publication . Preprint . Conference object . Other literature type . 2016

An Accurate Gaussian Process-Based Early Warning System for Dengue Fever

Albinati, Julio; Meira, Wagner; Pappa, Gisele L.;
Open Access English
  • Published: 10 Aug 2016
Abstract
Dengue fever is a mosquito-borne disease present in all Brazilian territory. Brazilian government, however, lacks an accurate early warning system to quickly predict future dengue outbreaks. Such system would help health authorities to plan their actions and to reduce the impact of the disease in the country. However, most attempts to model dengue fever use parametric models which enforce a specific expected behaviour and fail to capture the inherent complexity of dengue dynamics. Therefore, we propose a new Bayesian non-parametric model based on Gaussian processes to design an accurate and flexible model that outperforms previous/standard techniques and can be ...
Subjects
free text keywords: Statistics - Applications

[1] World Health Organization and Special Programme for Research and Training in Tropical Diseases, Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization, 2009.

[2] B. Samir, P. W. Gething, O. J. Brady, J. P. Messina, A. W. Farlow, C. L. Moyes, J. M. Drake, J. S. Brownstein, A. G. Hoen, O. Sankoh, M. F. Myers, D. B. George, T. Jaenisch, G. R. W. Wint, C. P. Simmons, T. W. Scott, J. J. Farrar, and S. I. Hay, “The global distribution and burden of dengue,” Nature, vol. 496, no. 7446, p. 504507, apr 2013.

[3] S. Naish, P. Dale, J. S. Mackenzie, J. McBride, K. Mengersen, and S. Tong, “Climate change and dengue: a critical and systematic review of quantitative modelling approaches,” BMC infectious diseases, vol. 14, no. 1, p. 167, 2014.

[4] V. R. Louis, R. Phalkey, O. Horstick, P. Ratanawong, A. WilderSmith, Y. Tozan, and P. Dambach, “Modeling tools for dengue risk mapping - a systematic review,” International Journal of Health Geographics, vol. 13, no. 1, pp. 1-15, 2014. [Online]. Available: http://dx.doi.org/10.1186/1476-072X-13-50 [OpenAIRE]

[5] W. Hu, A. Clements, G. Williams, S. Tong, and K. Mengersen, “Spatial patterns and socioecological drivers of dengue fever transmission in queensland, australia,” Environmental health perspectives, vol. 120, no. 2, p. 260, 2012.

[6] X. Porcasi, C. H. Rotela, M. V. Introini, N. Frutos, S. Lanfri, G. Peralta, E. A. De Elia, M. A. Lanfri, and C. M. Scavuzzo, “An operative dengue risk stratification system in argentina based on geospatial technology,” Geospatial Health, vol. 6, no. 3, pp. 31-42, 2012.

[7] S.-C. Chen and M.-H. Hsieh, “Modeling the transmission dynamics of dengue fever: implications of temperature effects,” Science of the Total Environment, vol. 431, pp. 385-391, 2012.

[8] A. Earnest, S. Tan, and A. Wilder-Smith, “Meteorological factors and el nino southern oscillation are independently associated with dengue infections,” Epidemiology and infection, vol. 140, no. 7, pp. 1244-1251, 2012.

[9] M. Gharbi, P. Quenel, J. Gustave, S. Cassadou, G. L. Ruche, L. Girdary, and L. Marrama, “Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors,” BMC infectious diseases, vol. 11, no. 1, p. 166, 2011.

[10] W. Hu, A. Clements, G. Williams, and S. Tong, “Dengue fever and el nino/southern oscillation in Queensland, Australia: a time series predictive model,” Occupational and environmental medicine, vol. 67, no. 5, pp. 307-311, 2010.

[11] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Massachusetts Institute of Technology, Cambridge, Massachusetts 02142: The MIT Press, 2005.

[12] C. Chen and L.-M. Liu, “Joint estimation of model parameters and outlier effects in time series,” Journal of the American Statistical Association, vol. 88, no. 421, pp. 284-297, 1993. [Online]. Available: http://www.jstor.org/stable/2290724

[13] J. Vanhatalo and A. Vehtari, “Sparse log gaussian processes via mcmc for spatial epidemiology.” in Gaussian Processes in Practice, 2007, pp. 73-89. [OpenAIRE]

[14] R. Lowe, “Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in brazil,” Ph.D. dissertation, University of Exeter, 2010.

Abstract
Dengue fever is a mosquito-borne disease present in all Brazilian territory. Brazilian government, however, lacks an accurate early warning system to quickly predict future dengue outbreaks. Such system would help health authorities to plan their actions and to reduce the impact of the disease in the country. However, most attempts to model dengue fever use parametric models which enforce a specific expected behaviour and fail to capture the inherent complexity of dengue dynamics. Therefore, we propose a new Bayesian non-parametric model based on Gaussian processes to design an accurate and flexible model that outperforms previous/standard techniques and can be ...
Subjects
free text keywords: Statistics - Applications

[1] World Health Organization and Special Programme for Research and Training in Tropical Diseases, Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization, 2009.

[2] B. Samir, P. W. Gething, O. J. Brady, J. P. Messina, A. W. Farlow, C. L. Moyes, J. M. Drake, J. S. Brownstein, A. G. Hoen, O. Sankoh, M. F. Myers, D. B. George, T. Jaenisch, G. R. W. Wint, C. P. Simmons, T. W. Scott, J. J. Farrar, and S. I. Hay, “The global distribution and burden of dengue,” Nature, vol. 496, no. 7446, p. 504507, apr 2013.

[3] S. Naish, P. Dale, J. S. Mackenzie, J. McBride, K. Mengersen, and S. Tong, “Climate change and dengue: a critical and systematic review of quantitative modelling approaches,” BMC infectious diseases, vol. 14, no. 1, p. 167, 2014.

[4] V. R. Louis, R. Phalkey, O. Horstick, P. Ratanawong, A. WilderSmith, Y. Tozan, and P. Dambach, “Modeling tools for dengue risk mapping - a systematic review,” International Journal of Health Geographics, vol. 13, no. 1, pp. 1-15, 2014. [Online]. Available: http://dx.doi.org/10.1186/1476-072X-13-50 [OpenAIRE]

[5] W. Hu, A. Clements, G. Williams, S. Tong, and K. Mengersen, “Spatial patterns and socioecological drivers of dengue fever transmission in queensland, australia,” Environmental health perspectives, vol. 120, no. 2, p. 260, 2012.

[6] X. Porcasi, C. H. Rotela, M. V. Introini, N. Frutos, S. Lanfri, G. Peralta, E. A. De Elia, M. A. Lanfri, and C. M. Scavuzzo, “An operative dengue risk stratification system in argentina based on geospatial technology,” Geospatial Health, vol. 6, no. 3, pp. 31-42, 2012.

[7] S.-C. Chen and M.-H. Hsieh, “Modeling the transmission dynamics of dengue fever: implications of temperature effects,” Science of the Total Environment, vol. 431, pp. 385-391, 2012.

[8] A. Earnest, S. Tan, and A. Wilder-Smith, “Meteorological factors and el nino southern oscillation are independently associated with dengue infections,” Epidemiology and infection, vol. 140, no. 7, pp. 1244-1251, 2012.

[9] M. Gharbi, P. Quenel, J. Gustave, S. Cassadou, G. L. Ruche, L. Girdary, and L. Marrama, “Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors,” BMC infectious diseases, vol. 11, no. 1, p. 166, 2011.

[10] W. Hu, A. Clements, G. Williams, and S. Tong, “Dengue fever and el nino/southern oscillation in Queensland, Australia: a time series predictive model,” Occupational and environmental medicine, vol. 67, no. 5, pp. 307-311, 2010.

[11] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Massachusetts Institute of Technology, Cambridge, Massachusetts 02142: The MIT Press, 2005.

[12] C. Chen and L.-M. Liu, “Joint estimation of model parameters and outlier effects in time series,” Journal of the American Statistical Association, vol. 88, no. 421, pp. 284-297, 1993. [Online]. Available: http://www.jstor.org/stable/2290724

[13] J. Vanhatalo and A. Vehtari, “Sparse log gaussian processes via mcmc for spatial epidemiology.” in Gaussian Processes in Practice, 2007, pp. 73-89. [OpenAIRE]

[14] R. Lowe, “Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in brazil,” Ph.D. dissertation, University of Exeter, 2010.

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