• Title/Summary/Keyword: Epidemics

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Prevention and treatment of epidemics written in Ganuibyeokonbang (("간이벽온방"에 기재된 돌림병의 예방과 치료)

  • Lee, Yun-Sim;Chough, Won-Joon
    • Korean Journal of Oriental Medicine
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    • v.13 no.1 s.19
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    • pp.29-35
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    • 2007
  • As epidemics were spread over the whole Pyeongan province at 1524, Jungjong commanded the government officers like Kim Sunmong to publish Sokbyeokonbang, known as Ganuibyeokonbang, to cope with the epidemics. They regarded the cause of epidemics as abnormal climate, pathogen or grudge, and named the disease on the basis of cause. To prevent epidemics they presented three kinds of method. They used a charm to calm the people, used Sohaphyangwon to keep from getting infected with them and emphasized the importance of individaul sanitation. They proposed compound herb remedies like Sipsintang, Hyangsosan, Seungmagalgeuntang and so forth according to the symptoms. They presented lots of single herbs used for food or easily seeking herbs to lighten the people's expense, in addition.

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Epidemiological comparison of three Myco­plasma pneumoniae pneumonia epidemics in a single hospital over 10 years

  • Kim, Eun-Kyung;Youn, You-Sook;Rhim, Jung-Woo;Shin, Myung-Seok;Kang, Jin-Han;Lee, Kyung-Yil
    • Clinical and Experimental Pediatrics
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    • v.58 no.5
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    • pp.172-177
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    • 2015
  • Purpose: Mycoplasma pneumoniae (MP) pneumonia epidemics have occurred in 3- to 4-year cycles in Korea. We evaluated the epidemiologic characteristics of MP pneumonia in Daejeon, Korea, from 2003 to 2012. Methods: We retrospectively analyzed 779 medical records of children (0-15 years of old) with MP pneumonia admitted to our institution and compared the data from 3 recent epidemics. Results: In 779 patients, the mean age and male-to-female ratio were $5.0{\pm}2.2$ years and 1:1, and most cases were observed in autumn. There were three epidemics during the study period, in 2003, 2006-2007, and 2011. In our comparison of the three epidemics, we found no differences in mean age, the male-to-female ratio, hospital stay, or the rate of seroconverters during hospitalization. All three epidemics began in early summer and peaked in September 2003 and 2011 and in October 2006 and then gradually decreased until the next year's spring season, although the 2006 epidemic extended further into 2007. The peak age groups in the children in 2003 and 2006 were 3-6 year-olds (57.5% and 56%, respectively), but in the 2011 epidemic, the peak group was 1-4 year-olds (46.5%). The proportion of the <2 years of age group was 20%, 15.7% and 28.8%, and >10 years of age group was 5.2%, 13.8%, and 14.8% of total patients, respectively. Conclusion: MP pneumonia outbreaks occurred every 3-4 years. The pattern of 3 recent epidemics was similar in demographic characteristics and seasonality with some variations in each outbreak.

Infectious Disease Prevention Act Written on Medical Books in Joseon Dynasty (조선시대 피역의서에 나타난 역병(疫病) 예방법)

  • Chough, Won-Joon
    • Journal of Society of Preventive Korean Medicine
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    • v.12 no.2
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    • pp.145-157
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    • 2008
  • There were many books on infectious disease prevention act, and still remained 5 books including Ganuibyeokonbang. Epidemics were seriously ill and widely contagious, so it was important to prevent them. Therefore, they wrote various preventive measures from epidemics on those books. They emphasized medication, and used not only compound prescriptions but also singular ones. They wrote 5 compound prescriptions including Sohaphyangwon and many singular ones on Ganuibyeokonbang, and they used folk medicine such as red-beans준 for practical use on that book. On Sinchanbyeokonbang, they emphasized Hyangsosan and presented many prescriptions to specialize in epidemics. Heojun presented various prescriptions for Dangdokyeok on Byeokyeoksinbang, and he excluded incantation methods to cope with epidemics medically. Since Ganuibyeokonbang they had tried to improve personal hygiene such as boiling clothes of patients.

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The Ancient Understanding of Epidemic Development (고대인들의 역병 인식;"삼국사기"를 중심으로)

  • Choi, Seong-Woong;Yoo, Woon-Jun;Kim, Hong-Kyoon
    • Korean Journal of Oriental Medicine
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    • v.13 no.3
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    • pp.39-43
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    • 2007
  • The history of medicine has a strong correlation to the ancient development of epidemics. Although the study of the history of East Asian Traditional Medicine does not put much emphasis in understanding the flow of medical history in relation to epidemics, it largely impacted the development of this epidemic and the compilation and evolution of treatment methods. The same was true for Korea as they linked development of epidemics, unusual natural conditions, and social phenomenons from ancient documents. This study methodically classifies the epidemics mentioned in ${\ulcorner}$三國史記${\lrcorner}$ and concludes on how ancient Koreans understood epidemics.

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Predicting Potential Epidemics of Rice Leaf Blast Disease Using Climate Scenarios from the Best Global Climate Model Selected for Individual Agro-Climatic Zones in Korea (국내 농업기후지대 별 최적기후모형 선정을 통한 미래 벼 도열병 발생 위험도 예측)

  • Lee, Seongkyu;Kim, Kwang-Hyung
    • Journal of Climate Change Research
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    • v.9 no.2
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    • pp.133-142
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    • 2018
  • Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

Real-Time Micro-Weather Factors of Growing Field to the Epidemics of Rice Blast (벼 도열병 Epidemics에 미치는 재배 포장 실황기상 요인)

  • Kwon, Jae-Oun;Lee, Soon-Gu
    • Research in Plant Disease
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    • v.8 no.4
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    • pp.199-206
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    • 2002
  • It was investigated on the relationship of the rice blast epidemics and the real-time meteorological factors, at the experimental paddy field in 1997. Weather factors(temperature, relative humidity, irradiation, precipitation, the direction of wind, wind speed, soil temperature and leaf-wetness, etc) were measured by using the automated weather station. The most influenced weather factor to blast epidemics, was the average max-temp($R^2$= 0.95) during 10 days before leaf blast epidemics, while the least thing was wind speed($R^2$= 0.24). The most potential weather factors correlated with the blast epidemics were T-ave(average temperature), T-max(maximum temperature), RH(Relative Humidity) and RD(Relative Humidity > 90% hrs). A statistics model(the regression equation) of the blast epidemics with the potential weather factors, was established as tallows ; Y = -3410.91 - 23.91 $\times$ T-ave + 28.56 $\times$ T-max + 41.0 $\times$ RH - 3.75 $\times$ RD, ($R^2$= 0.99). (T-ave >= 19$^{\circ}C$, T-max - T-ave >= 5.2$^{\circ}C$ and RH% >= 90.4%). According to the fitness test($\chi$$^2$) of the model, the observed blast disease severity was quite close to those expected.

Comparison study of SARIMA and ARGO models for in influenza epidemics prediction

  • Jung, Jihoon;Lee, Sangyeol
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.1075-1081
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    • 2016
  • The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

Reemergence of mumps

  • Choi, Kyong-Min
    • Clinical and Experimental Pediatrics
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    • v.53 no.5
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    • pp.623-628
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    • 2010
  • The mumps virus is a single-stranded, non-segmented, negative-sense RNA virus belonging to the $Paramyxoviridae$ family. Mumps is characterized by bilateral or unilateral swelling of the parotid gland. Aseptic meningitis is a common complication, and orchitis is also common in adolescents and adult men. Diagnosis is based on clinical findings, but because of high vaccination coverage, clinical findings alone are not sufficient for diagnosis, and laboratory confirmation is needed. Mumps is preventable by vaccination, but despite high vaccination coverage, epidemics occur in several countries, including Korea. Many hypotheses are suggested for these phenomena. In this review, we investigate the reason for the epidemics, optimal methods of diagnosis, and surveillance of immunization status for the prevention of future epidemics.

AN EXTENSION OF AN ANALYTIC FORMULA OF THE DETERMINISTIC EPIDEMICS MODEL PROBLEM THROUGH LIE GROUP OF OPERATORS

  • Kumar, Hemant;Kumari, Shilesh
    • Bulletin of the Korean Mathematical Society
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    • v.47 no.6
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    • pp.1131-1138
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    • 2010
  • In the present paper, we evaluate an analytic formula as a solution of Susceptible Infective (SI) model problem for communicable disease in which the daily contact rate (C(N)) is supposed to be varied linearly with population size N(t) that is large so that it is considered as a continuous variable of time t. Again, we introduce some Lie group of operators to make an extension of above analytic formula of the determin-istic epidemics model problem. Finally, we discuss some of its particular cases.

Monitoring Seasonal Influenza Epidemics in Korea through Query Search (인터넷 검색어를 활용한 계절적 유행성 독감 발생 감지)

  • Kwon, Chi-Myung;Hwang, Sung-Won;Jung, Jae-Un
    • 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.