Revista de Matemática: Teoría y Aplicaciones ISSN Impreso: 1409-2433 ISSN electrónico: 2215-3373

OAI: https://revistas.ucr.ac.cr/index.php/matematica/oai
Variables climáticas como predictores de la incidencia de dengue en Costa Rica: un enfoque de modelo aditivo generalizado y bosques aleatorios
PDF (English)
PS (English)
DVI (English)

Palabras clave

mosquito-borne diseases
dengue
climate variables
Costa Rica
generalized additive models
random forests
enfermedades de trasmisión vectorial
dengue
variables climáticas
Costa Rica
modelos aditivos generalizados
bosques aleatorios

Cómo citar

Vásquez, P., Loría, A., Sánchez, F., & Barboza, L. A. (2019). Variables climáticas como predictores de la incidencia de dengue en Costa Rica: un enfoque de modelo aditivo generalizado y bosques aleatorios. Revista De Matemática: Teoría Y Aplicaciones, 27(1), 1–21. https://doi.org/10.15517/rmta.v27i1.39931

Resumen

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.

https://doi.org/10.15517/rmta.v27i1.39931
PDF (English)
PS (English)
DVI (English)

Citas

E.J. Alfaro, F.J. Soley, Descripción de dos métodos de rellenado de datos ausentes en series de tiempo meteorológicas, Revista de Matemática: Teoría y Aplicaciones 16(2009), no. 1, 60–75. doi: 10.15517/rmta. v16i1.1419

B.W. Alto, D. Bettinardi, Temperature and dengue virus infection in mosquitoes: Independent effects on the immature and adult stages, Am J Trop Med Hyg 88(2013), no. 3, 497–505. doi: 10.4269/ajtmh. 12-0421

AmericanMeteorologicalSociety,Precipitation.GlossaryofMeteorology, (2012) Available from: http://glossary.ametsoc.org/wiki/ Precipitation. Accessed Feb 19, 2019.

C.M. Benedum, O. Seidahmed, E. Eltahir, N. Markuzon, Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore, PLoS Negl Trop Dis 12(2018), no. 12, e0006935. doi: 10.1371/ journal.pntd.0006935

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, Nature 496(2013), no. 7446, 504–507. doi: 10.1038/nature12060

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 of dengue virus transmission by evidence-based consensus, PLoS Negl Trop Dis 6(2012), no. 8, e1760. doi: 10.1371/journal.pntd.0001760

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 Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission, Parasit Vectors 7(2014), no. 338. doi: 10.1186/ 1756-3305-7-338

L. Breiman, Random forests, Machine Learning 45(2001),no.1,5–32.doi: 10.1023/A:1010933404324

M. Cabrera, G. Taylor, Modelling spatio-temporal data of dengue fever using generalized additive mixed models, Spat Spatiotemporal Epidemiol 28(2019), 1–13. doi: 10.1016/j.sste.2018.11.006

Caja Costarricense de Seguro Social, Egresos Hospitalarios debidos a dengue según establecimiento de salud C.C.S.S., 1997-2018, Área de Estadística en Salud, 2018.

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 of dengue incidence using meteorological factors in metropolitan Manila, Philippines, BMC Infect Dis 18(2018), no. 183. doi: 10.1186/ s12879-018-3066-0

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/ S1473-3099(16)30545-X

D.V. Canyon, J.L.. Hii, R. Müller, Adaptation of Aedes aegypti (Diptera: Culicidae) oviposition behavior in response to humidity and diet, J Insect Physiol 45(1999), no. 10, 959–964. doi: 10.1016/ S0022-1910(99)00085-2

M. Chan, M.A. Johansson, The incubation periods of dengue viruses, PloS one 7(2012), no. 11, e50972. doi: 10.1371/journal.pone. 0050972

G.Chowell,F.Sanchez,Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico, J Environ Health 68(2006), no. 10, 40–44.

E.A. Costa, E.M. Santos, J.C. Correia, C.M.R. Albuquerque, Impact of small variations in temperature and humidity on the reproductive activity and survival of Aedes aegypti (Diptera, Culicidae), Rev Bras Entomol 54(2010), no. 3, 488–493. doi: 10.1590/ S0085-56262010000300021

E.Descloux,M.Mangeas,C.E.Menkes,M.Lengaigne,A.Leroy,L.Guillaumot, M. Teurlai, ..., X. Lamballerie, Climate-based models for understanding and forecasting dengue epidemics, PLoS Negl Trop Dis 6(2012), no. 2, e1470. doi: 10.1371/journal.pntd.0001470

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 Trop Med Hyg 87(2012), no. 4, 584–593. doi: 10.4269/ajtmh.2012. 11-0770

K.L. Ebi, J. Nealon,Dengue in a changing climate, Environ Res 151(2016), 115–123. doi: 10.1016/j.envres.2016.07.026

J.A. Falcón-Lezama, M. Betancourt-Cravioto, R. Tapia-Conyer (Eds.), Dengue fever: a resilient threat in the face of innovation, Intech Open, London, 2019. doi: 10.5772/intechopen.73901

D.O. Fuller, A. Troyo, J.C. Beier, El Niño Southern Oscillation and vegetation dynamics as predictors of dengue fever cases in Costa Rica,Environ Ress Lett 4(2009). doi: 10.1088/1748-9326/4/1/014011

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. doi: 10.3354/cr019035

M. González-Elizondo, Informe de vigilancia de Arbovirus basada en laboratorio, Centro Nacional de Referencia de Virología, Inciensa, 2018. Available from: https://bit.ly/2klQATO. Accessed Jan 23, 2019.

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

S.Hales,P.Weinstein,Y.Souares,A.Woodward,ElNiñoandthedynamics of vectorborne disease transmission, Environ Health Perspect 107(1999), no. 2, 99–102. doi: 10.1289/ehp.9910799

H. Halide, P. Ridd, A predictive model for dengue hemorrhagic fever epidemics, Int J Environ Health Res 18(2008), no. 4, 253–265. doi: 10. 1080/09603120801966043

T. Hastie, R. Tibshirani, Generalized additive models: some applications, J Am Stat Assoc 82(2009), no. 398, 371–386. doi: 10.2307/2289439, and 10.1007/978-1-4615-7070-7_8

T.Hastie,R.Tibshirani,J.Friedman,The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2009.

C.M. Hernández-Suárez, O. Mendoza-Cano, Empirical evidence of the effect of school gathering on the dynamics of dengue epidemics, Glob Health Action 9(2016), no. 1, 1–7. doi: 10.3402/gha.v9.28026

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. 11, e1908. doi: 10.1371/journal.pntd.0001908

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: 10. 3354/cr025085

Instituto de Desarrollo Rural, Caracterización del territorio central occidental, 2015. Available from: https://www. inder.go.cr/Alajuela-Poas-Grecia-ValverdeVega/ Caracterizacion-Alajuela-Poas-Gecia-Valverde-Vega. pdf. Accessed Feb 27, 2019.

Instituto de Desarrollo Rural, Caracterización del territorio Santa Cruz-Carrillo, 2016. Available from: https://www.inder.go.cr/santacruz-carrillo/ Caracterizacion-territorio-SantaCruz-Carrillo. pdf. Accessed Feb 19, 2019.

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/ cb3b55c3-f358-495a-b66c-90e677e35f57. Accessed Jan 15, 2019.

C.J.M. Koenraadt, L.C. Harrington, Flushing effect of rain on containerinhabiting mosquitoes Aedes aegypti and Culex pipiens (Diptera: Culicidae, J Med Entomol 45(2008), no. 1, 28–35. doi: 10.1603/ 0022-2585(2008)45[28:feoroc]2.0.co;2

M. Kuhn, Caret: Classification and regression training, 2019, Available from: https://CRAN.R-project.org/package=caret Accessed March 7, 2019.

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 the spatiotemporal dynamics of dengue, Proc Natl Acad Sci 116(2019), no. 9, 3642–3629. doi: 10.1073/pnas.1806094116

A. Liaw, M. Wiener, Classification and regression by random forest, R News2(2002), no. 3, 18–22.

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 and delayed impacts of climate on dengue risk in Barbados: a modelling study, PloS med. 15(2018), no. 7. doi: 10.1371/journal.pmed.1002613

R. Lowe, A.M. Stewart-Ibarra, D. Petrova, M. García-Díez, M.J. BorborCordova, R. Mejía, M. Regato, X. Rodó, Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador, The Lancet Planetary Health 1(2017), no. 4, e142–e151. doi: 10.1016/ S2542-5196(17)30064-5

P. Manso, W. Stolz, J.C. Fallas, El régimen de la precipitación en Costa Rica, Ambientico. Revista Mensual sobre la Actualidad Ambiental 144(2005), 7–8.

Ministerio de Ambiente y Energía, Instituto Meteorológico Nacional, Descripción del clima de Limón, 2016 Available from: http://cglobal.imn.ac.cr/documentos/ publicaciones/DescripciondelClimaSERIE/ DescripcionClimaCantonLimon/html5/index.html? page=1&noflash. Accessed Jan 22, 2019.

Ministerio de Salud, Análisis de Situación de Salud. Available from: https://www.ministeriodesalud. go.cr/index.php/vigilancia-de-la-salud/ analisis-de-situacion-de-salud. Accessed June 15, 2019.

Ministerio de Salud, Lineamientos Nacionales para el control del Dengue, 2010. Available from: http://www.solucionesss.com/ descargas/G-Leyes/LINEAMIENTOS_NACIONALES_PARA_ EL_CONTROL_DEL_DENGUE.pdf. Accessed December 17, 2018.

Ministerio de Vivienda y Asentamientos Humanos, Política Nacional de Ordenamiento Territorial 2012 a 2040. Contextualización y Línea Base, 2012. Available from: https://www.mivah.go.cr/ Documentos/politicas_directrices_planes/pnot/ Linea_Base_PNOT_2013-05-02.pdf. Accessed March 18, 2019.

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 on transmission of Zika, dengue, and chikungunya using mechanistic models,PLoSNeglTropDis11(2017),no.4.doi: 10.1371/journal. pntd.0005568

A. Morice, R. Marín, M.L. Ávila-Agüero,El dengue en Costa Rica: evolución histórica, situación actual y desafíos, in: La salud pública en Costa Rica. Estado actual, retos y perspectivas. San José, Universidad de Costa Rica, San José, 2010, pp. 197–217.

C.W. Morin, A.C. Comrie, K. Ernst, Climate and dengue transmission: Evidence and implications, Environ Health Perspect121(2013), no. 11-12, 1264–1272. doi: 10.1289/ehp.1306556

S. Naish, P. Dale, J.S. Mackenzie, J.McBride, K. Mengersen, S. Tong, Climate change and dengue: a critical and systematic review of quantitative modelling approaches, BMC infectious diseases 14(2014),no.1.doi: 10. 1186/1471-2334-14-167

National Centers for Environmental Information, Equatorial Pacific Sea Surface Temperatures, 2019. https://www.ncdc.noaa.gov/ teleconnections/enso/indicators/sst/. Accessed Jan 17, 2019.

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. 393, 96–103. doi: 10.1080/01621459.1986.10478243

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/ analisis-de-situacion-de-salud. Accessed May 2019.

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 Costa Leite, Temporal and Spatial Evolution of Dengue Incidence in Brazil, 2001-2012, PLoS One 11(2016), no. 11. doi: 10.1371/journal. pone.0165945

A.M. Ramirez, H.A. Chamizo, J.C. Fallas, El fenómeno ENOS y el dengue, regiones Pacífico Central y Huetar Atlántico, Costa Rica, 1990 a 2011, Población y Salud en Mesoamérica 15(2017), no. 1, 234–242. doi: 10. 15517/psm.v15i1.26189

E.M. Rasmusson, T.H. Carpenter, Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño, Mon Weather Rev 110(1982), no. 5, 354–384. doi: https://doi.org/10.1175/1520-0493(1982)110<0354: VITSST>2.0.CO;2

L.M. Rueda, K.J. Patel, R.C. Axtell, R.E. Stinner, Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae, J Med Entomol 27(1990), no. 5, 892–898. doi: 10.1093/jmedent/27.5.892

F. Sanchez, L.A. Barboza, P. Vásquez, Parameter estimates of the 20162017 Zika outbreak in Costa Rica: an approximate Bayesian computation (ABC) approach, Math Biosci Eng 16(2019), no. 4, 2738–2755. doi: 10. 3934/mbe.2019136

M. Tipayamongkholgul, F. Chi-Tai, S. Klinchan, C.M Liu, C.C. King, Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996-2005, BMC Public Health 27(2009), no. 1. doi: 10.1186/ 1471-2458-9-422

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 aegypti (Diptera: Culicidae) larval habitats in an urban area of Costa Rica with a history of mosquito control, J Vector Ecol 33(2008), no. 1, 76–88. doi: 10.3376/10811710(2008)33[76:spoaad]2.0.co;2

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 aegypti in north Queensland, Australia, Med Vet Entomol 14(2000), no. 1, 31–37. doi: 10.1046/j.1365-2915.2000.00207.x

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. ecolmodel.2018.07.003

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 dengue 2 virus, Am J Trop Med Hyg 36(1987), no. 1, 143–152. doi: 10.4269/ajtmh. 1987.36.143

S.N. Wood, Generalized Additive Models. An Introduction with R, Chapman & Hall/CRC, New York, 2017. doi: 10.1201/9781315370279

S.N. Wood, Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J R Stat Soc Series B (Stat Methodol) 73(2011), no. 1, 3–36. doi: 10.1111/j.14679868.2010.00749.x

World Health Organization, Using climate to predict infectious disease epidemics, Climate change and human health, 2005. Available from: https://www.who.int/globalchange/publications/ infectdiseases/en/. Accessed Feb 20, 2019.

World Health Organization, Global strategy for dengue prevention and control2012-2020,WHOReport,2012.Availablefrom: https://www. who.int/denguecontrol/9789241504034/en/. Accessed December 14, 2018.

World Meteorological Organization, Guide to meteorological instruments and Methods of Observation, Seventh Edition, WMO 8, pp. 681, 2008. Available from: https://www.weather.gov/media/epz/ mesonet/CWOP-WMO8.pdf

World Meteorological Organization, Use of climate predictions to manage risks, 2017. Available from: https://public.wmo.int/en/media/news/ use-of-climate-predictions-manage-risks

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 103(2007),no.1,50–57.doi: 10.1016/j.actatropica.2007.05. 014

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 Guangdong Province, China, Sci Total Environ 624(2018), 926–934. doi: 10. 1016/j.scitotenv.2017.12.200

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 infection rate of dengue virus serotype 2 infection in Aedes albopictus, Archives of Virology 159(2014), no. 11, 3053–3057. doi: 10.1007/s00705-014-2051-1

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 Aedes aegypti, the vector of dengue, Epidemiol Infect 137(2009), no. 8, 1188–1202 doi: 10.1017/S0950268809002040

Comentarios

Descargas

Los datos de descargas todavía no están disponibles.