The characteristics of COVID-19 have evolved at an accelerated rate over the last two years since the first SARS-CoV-2 case was discovered in December 2019. This evolution is due to the complex interplay among virus, humans, vaccines, and environments, which makes the elucidation of the clinical and epidemiological characteristics of COVID-19 essential to assess ongoing policy responses. In this study, we carry out an extensive retrospective analysis on infection clusters of COVID-19 in South Korea from January 2020 to September 2021 and uncover important clinical and social factors associated with age and regional patterns through the sophisticated large-scale epidemiological investigation using the data provided by the Korea Disease Control and Prevention Agency (KDCA). Epidemiological data of COVID-19 include daily confirmed cases, gender, age, city of residence, date of symptom onset, date of diagnosis, and route of infection. We divide the time span into six major periods based on the characteristics of COVID-19 according to various events such as the rise of new variants, vaccine rollout, change of social distancing levels, and other intervention measures. We explore key features of COVID-19 such as the relationship among unlinked, asymptomatic, and confirmed cases, serial intervals, infector–infectee interactions, and age/region-specific variations. Our results highlight the significant impact of temporal evolution of interventions implemented in South Korea on the characteristics of COVID-19 transmission, in particular, that of a high level of vaccination coverage in the senior-aged group on the dramatic reduction of confirmed cases.

Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this study is to identify risk factors that predict obesity using machine learning classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on the KNHANES, which used data constructed by the annual survey. Our data include a total of 21,100 participants (male 10,000 and female 11,100). We assess obesity prediction by utilizing six machine learning algorithms. We explore age- and gender-specific risk factors of obesity for adults (19–79 years old). Our results highlight the four most significant features in all age-gender groups for predicting obesity: triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid. Our findings show that the risk factors for obesity are sensitive to age and gender under different machine learning algorithms. Performance is highest for the 19–39 age group of both genders, with over 70% accuracy and AUC, while the 60–79 age group shows around 65% accuracy and AUC. For the 40–59 age groups, the proposed algorithm achieved over 70% in AUC, but for the female participants, it achieved lower than 70% accuracy. For all classifiers and age groups, there is no big difference in the accuracy ratio when the number of features is more than six; however, the accuracy ratio decreased in the female 19–39 age group.

Machine Learning is a powerful tool to discover hidden features in various data driven research fields. Obesity involves extremely complex factors, such as biological, physiological, psychological, and environmental factors. A machine learning framework can provide a successful approach to revealing essential risk factors of the complex obesity phenomenon. Over the last two decades, the obesity population (BMI of above 23) in Korea has been rapidly growing. In this work, we assess obesity prediction by utilizing eight Machine Learning algorithms, and identify risk factors of obesity based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2016-2019. We explore age-specific and gender-specific risk factors of obesity for adults (19-79 years old). Our findings show that the risk factors for obesity are sensitive to age and gender under different Machine Learning algorithms. Both male and female 19-39 age groups show the highest performance of over 70% accuracy and ROC while the 60-79 group shows around 65% accuracy and ROC. Both male and female 40-59 age groups achieved the highest performance of over 70% in ROC, but the female achieved lower 70% in accuracy. Our results highlight that the top four significant features in all age gender groups for prediciting obesity are Triglyceride, ALT(SGPT), Glycated hemoglobin, and urine acid. For the accuracy ratio of the classifiers and age groups, there is no big difference in accuracy when the number of features is more than six, except the accuracy ratio decreased in the female 19-39 age group.