• Journal of Preventive Medicine and Public Health
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• 제35권1호
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• pp.65-71
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• 2002

Objective : To estimate the status of HIV infection and AIDS incidence using a back-calculation model in Korea. Methods : Back-calculation is a method for estimating the past infection rate using AIDS incidence data. The method has been useful for obtaining short-term projections of AIDS incidence and estimating previous HIV prevalence. If the density of the incubation periods is known, together with the AIDS incidence, we can estimate historical HIV infections and forecast AIDS incidence in any time period up to time t. In this paper, we estimated the number of HIV infections and AIDS incidence according to the distribution of various incubation periods Results : The cumulative numbers of HIV infection from 1991 to 1996 were $708{\sim}1,426$ in Weibull distribution and $918{\sim}1,980$ in Gamma distribution. The projected AIDS incidence in 1997 was $16{\sim}25$ in Weibull distribution and $13{\sim}26$ in Gamma distribution. Conclusions : The estimated cumulative HIV infections from 1991 to 1996 were $1.4{\sim}4.0$ times more than notified cumulative HIV infections. Additionally, the projected AIDS incidence in 1997 was less than the notified AIDS cases. The reason for this underestimation derives from the very low level of HIV prevalence in Korea, further research is required for the distribution of the incubation period of HIV infection in Korea, particularly for the effects of combination treatments.

• Journal of Preventive Medicine and Public Health
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• 제53권6호
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• pp.387-396
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• 2020

Objectives: The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. Methods: We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. Results: Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. Conclusions: Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.

• The Plant Pathology Journal
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• 제40권3호
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• pp.290-298
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• 2024

K-Maryblyt has been developed for the effective control of secondary fire blight infections on blossoms and the elimination of primary inoculum sources from cankers and newly emerged shoots early in the season for both apple and pear trees. This model facilitates the precise determination of the blossom infection timing and identification of primary inoculum sources, akin to Maryblyt, predicting flower infections and the appearance of symptoms on various plant parts, including cankers, blossoms, and shoots. Nevertheless, K-Maryblyt has undergone significant improvements: Integration of Phenology Models for both apple and pear trees, Adoption of observed or predicted hourly temperatures for Epiphytic Infection Potential (EIP) calculation, incorporation of adjusted equations resulting in reduced mean error with 10.08 degree-hours (DH) for apple and 9.28 DH for pear, introduction of a relative humidity variable for pear EIP calculation, and adaptation of modified degree-day calculation methods for expected symptoms. Since the transition to a model-based control policy in 2022, the system has disseminated 158,440 messages related to blossom control and symptom prediction to farmers and professional managers in its inaugural year. Furthermore, the system has been refined to include control messages that account for the mechanism of action of pesticides distributed to farmers in specific counties, considering flower opening conditions and weather suitability for spraying. Operating as a pivotal module within the Fire Blight Forecasting Information System (FBcastS), K-Maryblyt plays a crucial role in providing essential fire blight information to farmers, professional managers, and policymakers.

• 한국농림기상학회지
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• 제18권2호
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• pp.65-73
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• 2016

RCP4.5와 RCP8.5 미래 기후 변화 시나리오자료와 참다래 궤양병 피해 예측 모형인 D-PSA-K, 미래 참다래 재배적지 지도를 활용하여 궤양병의 미래 피해를 예측하고 참다래 궤양병의 발생 변화의 경향성을 찾아 보았다. 병원 세균에 의한 감염이 충분히 있다는 가정 아래에서 RCP4.5와 RCP8.5 시나리오의 2020년대와 2050년대에서 궤양병의 최대이병주율은 제주도와 남해안 일부 지역을 제외한 대부분의 지역에서 75% 이상으로 나타날 것으로 예측되었다. 두 시나리오들 모두에서 월동기 저온 환경이 없다는 가정 아래에서의 참다래 궤양병에 의한 가지 피해량은 거의 모든 재배가능지에서 증가될 것으로 예측된 반면에 월동기 저온에 의한 가지 피해량 증가율은 거의 모든 재배가능지에서 감소할 것으로 예측되었다. 지역 및 시나리오별로 궤양병 피해의 증가 및 감소의 경향은 다르게 나타날 것으로 예측되었다. RCP4.5 시나리오에서 2050년대에 2020년대에 비하여 10% 이상 최대 이병주율의 증가가 일어날 것으로 예측된 참다래 재배 가능지는 전체 재배 가능지의 3.14%, RCP8.5 시나리오에서는 25.41%였다.

• The Plant Pathology Journal
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• 제26권1호
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• pp.17-24
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• 2010

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.

• 한국식물병리학회지
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• 제3권3호
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• pp.210-216
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• 1987

미기상 상태에 의하여 벼 도열병을 예찰하기 위한 전산화 예찰모델을 개발하여 그 정확도를 전산모델을 수록한 현지위치형 소형 전산기로서 1984년과 1985년에 걸쳐 포장에서 시험하였다. 건전지 작동형 소형 전산기는 벼 군락내 온도, 습도, 잎이 젖어있는 시간을 계속적으로 측정하여 그 상태를 도열병 발생가능성과 관련하여 평가해서 매일의 병발생가능성 수치(BUS)로 표현한다. 매일의 BUS의 누적치(CBUS)와 두 이병성 품종, M-201과 Brazos에서의 도열병 진전정도와는 밀접한 상관이 있었다. 발병엽율의 logit 치를 CBUS로 회귀하였을 때 평균 결정계수$(R^2)$는 품종과 실험한 해에 따라 $71\%\~91\%$였으며 이것은 시간을 독립변수로 사용하였을 때의 결정계수$61\%\~79\%$에 비하여 현저히 높았다. 결정계수는 M-201에 비하여 생육후기에 포장저항성을 보인 Brazos에서 더 낮았다. 이상의 결과, 현예찰 모델은 실제로 사용가능성이 있지만 앞으로 기주의 저항성이나 병원균 집단의 병원성과 관련한 변수들을 기상환경의 변수와 함께 통합함에 의하여 보다 정확한 예찰모델로 개발할 수 있으리라 생각한다.

• 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.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.224-232
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• 2016

The methodology to visualize the shape of blood vessel and its blood flow have been attracting as a very interesting problem to forecast and examinate a disease in thrombus precursor protein. May previous visualization researches have been appeared for designing the blood vessel and also modeling the blood flow using a doppler imaging technique which is one of nondestructive testing techniques. General visualization methods are to depict the blood flow obtained from doppler effects with fragmentary stream lines and also visualize the blood flow model using volume rendering. However, these visualizeation techniques have the disadvantage which a set of small line segments does not give the overall observation of blood flows. Therefore, we propose a visualization system which reconstruct the continuity of the blood flow obtained from doppler effects and also visualize the blood flow with the vector field of blood particles. This system will use doppler phase difference from medical equipments such as OCT with low penetration and reconstruct the blood flow by the curvature estimation from vector field of each blood particle.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.91-101
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• 2021

The paper examines the impact of the COVID-19 pandemic on the stock market prices. The vector autoregression model (VAR) has been used in this analysis to survey 341 stocks on the Ho Chi Minh City Stock Exchange (HOSE) for the period from January 23, 2020 to December 31, 2020. The empirical results obtained from the analysis of 11 economic sectors suggest that there is a statistically significant impact relationship between COVID-19 and the healthcare and utility industries. Additional findings show a statistically significant negative impact of COVID-19 on the utility share price at lag 1. Analysis of impulse response function (IRF) and forecast error variance decomposition (FEVD) show an inverse reaction of utility stock prices to the impact of COVID-19 and a gradual disappearing shock after two steps. Major findings show that there is a clear negative effect of the COVID-19 pandemic on share prices, and the daily increase in the number of confirmed cases, indicate that, in future disease outbreaks, early containment measures and positive responses are necessary conditions for governments and nations to protect stock markets from excessive depreciation. Utility stocks are among the most severely impacted shares on financial exchanges during a pandemic due to the high risk of immediate or irreversible closure of manufacturing lines and poor demand for basic amenities.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.149-158
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• 2022

The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest X-rays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables