dengue and severe dengue (2009). http://www.who.int/mediacentre/factsheets/fs117/en/. Accessed 25 March 2017

  33. World Health Organization: World health organization - chikungunya (2017). http://www.who.int/mediacentre/factsheets/fs327/en/. Accessed 25 March 2017

  34. Yusof, Y., Mustaffa, Z.: Dengue outbreak prediction: a least squares support vector machines approach. Int. J. Comput. Theory Eng. 3(4), 489 (2011)

    CrossRef  Google Scholar 

Download references

Acknowledgments

The authors wish to thank the Universidad Tecnológica de Bolívar (Colombia) and Universidad Autónoma de México for their financial support (Grant: TRFCI-1P2016, D. M-G: Programa de Becas Posdoctorales en la UNAM 2016).

Author information

Authors and Affiliations

  1. Grupo de Investigación de Tecnologías Aplicadas y Sistemas de Información, School of Engineering, Universidad Tecnológica de Bolívar, Cartagena, 130011, Colombia

    William Caicedo-Torres

  2. Grupo de Investigación en Estudios Químicos y Biológicos, School of Basic Sciences, Universidad Tecnológica de Bolívar, Cartagena, 130011, Colombia

    Diana Montes-Grajales, Wendy Miranda-Castro & Mary Fennix-Agudelo

  3. Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, 565-A, Cuernavaca, Mexico

    Diana Montes-Grajales

  4. School of Engineering, Universidad Tecnológica de Bolívar, Cartagena, 130011, Colombia

    Nicolas Agudelo-Herrera

Authors
  1. William Caicedo-Torres
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana Montes-Grajales
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wendy Miranda-Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mary Fennix-Agudelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicolas Agudelo-Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William Caicedo-Torres .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Occidente, Cali, Colombia

    Andrés Solano

  2. Universidad de San Buenaventura, Cali, Colombia

    Hugo Ordoñez

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Caicedo-Torres, W., Montes-Grajales, D., Miranda-Castro, W., Fennix-Agudelo, M., Agudelo-Herrera, N. (2017). Kernel-Based Machine Learning Models for the Prediction of Dengue and Chikungunya Morbidity in Colombia. In: Solano, A., Ordoñez, H. (eds) Advances in Computing. CCC 2017. Communications in Computer and Information Science, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-319-66562-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66562-7_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66561-0

  • Online ISBN: 978-3-319-66562-7

  • eBook Packages: Computer ScienceComputer Science (R0)