• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.393-406
• /
• 2015

We examine how to model mortality risk using the adaptation of the mean-reverting processes for the two factor model proposed by Cairns et al. (2006b). Mortality improvements have been recently observed in some countries such as United Kingdom; therefore, we assume long-run mortality converges towards a trend at some unknown time and the mean-reverting processes could therefore be an appropriate stochastic model. We estimate the parameters of the two-factor model incorporated with mean-reverting processes by a Metropolis-Hastings algorithm to fit United Kingdom mortality data from 1991 to 2015. We forecast the evolution of the mortality from 2014 to 2040 based on the estimation results in order to evaluate the issue price of a longevity bond of 25 years maturity. As an application, we propose a method to quantify the speed of mortality improvement by the average mean reverting times of the processes.

• The Korean Journal of Applied Statistics
• /
• v.28 no.4
• /
• pp.703-719
• /
• 2015

We propose an extension of the Lee and Jho (2015) mean reverting the two factor mortality model by incorporating a period-specific cohort effect. We found that the consideration of cohort effect improves the mortality fit of Korea male data above age 65. Parameters are estimated by the weighted least squares method and Metropolis algorithm. We also emphasize that the cohort effect is necessary to choose the base survival index to calculate longevity bond issue price. A key contribution of the article is the proposal and development of a method to calculate the longevity bond price to hedge the longevity risk exposed to Korea National Pension Services.

• Korea journal of population studies
• /
• v.26 no.2
• /
• pp.33-62
• /
• 2003

The purpose of this study is to analyze the relative importance of three factor -socioeconomic development, public health development, egalitarian nature of socioeconomic development- affecting mortality declines. Infant mortality rate and life expectancy at birth are used as the mortality index, that is the dependent variables, while GNP is used as the indicator of socioeconomic development, primary school enrollment ratio of female as the indicator of egalitarian nature of socioeconomic development, population per hospital bed as the indicator of public health. The data of these variables are collected two time-periods -before 1970 and during 1970-1980- over 50 countries. The explanatory data analysis is used as the statistical technique. We can find whether the relationship between dependent variable and independent variables are linear or nonlinear, and which case is the influential case in our model. The main results of this study are followings. First, the association between infant mortality rates and four indices are not linear. The most important factor explaining the variation of infant mortality is GNP, while primary enrollment of female is the second and GINI is the third important factor. However, population per hospital bed does not have a significant effect on the infant mortality rates in this study. Second, life expectancy at birth is log-linearly related to GNP. Unlike infant mortality rates, the most important factor explaining the variation of life expectance at birth is women's education and the next important factor GNP, and then the third one GINI. But, still population per hospital bed is not significantly related to the variation of life expectance in this study.

• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.521-536
• /
• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.

• The Korean Journal of Applied Statistics
• /
• v.31 no.4
• /
• pp.529-538
• /
• 2018

Tuberculosis causes high morbidity and mortality. However, Korea still has the highest tuberculosis (TB) incidence and mortality among OECD countries despite decreasing incidence and mortality due to the development of modern medicine. Korea has now implemented various policy projects to prevent and control tuberculosis. This study analyzes the effects of public-private mix (PPM) tuberculosis control program on treatment outcomes and identifies the factors that affecting the success of TB treatment. We analyzed 130,000 new tuberculosis patient cohort from 2012 to 2015 using data of tuberculosis patient reports managed by the Disease Control Headquarters. A cumulative incidence function (CIF) compared the cumulative treatment success rates for each factor. We compared the results of the analysis using two popular types of competition risk models (cause-specific Cox's proportional hazards model and subdistribution hazard model) that account for the main event of interest (treatment success) and competing events (death).

• Journal of Microbiology and Biotechnology
• /
• v.24 no.11
• /
• pp.1495-1502
• /
• 2014

Metarhizium anisopliae is a widely studied model to understand the virulence factors that participate in pathogenicity. Proteases such as subtilisin-like enzymes (Pr1) and trypsin-like enzymes (Pr2) are considered important factors for insect cuticle degradation. In four M. anisopliae strains (798, 6342, 6345, and 6347), the presence of pr1 and pr2 genes, as well as the enzymatic activity of these genes, was correlated with their virulence against two different insect pests. The 11 pr1 genes (A, B, C, D, E, F, G, H, I, J, and K) and pr2 gene were found in all strains. The activity of individual Pr1 and Pr2 proteases exhibited variation in time (24, 48, 72, and 96 h) and in the presence or absence of chitin as the inductor. The highest Pr1 enzymatic activity was shown by strain 798 at 48 h with chitin. The highest Pr2 enzymatic activity was exhibited by the 6342 and 6347 strains, both grown with chitin at 24 and 48 h, respectively. Highest mortality on S. exigua was caused by strain 6342 at 48 h, and strains 6342, 6345, and 6347 caused the highest mortality 7 days later. Mortality on Prosapia reached 30% without variation. The presence of subtilisin and trypsin genes and the activity of these proteases in M. anisopliae strains cannot be associated with the virulence against the two insect pests. Probably, subtilisin and trypsin enzyme production is not a vital factor for pathogenicity, but its contribution is important to the pathogenicity process.

• Safety and Health at Work
• /
• v.11 no.3
• /
• pp.353-360
• /
• 2020

Background: Urban bus drivers work under conditions that are among the most demanding, stressful, and unhealthy with higher rates of mortality and morbidity as well as absenteeism and turnover. Methods: Drawing on the job demand-resource model, this study investigates the impacts of job characteristics on emotional exhaustion and the effects of emotional exhaustion on job outcomes (including job satisfaction, life satisfaction, organizational commitment, and turnover intention) in the context of bus drivers. Results: Using self-reported survey data collected from a sample of 320 Taiwanese urban bus drivers, results reveal that role overload and work-family conflict (as job demand factors) positively relate to emotional exhaustion, and organizational support (as a job resource factor) is negatively associated with emotional exhaustion. Emotional exhaustion has negative effects on both job satisfaction and organizational commitment. Job satisfaction positively leads to life satisfaction, whereas organizational commitment negatively relates to turnover intention. Conclusion: This study concludes that role overload and work-family conflict as two stressors related to job demands and organizational support as the job resource factor to affect emotional exhaustion which further influence well-being in bus driver context. The moderating effects of both extraversion and neuroticism on the relationship between job demands and emotional exhaustion are evident.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.2
• /
• pp.851-856
• /
• 2016

Background: Breast cancer is a major health problem among women around the world. Recent developments in screening and treatment have greatly improved the prognosis of patients with breast cancer in developed countries. However, in developing countries breast cancer mortality remains high.Breast cancer awareness is a first and important step in reducing breast cancer mortality. The development of a validated instrument to measure breast cancer awareness is crucial for the understanding and implementation of suitable health education programs to facilitate early deletion and minimize mortality. Objective: The objective of this study was to develop an instrument for the assessment of breast cancer awareness in Thai women. Materials and Methods: This methodological study was conducted in two stages: (1) literature searches and semi-structured interviews were conducted to generate items of the breast cancer awareness scale (B-CAS) which were subsequently examined for content and face validity, and (2) an exploration of the factor structure of the resulting instrument and an examination of its reliability. Data were collected using a self-administered questionnaire in Thai women aged 20-64 in August, 2015. Results: A total of 219 women (response rate 97.4 %) participated in this validation study. The B-CAS contains five domains with 53 items on breast cancer awareness: 1) knowledge of risk factors, 2) knowledge of signs and symptoms, 3) attitude to breast cancer prevention, 4) barriers of breast screening, and 5) health behavior related to breast cancer awareness. Items with a content validity index < 0.80 were excluded, and factor structure for the remaining items reflected the hypothesized five factor model. The scales based on all retained items was shown to have strongly internal consistency reliability (Cronbach's ${\alpha}=0.86$). Conclusions: The B-CAS provides good psychometric properties to assess breast cancer awareness in women. It can be used to examine breast cancer awareness in Thai women and it could lead to the development and evaluation of suitable educational interventions for raising breast cancer awareness. Future research should focus on further validating the B-CAS including an assessment of construct and criterion-based validity.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.131-138
• /
• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.9
• /
• pp.5043-5047
• /
• 2013

Background: This is a part of a larger effort to characterize the effects on socio-economic factors (SEFs) on cancer outcome. Surveillance, Epidemiology and End Result (SEER) bone and joint sarcoma (BJS) data were used to identify potential disparities in cause specific survival (CSS). Materials and Methods: This study analyzed SEFs in conjunction with biologic and treatment factors. Absolute BJS specific risks were calculated and the areas under the receiver operating characteristic (ROC) curve were computed for predictors. Actuarial survival analysis was performed with Kaplan-Meier method. Kolmogorov-Smirnov's 2-sample test was used to for comparing two survival curves. Cox proportional hazard model was used for multivariate analysis. Results: There were 13501 patients diagnosed BJS from 1973 to 2009. The mean follow up time (SD) was 75.6 (90.1) months. Staging was the highest predictive factor of outcome (ROC area of 0.68). SEER stage, histology, primary site and sex were highly significant pre-treatment predictors of CSS. Under multivariate analysis, patients living in low income neighborhoods and rural areas had a 2% and 5% disadvantage in cause specific survival respectively. Conclusions: This study has found 2-5% decrement of CSS of BJS due to SEFs. These data may be used to generate testable hypothesis for future clinical trials to eliminate BJS outcome disparities.