{"title":"Spatio-Temporal Analysis and Mapping of Malaria in Thailand","authors":"Krisada Lekdee, Sunee Sammatat, Nittaya Boonsit","volume":91,"journal":"International Journal of Industrial and Manufacturing Engineering","pagesStart":393,"pagesEnd":398,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9998856","abstract":"
This paper proposes a GLMM with spatial and
\r\ntemporal effects for malaria data in Thailand. A Bayesian method is
\r\nused for parameter estimation via Gibbs sampling MCMC. A
\r\nconditional autoregressive (CAR) model is assumed to present the
\r\nspatial effects. The temporal correlation is presented through the
\r\ncovariance matrix of the random effects. The malaria quarterly data
\r\nhave been extracted from the Bureau of Epidemiology, Ministry of
\r\nPublic Health of Thailand. The factors considered are rainfall and
\r\ntemperature. The result shows that rainfall and temperature are
\r\npositively related to the malaria morbidity rate. The posterior means
\r\nof the estimated morbidity rates are used to construct the malaria
\r\nmaps. The top 5 highest morbidity rates (per 100,000 population) are
\r\nin Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4,
\r\n97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82).
\r\nAccording to the DIC criterion, the proposed model has a better
\r\nperformance than the GLMM with spatial effects but without
\r\ntemporal terms.<\/p>\r\n","references":"[1] WHO. (2013, Jan 11). \"Malaria Situation in SEAR Countries:\r\nThailand.\u201d Available: http:\/\/www.searo.who.int\/en\/Section10\/\r\nSection21\/Section340_4027.htm.\r\n[2] I. Kleinschmidt, B. Sharp, I. Mueller, and P. Vounatsou, \"Rise in\r\nmalaria incidence rates in South Africa: small area spatial analysis of\r\nvariation in time trends,\u201d Am J Epidemiol, vol. 155, 2002, pp. 257\u2013264.\r\n[3] R. Carter, K.N. Mendis, and D. Roberts, \"Spatial targeting of\r\ninterventions against malaria,\u201d Bull WHO, vol. 78, 2000, pp 1401\u20131411.\r\n[4] D. Le Sueur, F. Binka, C. Lengeler, D. De Savigny, B. Snow, T.\r\nTeuscher, and Y. Toure, \" An atlas of malaria in Africa,\u201d Africa Health.\r\nVol. 19, 1997, pp. 23\u201324.\r\n[5] R.W. Snow, K. Marsh, D. Le Sueur, \"The need for maps of transmission\r\nintensity to guide malaria control in Africa,\u201d Parasitol Today, vol. 12,\r\n1996, pp. 455\u2013457.\r\n[6] R.W. Snow, E. Gouws, and J.A. Omumbo, \"Models to predict the\r\nintensity of Plasmodium falciparum transmission: applications to the\r\nburden of disease in Kenya,\u201d Trans R Soc Trop Med Hyg, vol. 92, 1998,\r\npp. 601-606.\r\n[7] L.R. Beck, M.H. Rodriguez, and S.W. Dister, \"Remote sensing as a\r\nland-scape epidemiologic tool to identify villages at high risk for malaria\r\ntransmission,\u201d Am J Trop Med Hyg, vol. 51, 1994, pp. 271\u201380.\r\n[8] L. N. Kazembe, I. Kleinschmidt, T.H. Holtz, and B. L. Sharp, \"Spatial\r\nanalysis and mapping of malaria risk in Malawi using point-referenced\r\nprevalence of infection data,\u201d Int J Health Geogr, vol. 5, 2006, pp. 41.\r\n[9] Bureau of Epidemiology. (2012, Jan 20). \"Table of notifiable diseases,\u201d\r\nAvailable: http:\/\/www.boe.moph.go.th\/Annual\/AESR2012\/index.html.\r\n[10] K. Lekdee and L. Ingsrisawang, \"Risk factors for malaria in Thailand\r\nusing generalized estimating equations (GEE) and genalized linear\r\nmixed model (GLMM),\u201d Journal of Health Science, vol. 19, 2010, pp.\r\n364-373.\r\n[11] K. Lekdee, S. Sammatat, N. Boonsit, and L. Ingsrisawang, \"Spatial\r\nAnalysis and Malaria Mapping in Thailand,\u201d The World Congress on\r\nEngineering and Technology (CET2011), Oct. 28 to Nov. 2, 2011,\r\nShanghai, China,pp.445-448.\r\n[12] J. Besag, J.York, and A. Mollie, \"Bayesian image restoration, with two\r\napplications in spatialstatistics,\u201d Ann. Inst. Stat. Mat, vol. 43, 1991, pp.\r\n1-59.\r\n[13] P. Congdon, Bayesian Statistical Modelling, 2nd ed., John Wiley &\r\nSons: New York, pp. 1-56, 2006.\r\n[14] TMD. (2011, May 10). \"Weather.\u201d Available: URL:\r\nhttp:\/\/www.tmd.go.th\/en\/.\r\n[15] The BUGS Project. (2011, Jan 30). Available:\r\nhttp:\/\/www.mrcbsu.com.ac.uk .\r\n[16] D. Spiegelhalter, A. Thomas, N. Best, and D. Lunn, D. WinBUGS\r\nVersion 1.4 User Manual, MRC Biostatistics Unit, Institute of\r\nPublicHealth: London, pp. 15, 2003.\r\n[17] M. LH, Mabaso, P.Vounatsou, S. Midzi, J. Da Silva, and T. Smith,\r\n\"Spatio-temporal analysis of the role of climate in inter-annual variation\r\nof malaria incidence in Zimbabwe,\u201d International Journal of Health\r\nGeographics, 2006, pp. 5-20.\r\n[18] W.R.Tobler, \"A computer movie simulating urban growth in the Detroit\r\nregion,\u201d Economic Geography, vol. 46, 1970, pp. 234-240.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 91, 2014"}