Spatial Temporal Dynamics and Risk Zonation of Dengue Fever, Dengue Hemorrhagic Fever, and Dengue Shock Syndrome in Thailand

Full Text (PDF, 931KB), PP.58-68

Views: 0 Downloads: 0


Phaisarn Jeefoo 1,*

1. Geographic Information Science Field of Study, School of Information and Communication Technology, University of Phayao, 19 Moo 2, Mae-Ka, Mueang, Phayao 56000 Thailand

* Corresponding author.


Received: 12 Jun. 2012 / Revised: 15 Jul. 2012 / Accepted: 11 Aug. 2012 / Published: 8 Sep. 2012

Index Terms

Geographic Information System (GIS), Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF), Dengue Shock Syndrome (DSS), Local Spatial Autocorrelation Statistics (LSAS), Kernel-density estimation (KDE)


This study employed geographic information systems (GIS) to analyze the spatial factors related to dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) epidemics. Chachoengsao province, Thailand, was chosen as the study area. This study examines the diffusion pattern of disease. Clinical data including gender and age of patients with disease were analyzed. The hotspot zonation of disease was carried out during the outbreaks for years 2001 and 2007 by using local spatial autocorrelation statistics (LSAS) and kernel-density estimation (KDE) methods. The mean center locations and movement patterns of the disease were found. A risk zone map was generated for the incidence. Data for spatio-temporal analysis and risk zonation of DF/DHF/DSS were employed for years 2000 to 2007. Results found that the age distribution of the cases was different from the general population’s age distribution. Taking into account that the quite high incidence of DF/DHF/DSS cases was in the age group of 13-24 years old and the percentage rate of incidence was 42.9%, a DF/DHF/DSS virus transmission out of village is suspected. An epidemic period of 20 weeks, starting on 1st May and ending on 31st September, was analyzed. Approximately 25% of the cases occurred between Weeks 6-8. A pattern was found using mean centers of the data in critical months, especially during rainy season. Finally, it can be identified that from the total number of villages affected (821), the highest risk zone covered 7 villages (0.85%); the moderate risk zone comprised 39 villages (4.75%); for the low risk zone 22 villages (2.68%) were found; the very low risk zone consisted of 120 villages (14.62%); and no case occurred in 633 villages (77.10%). The zones most at risk were shown in districts Mueang Chachoengsao, Bang Pakong, and Phanom Sarakham. This research presents useful information relating to the DF/DHF/DSS. To analyze the dynamic pattern of DF/DHF/DSS outbreaks, all cases were positioned in space and time by addressing the respective villages. Not only is it applicable in an epidemic, but this methodology is general and can be applied in other application fields such as dengue outbreak or other diseases during natural disasters.

Cite This Paper

Phaisarn Jeefoo, "Spatial Temporal Dynamics and Risk Zonation of Dengue Fever, Dengue Hemorrhagic Fever, and Dengue Shock Syndrome in Thailand", International Journal of Modern Education and Computer Science(IJMECS), vol.4, no.9, pp.58-68, 2012. DOI:10.5815/ijmecs.2012.09.08


[1]M. Fakeeh, A. M. Zaki, “Virologic and serologic surveillance for dengue fever in Jeddah, Saudi Arabia, 1991-1999,” American Journal of Tropical Medicine and Hygiene, vol.65, pp. 764-767, 2001.
[2]K. Nakhapakorn, N. K. Tripathi, “An information value based analysis of physical and climatic factors affecting dengue fever and dengue hemorrhagic fever incidence,” International Journal of Health Geographics, vol.4, no.13, 2005.
[3]M. Derouich, A. Boutayeb, E. H. Twizell, “A model of dengue fever,” Biology Engineering, vol.2, no.4, 2003.
[4]D. J. Gubler, “Dengue and dengue hemorrhagic fever,” Clinical microbiology reviews, vol.11, pp. 480-496, 1998.
[5]World Health Organization (WHO), “Vector control for malaria and other mosquito-borne diseases,” Report of a WHO study group technical report series, Geneva, vol.857, pp. 1-99, 1995.
[6]P. Kittayapong, S. Yoksan, U. Chansang, C. Chansang, “A Bhumiratana, suppression of dengue transmission by application of integrated vector control strategies at sero-positive GIS-Based Foci,” American Journal of Tropical Medicine and Hygiene, vol.78, pp. 70-76, 2008.
[7]B. H. B. Vanbenthem, S. O. Vanwambeke, N. Khantikul, C. Burghoorn-Maas, K. Panart, L. Oskam, E. F. Lambin, P. Somboon, “Spatial patterns of and risk factors for seropositivity for dengue infection,” American Social of Tropical Medicine and Hygiene, vol.72, pp. 201-208, 2005.
[8]P. Barbazan, S. Yoksan, J. P. Gonzalez, “Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics,” Microbes and Infection, vol.4, pp. 699-705, 2002.
[9]M. Ali, Y. Wagatsuma, M. Emch, R. F. Breiman, “Use of a geographic information system for defining spatial risk for dengue transmission in Bangladesh: role for Aedes albopictus in an urban outbreak,” American Society of Tropical Medicine and Hygiene, vol.69, pp. 634-640, 2003.
[10]P. C. Wu, J. G. Lay, H. R. Guo, C. Y. Lin, S. C. Lung, H. J. Su, “Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan,” Science of the total Environment, vol.407, pp. 2224-2233, 2009.
[11]C. Rotela, F. Fouque, M. Lamfri, P. Sabatier, V. Introini, M. Zaidenberg, C. Scavuzzo, “Space-time analysis of the dengue spreading dynamics in the 2004 Tartagel outbreak, Northern Argentina,” Acta Tropica, vol.103, pp. 1-13, 2007.
[12]V. Herbreteau, F. Demoraes, W. Khaungaew, J. P. Hugot, J. P. Gonzalez, P. Kittayapong, M. Souris, “Use of Geographic Information Systems and Remote Sensing for assessing environment influence on Leptospirosis incidence, Phrae province, Thailand,” International Journal of Geographics, vol.2, pp. 43-49, 2006.
[13]A. Mondini, F. Chiaravalloti-Neto, “Spatial correlation of incidence of dengue with socioeconomic, demographic and environmental variables in a Brazilian city,” Science of the Total Environment, vol.393, pp. 241-248, 2008.
[14]A. C. Yost, “Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA,” Ecological Indicators, vol.8, pp. 46-56, 2006.
[15]Y. A. Twumasi, E. C. Merem, “GIS applications in land management: The loss of high quality land to development in central Mississippi from 1987-2002,” International Journal of Environmental Research and Public Health, vol.2, pp. 234-244, 2005.
[16]L. Anselin, Spatial statistical modeling in a GIS environment: GIS, Spatial Analysis, and Modeling-Chapter 5. ESRI Press, California, USA, pp. 93-111, 2005.
[17]United States Department of Agriculture (USDA), West Nile Virus: In equids in the Northeastern United States in 2000. New York, USA, pp. 1-42, 2001.
[18]M. P. Ward, M. Levy, H. L. Thacker, M. Ash, S. L. Norman, G. E. Moore, P. W. Webb, “Investigation of an outbreak of Encephalomyelitis caused by West Nile virus in 136 horses,” Journal of American Veterinary Medical Association, vol.225, pp. 75-84, 2004.
[19]R. Harris, Z. Chen, “Giving dimension to point location: urban density profiling using population surface models,” Computers, Environment and Urban Systems, vol.29, pp. 115-132, 2005.
[20]C. A. Wittich, Spatial analysis of West Nile virus and predictors of hyperendemicity in the Texas equine industry [Thesis]: Texas A&M University, 2007.
[21]G. Hay, K. Kypri, P. Whigham, J. Langley, “Potential biases due to geocoding error in spatial analyses of official data,” Health & Place, vol.15, pp. 562-567, 2008.
[22]J. T. Watson, J. C. Roderick, K. Gibbs, W. Paul, “Dead crow reports and location of human West Nile Virus cases, Chicago, 2002,” Emerging Infectious Diseases, vol.10, pp. 938-940, 2004.
[23]J. S. Brownstein, H. Rosen, D. Prudy, J. R. Miller, M. Merlino, F. Mostashari, D. Fish, “Spatial analysis of West Nile Virus: rapid risk assessment of an introduced vector-borne zoonosis,” Vector Borne and Zoonotic Diseases, vol.2, pp. 101-112, 2002.
[24]E. Liebscher, “Strong convergence of sums of α-mixing random variables with applications to density estimation,” Stochastic Processes and their Applications, vol.65, pp. 69-80, 1996.
[25]H. T. Wist, M. Dag, H. Rue, “Statistical properties of successive wave heights and successive wave periods,” Applied Ocean Research, vol.26, pp. 114-136, 2005.
[26]A. D. Cliff, J. K. Ord, Spatial autocorrelation. Pion London, pp. 178, 1973.
[27]M. F. Goodchild, Spatial autocorrelation. Geo, Norwich, United Kingdom, pp. 56, 1986.
[28]D. A. Griffith, Spatial autocorrelation: A Primer. Research Publication in Geography, Association of American Geographers, Washington D.C., USA, pp. 82, 1987.
[29]T. Warner, M. C. Shank, “Spatial autocorrelation analysis of hyperspectral imagery for feature selection,” Remote Sensing Environment, vol.60, pp. 58-70, 1997.
[30]J. Lee, L. K. Marion, “Analysis if spatial autocorrelation of U.S.G.S 1:250,000 digital elevation models,” American Society for Photogrammetry and Remote Sensing, pp. 504-513, 1994.
[31]P. Haggett, A. D. Cliff, A. Frey, Locational analysis in human geography 2: Locational methods. John Wiley, New York, pp. 605, 1977.
[32]L. S. Premo, “Local spatial autocorrelation statistics quantify multi-scale patterns in distributional data: an example from the Maya Lowlands,” Journal of Archaeological Science, vol.31, pp. 855-866, 2003.
[33]X. Cai, D. Wang, “Spatial autocorrelation of topographic index in catchments,” Journal of Hydrology, vol.328, pp. 581-591, 2006.
[34]A. Getis, J. K. Ord, “The analysis of spatial association by use of distance statistics,” Geographical Analysis, vol.24, pp. 189-206, 1992.
[35]N. A. C. Cressie, Statistics for spatial data. Wiley, New York, pp.900, 1993.
[36]M. A. Wulder, J. C. White, N. C. Coops, “Using local spatial autocorrelation to compare outputs from a forest growth model,” Economical Modeling, vol.209, pp. 264-276, 2007.
[37]B. Flahaut, M. Mouchart, E. S. Martin, I. Thomas, “The local spatial autocorrelation and kernel method for identifying black zones a comparative approach,” Accident Analysis and Prevention, vol.35, pp. 991-1004, 2002.
[38]L. Anselin, Spatial econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 284, 1988.
[39]L. Anselin, S. Rey, “Review-Introduction to the special issue on spatial econometrics,” International Regional Science, vol.20, pp. 1-7, 1997.
[40]R. K. Pace, R. Barry, C. F. Sirmans, “Spatial statistics and real estate,” Journal of Real Estate Finance, vol.17, pp. 5-13, 1998.
[41]J. L. Ping, C. J. Green, R. E. Zartman, K. F. Bronson, “Exploring spatial dependence of cotton yield using global and local autocorrelation statistics,” Field Crops Research, vol.89, pp. 219-236, 2004.
[42]A. K. Yeshiwondim, S. Gopal, A. T. Hailemariam, D. O. Dengela, H. P. Patel, “Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia,” International Journal of Health Geographics, vol.8, pp. 1-11, 2009.
[43]D. J. Gubler, Dengue and dengue hemorrhagic fever; its history and resurgence as a global public health problem. In Dengue and Dengue Hemorrhagic Fever bulletin, pp. 22, 1997.
[44]J. H. Jetten, D. A. Focks, “Changes in the distribution of dengue transmission under climate warming scenarios,” American Journal of Tropical Medicine and Hygiene, vol.57, pp. 285-297, 1997.
[45]World Health Organization (WHO). Dengue hemorrhagic fever: Diagnosis, treatment, prevention and control, 2nd ed.; Geneva, 1997.
[46]J. L. Meza, “Empirical Bayes estimation smoothing of relative risks in disease mapping,” Journal of Statistical Planning and Inference, vol.112, pp. 43-62, 2002.
[47]R. J. Marshall, “Mapping disease and mortality rates using empirical Bayes estimators,” Journal of the Royal Statistical Society: Series C (Applied Statistics). vol.40, pp. 283-94, 1991.
[48]J. K. Ord, A. Getis, “Local spatial autocorrelations statistics: distributional issues and application,” Geographical Analysis, vol.27, pp. 286-306, 1995.
[49]S. Hinman, J. K. Blackburn, A. Curtis, “Spatial and temporal structure of typhoid outbreaks in Washington D.C., 1906-1909: evaluating local clustering with the statistic,” International Journal of Health Geographics, vol.5, pp. 1-13, 2006.
[50]A. Getis, A. C. Morrison, K. Gray, T. W. Scott, “Characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru,” American Journal of Tropical Medicine and Hygiene, vol.69, pp. 494-505, 2003.
[51]J. Wu, J. Wang, B. Meng, G. Chen, L. Pang, X. Song, K. Zhang, T. Zhang, X. Zhang, “Exploratory spatial data analysis for the identification of risk factors to birth defects,” BMC Public Health, vol.4, pp. 1-23, 2004.
[52]F. I. MacKellar, In The Cambridge World History of Human Disease; Kiple, K.F., Ed.; Early mortality data: sources and difficulties of interpretation, Cambride University, pp. 209-213, 1993.
[53]M. P. Ward, T. E. Carpenter, “Techniques for analysis of disease clustering in space and in time in veterinary epidemiology,” Preventive Veterinary Medicine, vol.45, pp. 257-284, 2000.
[54]D. W. S. Wong, J. Lee, Point Pattern Descriptors: Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS. John Wiley & Sons, Inc., Hoboken, New Jersey, USA, pp. 189-192, 2005.
[55]S. Hales, P. Weinstein, A. Woodward, “Dengue fever epidemics in the South Pacific: by El Nino Southern Oscillation,” Lancet, vol.348, pp. 1664-1665, 1996.
[56]J. Keating, “An investigation into the cyclical incidence of dengue fever,” Social Science Medicine, vol.53, pp. 1587-1597, 2001.
[57]D. Guha-Sapir, B. Schimmer, “Review, Dengue fever: new paradigms for a changing epidemiology,” Emerging Themes in Epidemiology, vol.2, no.1, 2005.
[58]K. Nakhapakorn, S. Jirakajohnkool, “Temporal and Spatial Autocorrelation Statistics of Dengue Fever,” Dengue Bulletin, vol.30, pp. 177-183, 2006.
[59]D. O’Sullivan, D. Unwin, D. Point Pattern Analysis: Geographic information Analysis. John Wiley & Sons, Inc., Hoboken, New Jersey, USA, pp. 81-88, 2003.
[60]S. Fotherringham, P. Rogerson, Spatial analysis and GIS. Department of Geography, SUNY at Buffalo, 1994.