Ir al contenido

Documat

Climate-driven statistical models as efective predictions of local dengue indicence in Costa Rica: a generalized additive model and random forest approach

  • VÁSQUEZ, PAOLA [1] ; LORÍA, ANTONIO [1] ; SÁNCHEZ, FABIO [1] ; BARBOZA, LUIS ALBERTO
    1. [1] Universidad de Costa Rica

      Universidad de Costa Rica

      Hospital, Costa Rica

  • Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 27, Nº. 1, 2020 (Ejemplar dedicado a: Revista de Matemática: Teoría y Aplicaciones), págs. 1-21
  • Idioma: inglés
  • DOI: 10.15517/rmta.v27i1.39931
  • Títulos paralelos:
    • Variables climáticas como predictores de la incidencia de dengue en Costa Rica: un enfoque de modelo aditivo generalizado y bosques aleatorios
  • Enlaces
  • Resumen
    • español

      En países tropicales y subtropicales alrededor del mundo, el clima ha sido un factor fundamental en moldear la distribución geográfica e incidencia de los casos de dengue. En Costa Rica, un país tropical con múltiples microclimas, el dengue ha sido endémico desde 1993, con repercusiones no solo en el ámbito de la salud, sino también en el social y económico. Utilizando el número de casos de dengue y los datos climáticos del 2007-2017, ajustamos un modelo predictivo mediante un enfoque de Modelo Aditivo Generalizado y Random Forest, el cual nos permitió predecir de forma retrospectiva el riesgo relativo de dengue en cinco cantones alrededor del país.

    • English

      Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.

  • Referencias bibliográficas
    • [1] E.J. Alfaro, F.J. Soley, Descripción de dos métodos de rellenado de datos ausentes en series de tiempo meteorológicas, Revista de Matemática:...
    • [2] B.W. Alto, D. Bettinardi, Temperature and dengue virus infection in mosquitoes: Independent effects on the immature and adult stages,...
    • [3] AmericanMeteorologicalSociety,Precipitation.GlossaryofMeteorology, (2012) Available from: http://glossary.ametsoc.org/wiki/ Precipitation....
    • [4] C.M. Benedum, O. Seidahmed, E. Eltahir, N. Markuzon, Statistical modeling of the effect of rainfall flushing on dengue transmission in...
    • [5] S. Bhatt, P. Gething, O. Brady, J. Messina, A. Farlow, C.L. Moyes, J.M. Drake, ..., S.I. Hay, The global distribution and burden of dengue,...
    • [6] O.J. Brady, P.W. Gething, S. Bhatt, J.P. Messina, J.S. Brownstein, A.G. Hoen, C.L. Moyes, ..., S.I. Hay, Refining the global spatial limits...
    • [7] O.J. Brady, N. Golding, D. Pigott, M. Kraemer, J.P. Messina, R.C. Reiner, T.W. Scott, ..., S.I. Hay, Global temperature constraints on...
    • [8] L. Breiman, Random forests, Machine Learning 45(2001),no.1,5–32.doi: 10.1023/A:1010933404324
    • [9] M. Cabrera, G. Taylor, Modelling spatio-temporal data of dengue fever using generalized additive mixed models, Spat Spatiotemporal Epidemiol...
    • [10] Caja Costarricense de Seguro Social, Egresos Hospitalarios debidos a dengue según establecimiento de salud C.C.S.S., 1997-2018, Área...
    • [11] T.M. Carvajal, K.M. Viacrusis, L.F.T. Hernandez, H.T. Ho, D.M. Amalin, K. Watanabe, Machine learning methods reveal the temporal pattern...
    • [12] M.C. Castro, M.E. Wilson, D.E. Blomm, Disease and economic burdens of dengue, Lancet Infect Dis 17(2017), no. 3, e70–e78. doi: 10.1016/...
    • [13] D.V. Canyon, J.L.. Hii, R. Müller, Adaptation of Aedes aegypti (Diptera: Culicidae) oviposition behavior in response to humidity and...
    • [14] M. Chan, M.A. Johansson, The incubation periods of dengue viruses, PloS one 7(2012), no. 11, e50972. doi: 10.1371/journal.pone. 0050972
    • [15] G.Chowell,F.Sanchez,Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico, J Environ Health 68(2006),...
    • [16] E.A. Costa, E.M. Santos, J.C. Correia, C.M.R. Albuquerque, Impact of small variations in temperature and humidity on the reproductive...
    • [17] E.Descloux,M.Mangeas,C.E.Menkes,M.Lengaigne,A.Leroy,L.Guillaumot, M. Teurlai, ..., X. Lamballerie, Climate-based models for understanding...
    • [18] O.B. Dick, J.L. San-Martín, R.H. Montoya, J. del Diego, B. Zambrano, G.H.Dayan, The history of dengue outbreaks in the Americas, Am J...
    • [19] K.L. Ebi, J. Nealon,Dengue in a changing climate, Environ Res 151(2016), 115–123. doi: 10.1016/j.envres.2016.07.026
    • [20] J.A. Falcón-Lezama, M. Betancourt-Cravioto, R. Tapia-Conyer (Eds.), Dengue fever: a resilient threat in the face of innovation, Intech...
    • [21] D.O. Fuller, A. Troyo, J.C. Beier, El Niño Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa...
    • [22] A.S. Gagnon, A.B.G. Bush, K.E. Smoyer-Tomic, Dengue epidemics and the El Niño southern oscillation, Clim Res 19(2001), no. 1, 35–43....
    • [23] M. González-Elizondo, Informe de vigilancia de Arbovirus basada en laboratorio, Centro Nacional de Referencia de Virología, Inciensa,...
    • [24] D.J. Gubler, The economic burden of dengue, Am J Trop Med Hyg 86(2012), no. 5, 743–744. doi: 10.4269/ajtmh.2012.12-0157
    • [25] S.Hales,P.Weinstein,Y.Souares,A.Woodward,ElNiñoandthedynamics of vectorborne disease transmission, Environ Health Perspect 107(1999),...
    • [26] H. Halide, P. Ridd, A predictive model for dengue hemorrhagic fever epidemics, Int J Environ Health Res 18(2008), no. 4, 253–265. doi:...
    • [27] T. Hastie, R. Tibshirani, Generalized additive models: some applications, J Am Stat Assoc 82(2009), no. 398, 371–386. doi: 10.2307/2289439,...
    • [28] T.Hastie,R.Tibshirani,J.Friedman,The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2009.
    • [29] C.M. Hernández-Suárez, O. Mendoza-Cano, Empirical evidence of the effect of school gathering on the dynamics of dengue epidemics, Glob...
    • [30] Y.L.Hii, H.Zhu, N.Ng, L.CNg, L.Ching, J.Rocklöv, Forecast of dengue incidence using temperature and rainfall,PLoS Negl Trop Dis6(2012),no....
    • [31] M.J. Hopp, J.A. Foley, Worldwide fluctuations in dengue fever cases related to climate variability, Clim Res 25(2003), no. 1, 85–94. doi:...
    • [32] Instituto de Desarrollo Rural, Caracterización del territorio central occidental, 2015. Available from: https://www. inder.go.cr/Alajuela-Poas-Grecia-ValverdeVega/...
    • [33] Instituto de Desarrollo Rural, Caracterización del territorio Santa Cruz-Carrillo, 2016. Available from: https://www.inder.go.cr/santacruz-carrillo/...
    • [34] Instituto Meteorológico Nacional, Clima en Costa Rica. El clima y las regiones climáticas de Costa Rica, https://www.imn.ac.cr/ documents/10179/31165/clima-regiones-climat.pdf/...
    • [35] C.J.M. Koenraadt, L.C. Harrington, Flushing effect of rain on containerinhabiting mosquitoes Aedes aegypti and Culex pipiens (Diptera:...
    • [36] M. Kuhn, Caret: Classification and regression training, 2019, Available from: https://CRAN.R-project.org/package=caret Accessed March...
    • [37] R. Li, L. Xu, O.N. Bjømstad, K. Liu, T. Song, A. Chen , B. Xu , Q. Liu, N.C. Stenseth, Climate-driven variation in mosquito density predicts...
    • [38] A. Liaw, M. Wiener, Classification and regression by random forest, R News2(2002), no. 3, 18–22.
    • [39] R. Lowe, A. Gasparrini, C.J. Van Meerbeeck, C.A. Lippi, R. Mahon, A.R. Trotman, L.R. Hinds, S.J. Ryan, A.M. Stewart-Ibarra, Nonlinear...
    • [40] R. Lowe, A.M. Stewart-Ibarra, D. Petrova, M. García-Díez, M.J. BorborCordova, R. Mejía, M. Regato, X. Rodó, Climate services for health:...
    • [41] P. Manso, W. Stolz, J.C. Fallas, El régimen de la precipitación en Costa Rica, Ambientico. Revista Mensual sobre la Actualidad Ambiental...
    • [42] Ministerio de Ambiente y Energía, Instituto Meteorológico Nacional, Descripción del clima de Limón, 2016 Available from: http://cglobal.imn.ac.cr/documentos/...
    • [43] Ministerio de Salud, Análisis de Situación de Salud. Available from: https://www.ministeriodesalud. go.cr/index.php/vigilancia-de-la-salud/...
    • [44] Ministerio de Salud, Lineamientos Nacionales para el control del Dengue, 2010. Available from: http://www.solucionesss.com/ descargas/G-Leyes/LINEAMIENTOS_NACIONALES_PARA_...
    • [45] Ministerio de Vivienda y Asentamientos Humanos, Política Nacional de Ordenamiento Territorial 2012 a 2040. Contextualización y Línea...
    • [46] E. Mordecai, J.M. Cohen, M.V. Evans, P. Gudapati, L.R. Johnson, C.A. Lippi, K. Miazgowicz, ...,D.P. Weikel, Detecting the impact of temperature...
    • [47] A. Morice, R. Marín, M.L. Ávila-Agüero,El dengue en Costa Rica: evolución histórica, situación actual y desafíos, in: La salud pública...
    • [48] C.W. Morin, A.C. Comrie, K. Ernst, Climate and dengue transmission: Evidence and implications, Environ Health Perspect121(2013), no....
    • [49] S. Naish, P. Dale, J.S. Mackenzie, J.McBride, K. Mengersen, S. Tong, Climate change and dengue: a critical and systematic review of quantitative...
    • [50] National Centers for Environmental Information, Equatorial Pacific Sea Surface Temperatures, 2019. https://www.ncdc.noaa.gov/ teleconnections/enso/indicators/sst/....
    • [51] F. O’Sullivan, B.S. Yandell, W.J. Raynor, Automatic smoothing of regression functions in generalized linear models, J Am Stat Assoc 81(1986),no....
    • [52] Pan American Health Organization, Reported cases of dengue fever in the Americas. Available from: https://www.ministeriodesalud. go.cr/index.php/vigilancia-de-la-salud/...
    • [53] N.C. Pinheiro Rodrigues, V.T. Saraiva Lino, R.P. Paiva Daumas, M. de Noronha Andrade, G. O’Dwyer, D. Lite Maia, A. Gerardi,...,I. da...
    • [54] A.M. Ramirez, H.A. Chamizo, J.C. Fallas, El fenómeno ENOS y el dengue, regiones Pacífico Central y Huetar Atlántico, Costa Rica, 1990...
    • [55] E.M. Rasmusson, T.H. Carpenter, Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El...
    • [56] L.M. Rueda, K.J. Patel, R.C. Axtell, R.E. Stinner, Temperature-dependent development and survival rates of Culex quinquefasciatus and...
    • [57] F. Sanchez, L.A. Barboza, P. Vásquez, Parameter estimates of the 20162017 Zika outbreak in Costa Rica: an approximate Bayesian computation...
    • [58] M. Tipayamongkholgul, F. Chi-Tai, S. Klinchan, C.M Liu, C.C. King, Effects of the El Niño-Southern Oscillation on dengue epidemics in...
    • [59] A. Troyo, O. Calderón-Arguedas, D.O. Fuller, M.E. Solano, A. Avendaño, K.L. Arheart, D.D. Chadee, J.C. Beier, Seasonal profiles of Aedes...
    • [60] W. Tun-Lin, T.R. Burkot, B.H. Kay, Effects of temperature and larval diet on development rates and survival of the dengue vector Aedes...
    • [61] L.D. Valdez, G.J. Sibona, C.A. Condat, Impact of rainfall on Aedes aegypti populations, Ecol Modell 385(2018), 96–105. doi: 10.1016/j....
    • [62] D.M. Watts, D.S. Burke, B.A. Harrison, R.E. Whitmire, A. Nisalak, Effect of temperature on the vector efficiency of Aedes aegypti for...
    • [63] S.N. Wood, Generalized Additive Models. An Introduction with R, Chapman & Hall/CRC, New York, 2017. doi: 10.1201/9781315370279
    • [64] S.N. Wood, Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models,...
    • [65] World Health Organization, Using climate to predict infectious disease epidemics, Climate change and human health, 2005. Available from:...
    • [66] World Health Organization, Global strategy for dengue prevention and control2012-2020,WHOReport,2012.Availablefrom: https://www. who.int/denguecontrol/9789241504034/en/....
    • [67] World Meteorological Organization, Guide to meteorological instruments and Methods of Observation, Seventh Edition, WMO 8, pp. 681, 2008....
    • [68] World Meteorological Organization, Use of climate predictions to manage risks, 2017. Available from: https://public.wmo.int/en/media/news/...
    • [69] P.C. Wu, H.R. Guo, S.C. Lung, C.Y. Lin, H.J. Su, Weather as an effective predictor for occurrence of dengue fever in Taiwan, Acta Tropica...
    • [70] J. Xiao, T. Liu, H. Lin, G. Zhu, W. Zeng, Weather variables and the El Nino Southern Oscillation may drive the epidemics of dengue in...
    • [71] F.Z. Xiao, Y. Zhang, Y.Q. Deng, S.He, H.G. Xie, X.N. Zhou, Y.S. Yan, The effect of temperature on the extrinsic incubation period and...
    • [72] H.M. Yang, M.G. Macoris, K.C. Galvani, M.T.M. Andrighetti, D.M.V.Wanderley, Assessing the effects of temperature on the population of...
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno