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HOME > Osong Public Health Res Perspect > Volume 3(4); 2012 > Article
Articles A New Statistical Approach to Analyze Plasmodium vivax Malaria Endemic in Korea
Hae-Wol Cho, Chaeshin Chu
Osong Public Health and Research Perspectives 2012;3(4):191-191.
DOI: https://doi.org/10.1016/j.phrp.2012.11.004
Published online: November 30, 2012
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aOsong Osong Public Health and Research Perspectives, Osong, Korea, College of Health Industry, Eulji University, Seongnam, Korea, College of Medicine, Eulji University, Daejeon, Korea

bOsong Osong Public Health and Research Perspectives, Osong, Korea

Copyright ©2012, Korea Centers for Disease Control and Prevention

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

After more than 10 years of absence in Korea, Plasmodium vivax malaria re-emerged in 1993 near the Korean demilitarized zone. Since then it has been a major public health concern in the Republic of Korea. Although the mosquitoes from North Korea were suspected to be the main cause of the re-emerging infection, the subject is a matter of controversy, because the prevalence of malaria in North Korea was reported only in 1997–1998 [1]. While it was initially suggested that the incidence rate will decrease soon [2], it has not decreased rapidly and there was a fluctuation of incidence rate even after 10 years owing to the fluctuation of the malarial incidence in North Korea [3].
In this issue Noh and co-workers have estimated the annual pattern of malarial incidence by age, gender, and year in Korea using the data from the last 12 years (2001–2011) by adopting the hierarchical generalized linear model (HGLM) [4]. Spatial and temporal correlations have been estimated and the best model has been selected out of nine models. Results were presented as diseases map according to age and gender. In the model, spatial and temporal correlations were also considered for the best estimation. Results of estimated incidence were represented as diseases map [5]. To draw a diseasesmap by estimating the incidence rate fromsmall regions, empirical Bayesian and hierarchical Bayesian estimation methods have been used. Bayesian approach requires assumption of previous parameters, but HGLM approach presents hierarchical likelihood so that there are no sensitivity problems of parameter estimation that may occur in the case of wrong assumption of previous parameters [5]. The advantage of usingHGLMinstead of Bayesian approaches is to avoid complicated calculation process such as Gibbs sampling procedure for parameter estimation. The authors have developed R package for estimation [6].
The analysis well addresses the following question: Does the malarial incidence of one region in a year correlates with that of adjacent regions and the previous year? The study also clearly specifies the characteristics of malarial incidence by age and gender. Finally, the spatial and temporal correlations of malarial incidence are presented using the HGLM model.
The results of the study suggest that it is possible to predict malarial transmission according to time and geographic location, and it has also provided an analytical basis to define strategy and identify countermeasures for preventing malaria in Korea.
  • 1. Gho W-G. . Reemergence of malaria in Korea. J Korean Med Assoc 11;2007;50(11): 959−66.Article
  • 2. Lee JS Lee WJ Cho SH et al.. Outbreak of vivax malaria in areas adjacent to the demilitarized zone, South Korea, 1998. Am J Trop Med Hyg 1;2002;66(1): 13−7. PMID: 12135260.ArticlePubMed
  • 3. Park JW Jun G Yeom JS . Plasmodium vivax malaria: status in the Republic of Korea following reemergence. Korean J Parasitol 10;2009;47(Suppl.): S39−50. PMID: 19885334.ArticlePubMedPMC
  • 4. Noh M Lee Y Oh S et al.. Spatial and temporal distribution of Plasmodium vivax malaria in Korea estimated with hierarchical generalized linear model. Osong Public Health Res Perspect 12;2012;3(4): 192−8.ArticlePubMedPMC
  • 5. Lee Y Jang M Lee W . Prediction interval for diseasemapping using hierarchical likelihood. Comput Stat 3;2011;26(1): 159−79.Article
  • 6. Noh M Lee Y . dhglm: Double Hierarchical Generalized Linear Models. R package version 1.0 (Internet). The R Foundation for Statistical Computing. Vienna, 2012, Available from http://. (Accessed 01.11.2012).

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