• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.931-946
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• 2015

Many multiple life insurance products consider benefits that are contingent on the combined survival status of two lives. To value premiums of the insurance products accurately, we need to consider the impact of the survivorship of one life on another. To show a dependence relation between married couple, we calculate correlation coefficients by using married couples data from National Pension Service and the results show some positive dependence between them. Moreover, by analyzing the death after bereavement, we find a evidence that mortality rates increase after the death of a spouse and, in addition, that this phenomenon, the broken-heart syndrome, diminishes over time. The results of this study can support the method to calculate the premium of multiple life insurance reflecting more realistic joint mortality rates.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.