DEVELOPMENT OF SPATIAL SURVEILLANCE MODELS FOR DENGUE FEVER AND DENGUE HAEMORRHAGIC FEVER PREVENTION AND CONTROL
Keywords:
DF/DHF, surveillance modelsAbstract
The purpose of the study is to develop spatial models for surveillance of epidemics of dengue fever and dengue haemorrhagic fever (DF/DHF). Seasonal data of each sub-district in Ubon Ratchathani Province of Thailand related to the epidemics including rain, temperature, House Index (HI), Container Index (CI), and Breteau Index (BI) during 2001-2005 were used for analysis through mathematical equations. Epidemic probabilities related to temperature (Pt), rain (Pr), and larvalindex (I) were analyzed seasonally. These parameters were used as dependent variables in the regression analysis for the case prediction (y). The predicted cases were further transformed to be probabilities of occurrence (P) by logistic regression. The Delphi technique was employed to classify the probability of occurrences at 3 levels of risk: high, moderate, and low. An error matrix was used to verify the levels of predicted risk by spatially comparing them with data of the actual risk classified by the conventional method. From 18 seasons in those years, it revealed that 60%overall accuracy was achieved. The model outputs can be used as basic data for short-term and long-term planning in resources management and surveillance network operation.
References
Abraham, B. and Ledolter, J. (1983). Statistical Methods for Forecasting. 3rd edition. John Wiley and Sons, Hoboken, NJ, USA, 562p.
Ali, M., Wagatsuma, Y., Emch, M., and Breiman, R.F. (2003). Use of a geographic information system for defining spatial risk for dengue transmission in Bangladesh: role of Aedes albopictus in an urban outbreak. Am. J. Trop. Med. Hyg., 69:634-640.
Alpana, B. and Haja, A. (2001). Application of GIS in modeling of dengue risk on socio-cultural data: case of Jalor, Rajasthan, India. Available from: http://www.geog.ucl.ac.uk/~pdensham/SDSS/s_t_paper.html. Accessed date: March 22, 2006.
Banai-Kashani, R.A. (1989). New method for site suitability analysis: the analytic hierarchy process. Environ. Manage., 13:685-693.
Barbazan, P., Yoksan, S., and Gonzalez, J.P. (2002). Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics. Microbes Infect., 4(7):699-705.
Bohra, A. (2001). Prediction modeling of dengue risk based on socio cultural and environmental factors using GIS: case of Jalor town, Rajasthan, India, [PhD thesis]. School of Advanced Technologies, Asian Institute of Technology. Pathumthani, Thailand, p.16-31.
Boonmaging, S. (2004). Urban ecology and dengue hemorrhogic fever a community study in Bangkok, [M.S. thesis]. University of Mahidol, Nakhon Pathom, Thailand, p.4-15.
Borke, M.M. and Fisher, D.G. (1998). Solution algorithms for fuzzy Relational equations with max-product composition. Fuzzy Set. Syst., 94:61-69.
Brown, A.W.A. (1974). World wide surveillance of Aedes aegypti. Proceedings of Annual Conference California Mosquito Control Association; January 20-23; NY, USA, p. 20-25.
Brown, A.W.A. (1977). Yellow fever, dengue and dengue haemorrhagic fever. In: Howe, G.M. (ed). A World Geography of Human Diseases. Academic Press, London, UK, p. 271-316.
Chareonsook, O., Foy, H.M., Teeraratkul, A., and Silarug, N. (1999). Changing epidemiology of dengue hemorrhagic fever in Thailand. Epidemiol. Infect., 122(1):161-66.
Day, J. and Curtis, A. (1989). Influence of rainfall on culexnigripalpus (Ditera: Culicidae) blood-feeding behavior in Indian River County, Florida. Ann.Entomol. Soc. Am., 82:32-37.
Eamchan, P., Nisalak, A., and Foy, H.J. and Charoensook, O.A. (1989). Epidemiology and control of dengue virus infections in Thai villages in 1987. Am. J. Trop. Med. Hyg., 41(1):95-101.
Focks, D.A. (2003). A review of entomological sampling methods and Indicators for dengue vectors. WHO, Geneva. Available from: http://www.who.int/tdr/ publications/publications/pdf/dengue_review.pdf. Accessed date: March 24, 2006.
Focks, D.A., Daniels, E., and Haile, D.G. (1995). A simulation model of the epidemiology of urban dengue fever: literature analysis, model deve-lopment, preliminary validation, and samples of simulation results. Am. J. Trop. Med. Hyg., 53(5):489-506.
Githeko, A., Lindsay, S., Confalonieri, U., and Patz, J. (2000). Climate change and vector–borne diseases: a regional analysis. B. World Health Organ., 78:1136-1145.
Goodman, C. (1987). The Delphi technique: a critique. J. Adv. Nurs., 12:729-734.
Gratz, N.G. (1993). Lessons of aedes aegypti control in Thailand. Med. Vet. Entomol., 7(1):1-10.
Gratz, N.G. (1999). Emerging and resurging vector-borne disease. Annu. Rev. Entomol., 44:51-75.
Gubler, D.J. (1998). The global pandemic of dengue/dengue haemorrhagic fever: current status and prospects for the future. Ann. Acad. Med. Singap., 27:227-234.
Hasson, F., Keeney, S., and McKenna, H. (2000). Research guidelines for the Delphi survey technique. J. Adv. Nurs., 32:1008-1015.
Jetten, T.H. and Focks, D. (1997). Potential changes in the distribution of dengue transmission under climate warming. Am. J. Trop. Med. Hyg., 57(3):285-297.
Kang, F.V. (1998). Estimating generalized ordered logic models. Stata Technical Bulletin, 44:27-30.
Kittayapong, P. and Strickman, D. (1993a). Distri-bution of container-inhabiting Aedes larvae (Diptera: Culicidae) at a dengue focus in Thailand. J. Med. Entomol., 30:601-606.
Kittayapong, P. and Strickman, D. (1993b). Three simple devices for preventing development of Aedes aegypti larvae in water jars. Am. J. Trop. Med. Hyg., 49:158-165.
Koopman, J.S., Prevots, D.R., Marin, M.A.V., Dantes, H.G., Aquino, M.L.Z., Longini, J.I.M., and Amer, J.S. (1991). Determinants and predictors of dengue infection in Mexico. Am. J. Epidemiol., 133:1168-1178.
Malczewski, J. (2000). On the use of weighted linear combination method in GIS: common and best practice approaches. Transaction in GIS, 4:5-22.
Martens, W.J.M., Jetten, T.H., and Focks, D.A. (1997). Sensitivity of malaria, schistosomiasis and dengue to global warming. Climatic Change, 35:145-156.
McCullagh, P. (1980). Regression models for ordinal data (with discussion). J. R. Stat. Soc. B, 42:109-112.
Muttitanon, W., Kongthong, P., Kongkanon, C., Yoksan, S., Nitatpattana, N., Gonzales, J.P., and Barbazan, P. (2004). Spatial and temporal dynamics of Dengue Hemorrhagic Fever epidemics, Nakhon Pathom province, Thailand. Dengue Bulletin, 28:35-43.
Nakhapakorn, K. (2005). An information value analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. Int. J. Health Geogr., 4:13.
Patz, J.A., Epstein, P.R., Burke, T.A., and Balbus, J.M. (1996). Global climate change and emerging infectious diseases. J. Amer. Med. Assoc., 275 (3): 217-223.
Pumplin, J. and Stump, D.R. (2001). Multivariate fitting and the error matrix in global analysis of data. Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA, p.7-12.
Saaty, T.L. (1997). The Analytic Hierarchy Process. McGraw-Hill, NY, USA, 287p.
Strickman, D. and Kittayapong, P. (2002). Dengue and its vectors in Thailand: introduction to the study and seasonal distribution of Aedes larvae. Am. J. Trop. Med. Hyg., 67:247-259.
Ubon Ratchathani Provincal Office of Public Health, (UOPH). (2006). Department of Communicable Disease Control, Ubon Ratchathani, Thailand.
Ungchusak, K. and Kunasol, P. (1988). Dengue haemorrhagic fever in Thailand, Southeast. Asian J. Trop. Med. Public Health, 19(3):487-490.
Watts, D.M., Burke, D.S., Harrison, B.A., Whitmire, R.E., and Nisalak, A. (1987). Effect of temperature on the vector efficiency of Aedes aegypti for dengue virus. Am. J. Trop. Med. Hyg., 36:143-152.
World Health Organization, (1997). Dengue hae-morrhagic fever: Diagnosis, treatment, prevention and control. Geneva 2nd ed. World Health Organization, Geneva, Switzerland, p.14-30
