- Presumed population immunity to SARS-CoV-2 in South Korea, April 2022
-
Eun Jung Jang, Young June Choe, Seung Ah Choe, Yoo-Yeon Kim, Ryu Kyung Kim, Jia Kim, Do Sang Lim, Ju Hee Lee, Seonju Yi, Sangwon Lee, Young-Joon Park
-
Osong Public Health Res Perspect. 2022;13(5):377-381. Published online October 14, 2022
-
DOI: https://doi.org/10.24171/j.phrp.2022.0209
-
-
Graphical Abstract
Abstract
PDF

- Objectives
We estimated the overall and age-specific percentages of the Korean population
with presumed immunity against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) as of April 2022 using the national registry.
Methods We used the national coronavirus disease 2019 (COVID-19) infection and vaccination
registry from South Korea, as described to define individuals with a previous history of
COVID-19 infection, vaccination, or both, as persons with presumed immunity.
Results Of a total of 53,304,627 observed persons, 24.4% had vaccination and infection, 58.1%
had vaccination and no infection, 7.6% had infection and no vaccination, and 9.9% had no
immunity. The SARS-CoV-2 Omicron variant emerged at a time when the presumed population
immunity ranged from 80% to 85%; however, nearly half of the children were presumed to
have no immunity.
Conclusion We report a gap in population immunity, with lower presumed protection in
children than in adults. The approach presented in this work can provide valuable informed
tools to assist vaccine policy-making at a national level.
- Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
-
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
-
Osong Public Health Res Perspect. 2011;2(2):75-82. Published online June 30, 2011
-
DOI: https://doi.org/10.1016/j.phrp.2011.07.005
-
-
2,141
View
-
15
Download
-
11
Citations
-
Abstract
PDF Supplementary Material
- Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models. Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor. Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best. Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.
-
Citations
Citations to this article as recorded by 
- Population stratification in type 2 diabetes mellitus: A systematic review
Sam Hodgson, Sukhmani Cheema, Zareena Rana, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller Diabetic Medicine.2022;[Epub] CrossRef - The Prediction of Diabetes
Lalit Kumar, Prashant Johri International Journal of Reliable and Quality E-He.2022; 11(1): 1. CrossRef - Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
Mohammad Farhan Khan, Gazal Kalyan, Sohom Chakrabarty, M. Mursaleen Nutrients.2022; 14(14): 2794. CrossRef - Supervised and unsupervised algorithms for bioinformatics and data science
Ayesha Sohail, Fatima Arif Progress in Biophysics and Molecular Biology.2020; 151: 14. CrossRef - Medical Internet of things using machine learning algorithms for lung cancer detection
Kanchan Pradhan, Priyanka Chawla Journal of Management Analytics.2020; 7(4): 591. CrossRef - Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
Stephanie Andraos, Melissa Wake, Richard Saffery, David Burgner, Martin Kussmann, Justin O'Sullivan Advances in Nutrition.2019; 10(6): 944. CrossRef - Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy Computers in Biology and Medicine.2018; 103: 109. CrossRef - Machine Learning and Data Mining Methods in Diabetes Research
Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda Computational and Structural Biotechnology Journal.2017; 15: 104. CrossRef - Survey on clinical prediction models for diabetes prediction
N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao Journal of Big Data.2017;[Epub] CrossRef - Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728. CrossRef - Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim Osong Public Health and Research Perspectives.2015; 6(4): 224. CrossRef
|