Abstract
This research focuses on the development of a computational data-driven modeling method to be used in the dengue outbreak surveillance system. The outbreak-level forecasting is based on the estimation of dengue fever cases in Thailand using both statistical and data mining techniques. Major statistical techniques used in this research are linear regression and generalized linear model. The data mining algorithms used in our study are chi-squared automatic interaction detection (CHAID), classification and regression tree, artificial neural network, and support vector machine. The input data are from four sources, which are remotely sensed indices from the NOAA satellite to represent vegetation health and other related weather conditions, rainfall, the oceanic Niño index (ONI) for justifying climate variability affecting amount of rainfall, and historical dengue cases in Thailand to be used as the modeling target. In the modeling process, these data are lagged from 1 up to 24 months to observe time-series effect. On comparing performances of models built from different algorithms, we found that CHAID is the best one yielding the least error on estimating dengue cases. From the CHAID models to forecast dengue cases in Bangkok metropolitan and Nakhon Ratchasima in the northeast of Thailand, the high level of ONI is the most important factor. The large amount of rainfall is significant factor contributing to dengue outbreak in Chiang Mai in the north and Songkhla in the south.
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References
Cogan, J.E.: Dengue and severe dengue. Fact sheets, World Health Organization (2018). http://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
Department of Disease Control, Ministry of Public Health: Dengue Surveillance Report (2018). http://www.thaivbd.org/n/home
Dom, N.C., Ahmad, A.H., Latif, Z.A., Ismail, R., Pradhan, B.: Coupling of remote sensing data and environmental related parameters for dengue transmission risk assessment in Subang Jaya, Malaysia. Geocarto Int. 28(3), 258–272 (2013)
Hii, Y., Rocklov, J., Ng, N., Tang, C., Pang, F., Sauerborn, R.: Climate variability and increase in intensity and magnitude of dengue incidence in Singapore. Glob. Health Action 2, 124–132 (2009)
Loh, B., Song, R.J.: Modeling dengue cluster size as a function of Aedes aegypti population and climate in Singapore. Dengue Bull. 25, 74–78 (2001)
Su, G.L.S.: Correlation of climatic factors and dengue incidence in Metro Manila, Philippines. AMBIO: J. Hum. Environ. 37(4), 292–294 (2008)
Hasan, T., Bambrick, H.: The effects of climate variables on the outbreak of dengue in Queensland 2008–2009. Southeast Asian J. Trop. Med. Public Health 44(4), 613–622 (2013)
Khormi, H.M., Kumar, L.: Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Sci. Total Environ. 409, 4713–4719 (2011)
Moreno-Madrinan, M.J., et al.: Correlating remote sensing data with the abundance of pupae of the dengue virus mosquito vector, Aedes aegypti, in Central Mexico. ISPRS Int. J. Geo-Inf. 3, 732–749 (2014)
Qi, X., et al.: The effects of socioeconomic and environmental factors on the incidence of dengue fever in the pearl river delta, China 2013. PLoS Neglected Trop. Dis. 9(10), e0004159 (2015)
Chaikoolvatana, A., Singhasivanon, P., Haddawy, P.: Utilization of a geographical information system for surveillance of Aedes aegypti and dengue haemorrhagic fever in north-eastern Thailand. Dengue Bull. 31, 75–82 (2007)
Tipayamongkholgul, M., Fang, C., Klinchan, S., Liu, C., King, C.: Effects of the El Nino-Southern oscillation and dengue epidemics in Thailand, 1996–2005. BMC Publ. Health 9, 1–15 (2009). article 422
Kiang, R.K., Soebiyanto, R.P.: Mapping the risks of malaria, dengue and influenza using satellite data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIX-BB, pp. 83–86 (2012)
Nakhapakorn, K., Tripathi, N.K.: An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. Int. J. Health Geographics 4, 1–13 (2005)
Nitatpattana, N., et al.: Potential association of dengue haemorrhagic fever incidence and remote senses land surface temperature, Thailand, 1998. Southeast Asian J. Trop. Med. Public Health 38(3), 427–433 (2007)
Sithiprasasna, R., Linthicum, K.L., Lerdthusnee, K., Brewer, T.G.: Use of geographical information system to study the epidemiology of dengue haemorrhagic fever in Thailand. Dengue Bull. 21, 68–73 (1997)
Kerdprasop, K., Kerdprasop, N.: Rainfall estimation models induced from ground station and satellite data. In: 24th International MultiConference of Engineers and Computer Scientists, pp. 297–302 (2016)
NOAA: Climate Data Online. National Centers for Environmental Information, NOAA, U.S.A. (2018). https://www.ncdc.noaa.gov/cdo-web/
Kogan, F.: Operational space technology for global vegetation assessment. Bull. Am. Meteorol. Soc. 82(9), 1949–1964 (2001)
Kogan, F.: 30-year land surface trend from AVHRR-based global vegetation health data. In: Kogan, F. et al. (eds.) Use of Satellite and In-situ Data to Improve Sustainability, pp. 119–123. Springer, Dordrecht (2011). https://doi.org/10.1007/978-90-481-9618-0_14
Kogan, F., Guo, W.: Early detection and monitoring droughts from NOAA environmental satellites. In: Kogan, F. et al. (eds.) Use of Satellite and In-situ Data to Improve Sustainability, pp. 11–18. Springer, Dordrecht (2011). https://doi.org/10.1007/978-90-481-9618-0_2
NOAA STAR: Global Vegetation Health Products. Center for Satellite Applications and Research, NOAA, U.S.A. (2018). https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browseByCountry_province.php
Kageyama, Y., Sato, I., Nishida, M.: Automatic classification algorithm for NOAA-AVHRR data using mixels. In: 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 2040–2043 (2007)
Li, C., et al.: Post calibration of channels 1 and 2 of long-term AVHRR data record based on SeaWiFS data and pseudo-invariant targets. Remote Sens. Environ. 150, 104–119 (2014)
NOAA National Weather Service: Cold & Warm Episodes by Season. Climate Prediction Center, NOAA, U.S.A. (2018). http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
Huang, B., et al.: Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgraded, validations, and intercomparisons. J. Clim. 30(20), 8179–8205 (2017)
Bureau of Epidemiology: Dengue Fever. Department of Disease Control, Ministry of Public Health, Thailand (2018). http://www.boe.moph.go.th/boedb/surdata/disease.php?ds=66
Null, J.: El Niño and La Niña Years and Intensities Based on Oceanic Niño Index (ONI). Golden Gate Weather Services (2018). https://ggweather.com/enso/oni.htm/
Acknowledgment
This work was financially supported by grants from the Thailand Toray Science Foundation, the National Research Council of Thailand, and Suranaree University of Technology through the funding of the Data and Knowledge Engineering Research Unit.
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Kerdprasop, K., Kerdprasop, N., Chansilp, K., Chuaybamroong, P. (2019). The Use of Spaceborne and Oceanic Sensors to Model Dengue Incidence in the Outbreak Surveillance System. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_33
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DOI: https://doi.org/10.1007/978-3-030-24289-3_33
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