• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2785-2791
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• 2014

Aims: To analyze time-dependent changes in female breast cancer (BC) mortality in China, forecast the trend in the ensuing 5 years, and provide recommendations for prevention and management. Materials and Methods: Mortality data of breast cancer in China from 1991 to 2011 was used to describe characteristics and distribution, such as the changes of the standardized mortality rate, urban-rural differences and age differences. Trend-surface analysis was used to study the geographical distribution of mortality. In addition, curve estimation, time series modeling, Gray modeling (GM) and joinpoint regression were performed to estimate and predict future trends. Results: In China, the mortality rate of breast cancer has increased yearly since 1991. In addition, our data predicted that the trend will continue to increase in the ensuing 5 years. Rates in urban areas are higher than those in rural areas. Over the past decade, all peak ages for death by breast cancer have been delayed, with the first death peak occurring at 55 to 65 years of age in urban and rural areas. Geographical analysis indicated that mortality rates increased from Southwest to Northeast and from West to East. Conclusions: The standardized mortality rate of breast cancer in China is rising and the upward trend is predicted to continue for the next 5 years. Since this can cause an enormous health impact in China, much better prevention and management of breast cancer is needed. Consequently, disease control centers in China should place more focus on the northeastern, eastern and southeastern parts of China for breast cancer prevention and management, and the key population should be among women between ages 55 to 65, especially those in urban communities.

• Journal of Preventive Medicine and Public Health
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• 제48권4호
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• pp.203-215
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• 2015

Objectives: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea. Methods: We collected and stored 660 000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul. Results: Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient. Conclusions: In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.

• 보건행정학회지
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• 제26권3호
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• pp.185-194
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• 2016

Background: The purpose of this study is to forecast changes in the prevalence of chronic diseases and health expenditure by age group. Methods: Based on the Future Elderly Model, this study projects the size of Korean population, the prevalence of chronic diseases, and health expenditure over the 2014-2040 period using two waves (2012, 2013) of the Korea Health Panel and National Health Insurance Service database. Results: First, the prevalence of chronic diseases increases by 2040. The population with hypertension increases 2.04 times; the diabetes increases 2.43 times; and the cancer increases 3.38 times. Second, health expenditure on chronic diseases increases as well. Health expenditure on hypertension increases 4.33 times (1,098,753 million won in 2014 to 4,760,811 million won in 2040); diabetes increases 5.34 times (792,444 million won in 2014 to 4,232,714 million won in 2040); and cancer increases 6.09 times (4,396,223 million won in 2014 to 26,776,724 million won in 2040). Third, men and women who belong to the early middle-aged group (44-55 years old) as of 2014, have the highest increase rate in health spending. Conclusion: Most Korean literature on health expenditure estimation employs a macro-simulation approach and does not fully take into account personal characteristics and behaviors. Thus, this study aims to benefit medical administrators and policy makers to frame effective and targeted health policies by analyzing personal-level data with a microsimulation model and providing health expenditure projections by age group.

• The Plant Pathology Journal
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• 제32권1호
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• pp.1-7
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• 2016

Responses of Botryosphaeria dothidea to interrupted wetness periods were investigated under in vivo and in vitro conditions. Conidia of B. dothidea were allowed to germinate on apple fruits under wetting condition at $25^{\circ}C$ for 5 hr. They were air-dried for 0, 1, 2 or 4 hr, and then rewetted at $25^{\circ}C$ for 5 hr. Following an initial wetness period of 5 hr, 83% of the conidia germinated. The percent conidial germination increased to 96% when wetting was extended continuously another 5 hr. However, no further conidial germination was observed when wetting was interrupted by dry periods of 1, 2 and 4 hr, resulting in 83, 81 and 82%, respectively. The mean length of the germ tubes was $37{\mu}m$ after 5 hr of wetting and elongated to $157{\mu}m$ after 10 hr of continuous wetting. On the other hand, interruption of wetting by a dry period of 1 hr or longer after the 5 hr of initial wetting arrested the germ tube elongation at approximately $42{\mu}m$ long. Prolonged rewetting up to 40 hr did not restore germ tube elongation on slide glasses under substrate treatments. Model simulation using weather data sets revealed that ending infection periods by a dry period of at least 1 hr decreased the daily infection periods, avoiding the overestimation of infection warning. This information can be incorporated into infection models for scheduling fungicide sprays to control apple white rot with fewer fungicide applications.

• 한국농림기상학회지
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• 제3권3호
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• pp.156-162
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• 2001

In order to monitor local climatic information, twelve automated weather stations (AWS) were installed in alpine area by the Alpine Agricultural Experiment Station, Rural Development Administration (RDA), at the field of major crop located in around highland area, and collected data from 1993 to 2000. Hourly measurements of air and soil temperature (underground 10 cm,20 cm), relative humidity, wind speed and direction, precipitation, solar radiation and leaf wetness were automatically performed and the data could be collected through a public phone line. Datalogger was selected as CR10X (Campbell scientific, LTD, USA) out of consideration for sensers' compatibility, economics, endurance and conveniences. All AWS in alpine area were combined for net work and daily climatic data were analyzed in text and graphic file by program (Chumsungdae, LTD) on 1 km $\times$ 1 km grid tell basis. In this analysis system, important multi-functionalities, monitoring and analysis of local climatic information in alpine area was emphasized. The first objective was to obtain the output of a real time data from AWS. Secondly, daily climatic normals for each grid tell were calculated from geo-statistical relationships based on the climatic records of existing weather stations as well as their topographical informations. On 1 km $\times$ 1 km grid cell basis, real time climatic data from the automated weather stations and daily climatic normals were analyzed and graphed. In the future, if several simulation models were developed and connected with this system it would be possible to precisely forecast crop growth and yield or plant disease and pest by using climatic information in alpine area.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.131-138
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• 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.

• Genomics & Informatics
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• 제21권4호
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• pp.50.1-50.9
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• 2023

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.

• Journal of Acupuncture Research
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• 제21권1호
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• pp.1-20
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• 2004

Pulse-feeling took its origin from making a diagnosis along meridians in the course of discovering and forming meridians and for a long time its meaning was mixed with meridians in the course of recognizing "The Pulse" then was separated from meridians in the early days of Western Han Dynasty. Ancient pulse-feeling methods are pulse-feeling method by the twelve regular meridians, pulse-feeling method by three regions and nine modes, pulse-feeling method by Inyeong(人迎) and Chon-gu(寸口), etc. Pulse-feeling was changed in proportion to diagnostic purpose and method of treating and if method and region of pulse-feeling is arranged, we will infer correlation between meridians and pulse-feeling and will infer transitional system of past pulse-feeling and will forecast transition of future pulse-feeling. As the result that I study the transition of the above three pulse-feeling methods of meridians diagnosis: 1. Three pulse-feeling methods of meridians diagnosis flowed into diagnostic method by taking pulse of setting six region for Chon(寸), Gwan(關) and Cheok(尺), i.e. the Chon[寸] spot pulse of $\ll$Nan-gyeong$\gg$ and were changed into diagnostic method being fit for use of five Su points, The Front-Mo points and Back-Su points that grasp the pathology of mutual internal organs and treat the disease. 2. Today it is suggesting the transition of another pulse-feeling method that do not apply diagnostic method by taking pulse of setting six region for Chon(寸), Gwan(關) and Cheok(尺), i.e. the Chon[寸] spot pulse of $\ll$Nan-gyeong$\gg$ to 19C Sasang(四象) Constitutional Medicine or 20C Eight Constitutional Medicine.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.294-297
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• 2015

최근 기계학습에 기반을 둔 빅데이터 분석이 큰 관심을 받으면서 다양한 학문 분야에 기계 학습 방안들이 접목되고 있다. 그 대표적인 분야 중 하나로 농축산 분야를 들 수 있고 실제 다양한 기계학습 방안들이 농축산분야에 적용되고 있다. 하지만 농축산에서 활용되는 기계학습의 경우 대부분 농업분야의 기후예측 및 축산분야의 유전자 분석 쪽으로 연구가 집중되어있고, 가축의 생체 데이터를 활용한 기계학습 방안은 많은 연구가 이루어지지 않았다. 본 연구에서는 가축의 실시간 생체 데이터를 이용하여 문제가 발생한 개체를 조기에 발견하는 방안을 제안하였다. 제안 방안에서는 기댓값 최대화 알고리즘을 이용하여 단일 가축 개체들의 실시간 생체 데이터를 2개의 클러스터로 나누고 이 두 클러스터 사이즈의 변화를 통해서 이상 개체를 조기에 판단한다. 특히 단일 개체의 문제와 전염성 질병 여부를 나누어 판단하므로 구제역과 같은 전염성 질병의 경우 빠른 대응을 가능케 하여 국가적 손실을 줄일 수 있게 한다. 더불어 제안 방안은 측정 생체 데이터에 대한 통계적 정보 없이도 적응적으로 클러스터를 형성할 수 있으므로 축사 외부의 환경 요소에 의해서 생체 데이터의 통계적 특성이 변화는 상황에서도 적응적으로 동작할 수 있다.

• 한국식품위생안전성학회지
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• 제27권4호
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• pp.339-348
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• 2012

본 연구에서는 향후 고령친화식품의 중심적인 상품군이 될 건강기능식품과 특수용도식품의 시장규모에 대한 예측을 시도하였다. 건강기능식품과 특수용도식품 중 고령친화와 관련된 시장의 범위를 설정하고 현황에 대해서 분석한 후 향후 전망에 대해서 예측하였는데, 경제성장과 민간소비지출의 변화 그리고 이에 따른 노령인구의 경제력 변화, 의료보험 및 연금저축의 변화 등을 반영하였다. 한편 관련기업들을 대상으로 한 설문조사 결과를 활용하는 한편, 노인인구의 변화 및 주요 만성질환의 발병률을 분석하여 예측에 반영하였다. 결과를 보면 2010년부터 2025년까지 건강기능식품과 특수용도식품 중 고령친화와 관련된 부문의 연평균 성장률은 최소 4.54%에서 최대 8.32%로 예측되었으며, 시장규모 예측치는 모형과 예측방식에 따라 2025년에 최소 7,073억원에서 최대 10,976억원으로 나타났다. 향후 고령친화제품의 수요는 고령인구의 증가와 보험급여 확대 등으로 크게 성장할 것으로 예상되는 반면에, 기업들은 수요의 변화를 관망하고 있는 상황으로 판단된다. 따라서 이는 자칫 수요에 비하여 부족한 공급 문제를 야기시킬 수 있다. 따라서 정부의 R&D에 대한 적극적인 지원, 고령친화식품에 대한 표준화 및 인증제 실시, 관련산업의 DB구축 등이 필요한 상황이다.