Spatio-Temporal Analysis and Mapping of Malaria in Thailand
This paper proposes a GLMM with spatial and
temporal effects for malaria data in Thailand. A Bayesian method is
used for parameter estimation via Gibbs sampling MCMC. A
conditional autoregressive (CAR) model is assumed to present the
spatial effects. The temporal correlation is presented through the
covariance matrix of the random effects. The malaria quarterly data
have been extracted from the Bureau of Epidemiology, Ministry of
Public Health of Thailand. The factors considered are rainfall and
temperature. The result shows that rainfall and temperature are
positively related to the malaria morbidity rate. The posterior means
of the estimated morbidity rates are used to construct the malaria
maps. The top 5 highest morbidity rates (per 100,000 population) are
in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4,
97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82).
According to the DIC criterion, the proposed model has a better
performance than the GLMM with spatial effects but without
temporal terms.
[1] WHO. (2013, Jan 11). "Malaria Situation in SEAR Countries:
Thailand.” Available: http://www.searo.who.int/en/Section10/
Section21/Section340_4027.htm.
[2] I. Kleinschmidt, B. Sharp, I. Mueller, and P. Vounatsou, "Rise in
malaria incidence rates in South Africa: small area spatial analysis of
variation in time trends,” Am J Epidemiol, vol. 155, 2002, pp. 257–264.
[3] R. Carter, K.N. Mendis, and D. Roberts, "Spatial targeting of
interventions against malaria,” Bull WHO, vol. 78, 2000, pp 1401–1411.
[4] D. Le Sueur, F. Binka, C. Lengeler, D. De Savigny, B. Snow, T.
Teuscher, and Y. Toure, " An atlas of malaria in Africa,” Africa Health.
Vol. 19, 1997, pp. 23–24.
[5] R.W. Snow, K. Marsh, D. Le Sueur, "The need for maps of transmission
intensity to guide malaria control in Africa,” Parasitol Today, vol. 12,
1996, pp. 455–457.
[6] R.W. Snow, E. Gouws, and J.A. Omumbo, "Models to predict the
intensity of Plasmodium falciparum transmission: applications to the
burden of disease in Kenya,” Trans R Soc Trop Med Hyg, vol. 92, 1998,
pp. 601-606.
[7] L.R. Beck, M.H. Rodriguez, and S.W. Dister, "Remote sensing as a
land-scape epidemiologic tool to identify villages at high risk for malaria
transmission,” Am J Trop Med Hyg, vol. 51, 1994, pp. 271–80.
[8] L. N. Kazembe, I. Kleinschmidt, T.H. Holtz, and B. L. Sharp, "Spatial
analysis and mapping of malaria risk in Malawi using point-referenced
prevalence of infection data,” Int J Health Geogr, vol. 5, 2006, pp. 41.
[9] Bureau of Epidemiology. (2012, Jan 20). "Table of notifiable diseases,”
Available: http://www.boe.moph.go.th/Annual/AESR2012/index.html.
[10] K. Lekdee and L. Ingsrisawang, "Risk factors for malaria in Thailand
using generalized estimating equations (GEE) and genalized linear
mixed model (GLMM),” Journal of Health Science, vol. 19, 2010, pp.
364-373.
[11] K. Lekdee, S. Sammatat, N. Boonsit, and L. Ingsrisawang, "Spatial
Analysis and Malaria Mapping in Thailand,” The World Congress on
Engineering and Technology (CET2011), Oct. 28 to Nov. 2, 2011,
Shanghai, China,pp.445-448.
[12] J. Besag, J.York, and A. Mollie, "Bayesian image restoration, with two
applications in spatialstatistics,” Ann. Inst. Stat. Mat, vol. 43, 1991, pp.
1-59.
[13] P. Congdon, Bayesian Statistical Modelling, 2nd ed., John Wiley &
Sons: New York, pp. 1-56, 2006.
[14] TMD. (2011, May 10). "Weather.” Available: URL:
http://www.tmd.go.th/en/.
[15] The BUGS Project. (2011, Jan 30). Available:
http://www.mrcbsu.com.ac.uk .
[16] D. Spiegelhalter, A. Thomas, N. Best, and D. Lunn, D. WinBUGS
Version 1.4 User Manual, MRC Biostatistics Unit, Institute of
PublicHealth: London, pp. 15, 2003.
[17] M. LH, Mabaso, P.Vounatsou, S. Midzi, J. Da Silva, and T. Smith,
"Spatio-temporal analysis of the role of climate in inter-annual variation
of malaria incidence in Zimbabwe,” International Journal of Health
Geographics, 2006, pp. 5-20.
[18] W.R.Tobler, "A computer movie simulating urban growth in the Detroit
region,” Economic Geography, vol. 46, 1970, pp. 234-240.
[1] WHO. (2013, Jan 11). "Malaria Situation in SEAR Countries:
Thailand.” Available: http://www.searo.who.int/en/Section10/
Section21/Section340_4027.htm.
[2] I. Kleinschmidt, B. Sharp, I. Mueller, and P. Vounatsou, "Rise in
malaria incidence rates in South Africa: small area spatial analysis of
variation in time trends,” Am J Epidemiol, vol. 155, 2002, pp. 257–264.
[3] R. Carter, K.N. Mendis, and D. Roberts, "Spatial targeting of
interventions against malaria,” Bull WHO, vol. 78, 2000, pp 1401–1411.
[4] D. Le Sueur, F. Binka, C. Lengeler, D. De Savigny, B. Snow, T.
Teuscher, and Y. Toure, " An atlas of malaria in Africa,” Africa Health.
Vol. 19, 1997, pp. 23–24.
[5] R.W. Snow, K. Marsh, D. Le Sueur, "The need for maps of transmission
intensity to guide malaria control in Africa,” Parasitol Today, vol. 12,
1996, pp. 455–457.
[6] R.W. Snow, E. Gouws, and J.A. Omumbo, "Models to predict the
intensity of Plasmodium falciparum transmission: applications to the
burden of disease in Kenya,” Trans R Soc Trop Med Hyg, vol. 92, 1998,
pp. 601-606.
[7] L.R. Beck, M.H. Rodriguez, and S.W. Dister, "Remote sensing as a
land-scape epidemiologic tool to identify villages at high risk for malaria
transmission,” Am J Trop Med Hyg, vol. 51, 1994, pp. 271–80.
[8] L. N. Kazembe, I. Kleinschmidt, T.H. Holtz, and B. L. Sharp, "Spatial
analysis and mapping of malaria risk in Malawi using point-referenced
prevalence of infection data,” Int J Health Geogr, vol. 5, 2006, pp. 41.
[9] Bureau of Epidemiology. (2012, Jan 20). "Table of notifiable diseases,”
Available: http://www.boe.moph.go.th/Annual/AESR2012/index.html.
[10] K. Lekdee and L. Ingsrisawang, "Risk factors for malaria in Thailand
using generalized estimating equations (GEE) and genalized linear
mixed model (GLMM),” Journal of Health Science, vol. 19, 2010, pp.
364-373.
[11] K. Lekdee, S. Sammatat, N. Boonsit, and L. Ingsrisawang, "Spatial
Analysis and Malaria Mapping in Thailand,” The World Congress on
Engineering and Technology (CET2011), Oct. 28 to Nov. 2, 2011,
Shanghai, China,pp.445-448.
[12] J. Besag, J.York, and A. Mollie, "Bayesian image restoration, with two
applications in spatialstatistics,” Ann. Inst. Stat. Mat, vol. 43, 1991, pp.
1-59.
[13] P. Congdon, Bayesian Statistical Modelling, 2nd ed., John Wiley &
Sons: New York, pp. 1-56, 2006.
[14] TMD. (2011, May 10). "Weather.” Available: URL:
http://www.tmd.go.th/en/.
[15] The BUGS Project. (2011, Jan 30). Available:
http://www.mrcbsu.com.ac.uk .
[16] D. Spiegelhalter, A. Thomas, N. Best, and D. Lunn, D. WinBUGS
Version 1.4 User Manual, MRC Biostatistics Unit, Institute of
PublicHealth: London, pp. 15, 2003.
[17] M. LH, Mabaso, P.Vounatsou, S. Midzi, J. Da Silva, and T. Smith,
"Spatio-temporal analysis of the role of climate in inter-annual variation
of malaria incidence in Zimbabwe,” International Journal of Health
Geographics, 2006, pp. 5-20.
[18] W.R.Tobler, "A computer movie simulating urban growth in the Detroit
region,” Economic Geography, vol. 46, 1970, pp. 234-240.
@article{"International Journal of Medical, Medicine and Health Sciences:67724", author = "Krisada Lekdee and Sunee Sammatat and Nittaya Boonsit", title = "Spatio-Temporal Analysis and Mapping of Malaria in Thailand", abstract = "This paper proposes a GLMM with spatial and
temporal effects for malaria data in Thailand. A Bayesian method is
used for parameter estimation via Gibbs sampling MCMC. A
conditional autoregressive (CAR) model is assumed to present the
spatial effects. The temporal correlation is presented through the
covariance matrix of the random effects. The malaria quarterly data
have been extracted from the Bureau of Epidemiology, Ministry of
Public Health of Thailand. The factors considered are rainfall and
temperature. The result shows that rainfall and temperature are
positively related to the malaria morbidity rate. The posterior means
of the estimated morbidity rates are used to construct the malaria
maps. The top 5 highest morbidity rates (per 100,000 population) are
in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4,
97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82).
According to the DIC criterion, the proposed model has a better
performance than the GLMM with spatial effects but without
temporal terms.
", keywords = "Bayesian method, generalized linear mixed model
(GLMM), malaria, spatial effects, temporal correlation.", volume = "8", number = "7", pages = "390-5", }
