Abstract
Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with microclimates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks. Climate information was incorporated to model and forecast the dengue cases and relative risks using a Bayesian spatio-temporal model, from 2000 to 2021, in 32 Costa Rican municipalities. This approach is capable of analyzing the spatio-temporal behavior of dengue and also producing reliable predictions.
Similar content being viewed by others
Code availability
The code and datasets is available online under https://github.com/shuwei325/DengueCR_Bayesian_ST_Prediction.git
References
Akter R, Hu W, Gatton M, Bambrick H, Cheng J, Tong S (2021) Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: a bayesian spatial analysis. Environ Res 195:110285
Barboza LA, Chou-Chen S-W, Vásquez P, García YE, Calvo JG, Hidalgo HG, Sanchez F (2023) Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques. PLOS Negl Trop Dis 17(1):1–13. https://doi.org/10.1371/journal.pntd.0011047
Barrera R, Amador M, Clark GG (2006) Ecological factors influencing aedes aegypti (diptera: Culicidae) productivity in artificial containers in Salinas, Puerto Rico. J Med Entomol 43(3):484–492
Besag J, York J, Mollié A (1991) Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 43(1):1–20. https://doi.org/10.1007/BF00116466
Bivand R, Gómez-Rubio V, Rue H (2015) Spatial data analysis with r-inla with some extensions. Journal of Statistical Software, 63 (20): 1–31, https://doi.org/10.18637/jss.v063.i20
Campbell KM, Haldeman K, Lehnig C, Munayco CV, Halsey ES, Laguna- Torres VA, Scott TW (2015) Weather regulates location, timing, and intensity of dengue virus transmission between humans and mosquitoes. PLoS Negl Trop Dis 9(7):e0003957
Chastel C (2012) Eventual role of asymptomatic cases of dengue for the introduction and spread of dengue viruses in non-endemic regions. Front Physiol 3:70
Desjardins MR, Eastin MD, Paul R, Casas I, Delmelle EM (2020) Space-time conditional autoregressive modeling to estimate neighborhood-level risks for dengue fever in Cali, Colombia. Am J Trop Med Hyg 103(5):2040–2053
Dieng H, Ahmad AH, Mahyoub JA, Turkistani AM, Mesed H, Koshike S et al (2012) Household survey of container-breeding mosquitoes and climatic factors influencing the prevalence of aedes aegypti (diptera: Culicidae) in Makkah city, Saudi arabia. Asian Pac J Trop Biomed 2(11):849–857
Eidson M, Kramer L, Stone W, Hagiwara Y, Schmit K et al (2001) Dead bird surveillance as an early warning system for west nile virus. Emerg Infect Dis 7(4):631
Enfield DB, Mestas-Nuñez AM, Mayer DA, Cid-Serrano L (1999) How ubiquitous is the dipole relationship in tropical atlantic sea surface temperatures? J Geophys Res Oceans 104(C4):7841–7848
European Centre for Disease Prevention and Control (2022). Vbornet-european network for arthropod vector surveillance for human public health. www.vbornet.eu. Accessed Aug 2022
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Michaelsen J (2015) The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci Data 2(1):150066
García YE, Chou-Chen S-W, Barboza LA, Daza–Torres ML, Montesinos-López JC, Vasquez P, et al (2023) Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach. PLOS glob public health 3(10): e0002417. https://doi.org/10.1371/journal.pgph.0002417
Gasparrini A (2011) Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw 43(8):1–20
Gasparrini A (2014) Modeling exposure-lag-response associations with distributed lag non-linear models. Stat Med 33(5):881–899. https://doi.org/10.1002/sim.5963
Gasparrini A, Armstrong B, Kenward MG (2010) Distributed lag non-linear models. Stat Med 29(21):2224–2234. https://doi.org/10.1002/sim.3940
Geiger R (1954) Klassifikation der klimate nach w. köppen. landolt-börnstein zahlenwerte und funktionen aus physik, chemie, astronomie, geophysik und technik, alte serie, vol 3. Springer, Berlin, pp 603–607
Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102(477):359–378. https://doi.org/10.1198/016214506000001437
Gubler DJ (2012) The economic burden of dengue. Am J Trop Med and Hyg 86(5):743
Hidalgo HG, Alfaro EJ, Quesada-Montano B (2017) Observed (1970–1999) climate variability in central america using a high-resolution meteorological dataset with implication to climate change studies. Clim Change 141(1):13–28
Lopez LF, Amaku M, Coutinho FAB, Quam M, Burattini MN, Struchiner CJ, Massad E (2016) Modeling importations and exportations of infectious diseases via travelers. Bullet Math Biol 78(2):185–209
Lowe R, Bailey TC, Stephenson DB, Graham RJ, Coelho CA, Sá Carvalho M, Barcellos C (2011) Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Comput Geosci 37(3):371–381
Massad E, Amaku M, Coutinho FAB, Struchiner CJ, Burattini MN, Khan K, Wilder-Smith A (2018) Estimating the probability of dengue virus introduction and secondary autochthonous cases in Europe. Sci Rep 8(1):1–12
Mateus JC, Carrasquilla G (2011) Predictors of local malaria outbreaks: an approach to the development of an early warning system in Colombia. Memórias Do Instituto Oswaldo Cruz 106:107–113
Medlock JM, Avenell D, Barrass I, Leach S (2006) Analysis of the potential for survival and seasonal activity of aedes albopictus (diptera: Culicidae) in the United Kingdom. J Vector Ecol 31(2):292–304
Messina JP, Brady OJ, Golding N, Kraemer MU, Wint G, Ray SE et al (2019) The current and future global distribution and population at risk of dengue. Nat Microbiol 4(9):1508–1515
Ministerio de Salud (2022a). Sitio web del Ministerio de Salud de Costa rica. Bienvenido.https://www.ministeriodesalud.go.cr/
Ministerio de Salud (2022b). Sitio Web del Ministerio de Salud de costa rica. Bienvenido. https://www.ministeriodesalud.go.cr/index.php/biblioteca-dearchivos- left/documentos-ministerio-de-salud/material-informativo/materialpublicado/ boletines/boletines-vigilancia-vs-enfermedades-de-transmisionvectorial
Morin CW, Comrie AC, Ernst K (2013) Climate and dengue transmission: evidence and implications. Environ Health Perspect 121(11–12):1264–1272
Muñoz E, Poveda G, Arbeláez MP, Vélez ID (2021) Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and enso. Acta Tropica 224:106136
Murray NEA, Quam MB, Wilder-Smith A (2013) Epidemiology of dengue: past, present and future prospects. Clin Epidemiol 5:299
Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S (2014) Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infect Dis 14(1):1–14
NOAA (2022) Climate prediction center. https://www.cpc.ncep.noaa.gov/data/indices/ersst5.nino.mth.91-20.ascii. Accessed: 01 May 2022
Outammassine A, Zouhair S, Loqman S (2022) Global potential distribution of three underappreciated arboviruses vectors (Aedes japonicus, Aedes vexans and Aedes vittatus) under current and future climate conditions. Transbound Emerg Dis 69(4):e1160–e1171
Racloz V, Ramsey R, Tong S, Hu W (2012) Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Neglect Trop Dis 6(5):e1648
Romi R, Severini F, Toma L (2006) Cold acclimation and overwintering of female Aedes albopictus in Roma. J Am Mosq Control Assoc 22(1):149–151
Rue H, Martino S, Chopin N (2009) Approximate bayesian inference for latent Gaussian models by using integrated nested laplace approximations. J Royal Stat Soc Ser B ( Methodol ) 71(2):319–392
Rueda L, Patel K, Axtell R, Stinner R (1990) Temperature-dependent development and survival rates of Culex quinquefasciatus and I (diptera: Culicidae). J Med Entomol 27(5):892–898
Sarfraz MS, Tripathi NK, Tipdecho T, Thongbu T, Kerdthong P, Souris M (2012) Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping. BMC Public Health 12(1):1–19
Tuck SL, Phillips HR, Hintzen RE, Scharlemann JP, Purvis A, Hudson LN (2014) Modistools -downloading and processing modis remotely sensed data in R. Ecol Evol 4(24):4658–4668. https://doi.org/10.1002/ece3.1273
Tun-Lin W, Burkot T, Kay B (2000) 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(1):31–37
Van Benthem BH, Vanwambeke SO, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, Somboon P (2005) Spatial patterns of and risk factors for seropositivity for dengue infection. Am J Trop Med Hyg 72(2):201–208
Vásquez P, Loría A, Sanchez F, Barboza LA (2020) Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: a generalized additive model and random forest approach. Revista de Matematica: Teoria y Aplicaciones 27(1):1–21
Wang H, Zhao S, Wang S, Zheng Y, Wang S, Chen H, Chen Y (2022) Global magnitude of encephalitis burden and its evolving pattern over the past 30 years. J Infect 84(6):777–787
Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A. (1987). Effect of temperature on the vector efficiency of aedes aegypti for dengue 2 virus. Am J Trop Med 36(1):143-152. https://doi.org/10.4269/ajtmh.1987.36.143
Wen T-H, Lin NH, Lin C-H, King C-C, Su M-D (2006) Spatial mapping of temporal risk characteristics to improve environmental health risk identification: a case study of a dengue epidemic in Taiwan. Sci Total Environ 367(2–3):631–640
Winkler RL, Murphy AH (1968) “Good’’ probability assessors. J Appl Meteorol Climatol 7(5):751–758. https://doi.org/10.1175/1520-0450(1968)007.0751:PA.2.0.CO;2
World Health Organization (2022). Dengue and sever dengue. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severedengue. Accessed Aug 2022
Yang X, Quam MB, Zhang T, Sang S (2021) Global burden for dengue and the evolving pattern in the past 30 years. J Travel Med 28(8):146
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
S.W.C. proposed the main conceptual ideas, performed formal analysis, results validations and writing original draft preparation. L.A.B. worked out in data curation, formal analysis, and writing original draft preparation. P.V. helped with the writing original draft preparation and the public health contextualization. Y.E.G. work out in the original writing draft. J.G.C. helped write a review and editing the final version. H.G.H. work out in writing, review and editing. And F.S. supervises the research team and works out in writing, review and editing the final version.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Handling Editor: Luiz Duczmal.
Appendices
Appendix 1
Naïve methods’ predictive metrics of the testing period (from January to March 2021)
Appendix 2
Dengue cases modelling and prediction (see Figs. 8, 9).
Appendix 3
Relative risk prediction maps in 2002, 2011 and 2020 (see Figs. 10, 11, 12).
Appendix 4
Absolute percentage error maps in 2002, 2011 and 2020 (see Figs. 13, 14, 15)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chou-Chen, S.W., Barboza, L.A., Vásquez, P. et al. Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica. Environ Ecol Stat 30, 687–713 (2023). https://doi.org/10.1007/s10651-023-00580-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10651-023-00580-9