Abstract
Based solely on the dengue confirmed-cases of six densely populated urban areas in Brazil, distributed along the country, we propose in this paper regularized linear and nonlinear autoregressive models for one-week ahead prediction of the future behaviour of each time series. Though exhibiting distinct temporal behaviour, all the time series were properly predicted, with a consistently better performance of the nonlinear predictors, based on MLP neural networks. Additional local information associated with environmental conditions will possibly improve the performance of the predictors. However, without including such local environmental variables, such as temperature and rainfall, the performance was proven to be acceptable and the applicability of the methodology can then be directly extended to endemic areas around the world characterized by a poor monitoring of environmental conditions. For tropical countries, predicting the short-term evolution of dengue confirmed-cases may represent a decisive feedback to guide the definition of effective sanitary policies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, T.W.: Estimating linear statistical relationships. Ann. Stat. 12(1), 1–45 (1984)
Battiti, R.: First and Second-order methods for learning: between steepest descent and Newton’s method. Neural Comput. 4, 141–166 (1992)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, New York (1995)
Ministério da Saúde.: Monitoramento dos casos de dengue e febre de chikungunya até a Semana Epidemiológica (SE) 53 de 2014. Bol. Epidemiológico 46, 3 (2015)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis, Forecasting and Control. Holden Day, San Francisco (1976)
Bradley, E.: Time-series analysis. In: Berthold, M., Hand, D. (eds.) Intelligent Data Analysis: An Introduction, 2nd edn. Springer, Heidelberg (2003)
Chakraborty, K., Mehrotra, K., Mohan, C.K., Ranka, S.: Forecasting the behavior of multivariate time series using neural networks. Neural Netw. 5, 961–970 (1992)
Cherkassky, V., Mulier, F.: Learning from Data: Concepts, Theory, and Methods, 2nd edn. Wiley IEEE Press, New York (2007)
Crompton, D.W.T., Peters, P. (eds.): World Health Organization. Working to overcome the Global Impact of Neglected Tropical Diseases, First WHO Report on Neglected Tropical Diseases, Switzerland, 172 p. (2010)
Forattini, O.P.: Culicidologia Médica, vol. 1, 864 p. EDUSP, São Paulo (2002)
Girosi, F., Jones, M., Poggio, T.: Regularization theory and neural network architectures. Neural Comput. 7(2), 219–269 (1995)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall, Upper Saddle River (2008)
Hopp, M.J., Foley, J.A.: Global-scale relationships between climate and the dengue fever vector Aedes aegypti. Clim. Change 48, 441–463 (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybernet. 2, 107–122 (2011)
Kulaif, A.C.P., Von Zuben, F.J.: Improved regularization in extreme learning machines. In: 11th Brazilian Congress on Computational Intelligence, pp 1–6 (2013)
Luenberger, D.G.: Introduction to Linear and Nonlinear Programming. Addison-Wesley, Reading (1973)
Mairuhu, A.T.A., Wangenaar, J., Brandjes, D.M.P., Gorp, E.C.M.: Dengue: an arthropod-borne disease of global importance. Eur. J. Clin. Microbiol. Infect. Dis. 23, 425–433 (2004)
Ministério da Saúde 1996. Plano Diretor de Erradicação do Aedes aegypti no Brasil, Brasília, pp. 78–79 (1996)
Moody, J.E.: The effective number of parameters:an analysis of generalization and regularization in nonlinear learning systems. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (eds.) Advances in Neural Information Processing Systems, vol. 4, pp. 847–854. Morgan Kaufmann, San Mateo (1992)
Prechelt, L.: Automatic early stopping using cross validation: quantifying the criteria. Neural Netw. 11(4), 761–767 (1998)
Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. Am. Stat. 42(1), 59–66 (1988)
Teixeira, M.G.: Epidemiologia e medidas de prevenção do dengue. Informe Epidemiológico do SUS-IESUS 8, 5–8 (1999)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58(1), 267–288 (1996)
van der Smagt, P.: Minimization methods for training feedforward neural networks. Neural Netw. 7(1), 1–11 (1994)
Weigend, A.S., Gershenfeld, N.A.: Time Series Prediction: Forecasting the Future and Understanding the Past. Perseus Press, New York (1993)
Wentian, L.: Mutual information functions versus correlation functions. J. Stat. Phys. 60(5–6), 823–837 (1990)
Acknowledgements
The authors would like to thank FAPESP (São Paulo Research Foundation), process [2014/05101-2], and CNPq for the financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sousa, L.B., Von Zuben, C.J., Von Zuben, F.J. (2015). Regularized Linear and Nonlinear Autoregressive Models for Dengue Confirmed-Cases Prediction. In: Calude, C., Dinneen, M. (eds) Unconventional Computation and Natural Computation. UCNC 2015. Lecture Notes in Computer Science(), vol 9252. Springer, Cham. https://doi.org/10.1007/978-3-319-21819-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-21819-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21818-2
Online ISBN: 978-3-319-21819-9
eBook Packages: Computer ScienceComputer Science (R0)