• Research in Community and Public Health Nursing
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• v.8 no.2
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• pp.314-326
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• 1997

In Korea, the nosocomial infection control program is not well developed. This situation is created by a lack of interest from medical personnel and the medical payment system. This study identifies current problems and develops a model for nosocomial infection control. The studies of Lee & Kim(1995), Lee (1993) and SENIC project model were used to construct this model. 1. The problems of nosocomial infection control were identified as the following: dis approval by hospital authorities, lack of sources for program direction, lack of overall structure and function in the program, inadequate direct action, lack of education and training, and so on. 2. The problems are reorganized according to the 5 elements of system theory. 3. As a result, the new nosocomial infection control model was developed. The inputs of the model were the elements, resources and boundaries of nosocomial infection. With the new model, each hospital can evaluate their current programs and plan a new program for the better control of nosocomial infection.

• The Plant Pathology Journal
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• v.30 no.2
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• pp.125-135
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• 2014

A population model of bacterial spot caused by Xanthomonas campestris pv. vesicatoria on hot pepper was developed to predict the primary disease infection date. The model estimated the pathogen population on the surface and within the leaf of the host based on the wetness period and temperature. For successful infection, at least 5,000 cells/ml of the bacterial population were required. Also, wind and rain were necessary according to regression analyses of the monitored data. Bacterial spot on the model is initiated when the pathogen population exceeds $10^{15}cells/g$ within the leaf. The developed model was validated using 94 assessed samples from 2000 to 2007 obtained from monitored fields. Based on the validation study, the predicted initial infection dates varied based on the year rather than the location. Differences in initial infection dates between the model predictions and the monitored data in the field were minimal. For example, predicted infection dates for 7 locations were within the same month as the actual infection dates, 11 locations were within 1 month of the actual infection, and only 3 locations were more than 2 months apart from the actual infection. The predicted infection dates were mapped from 2009 to 2012; 2011 was the most severe year. Although the model was not sensitive enough to predict disease severity of less than 0.1% in the field, our model predicted bacterial spot severity of 1% or more. Therefore, this model can be applied in the field to determine when bacterial spot control is required.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.190-201
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• 2024

This study examined the effect of the distance Infection Control Education Program (ICEP), developed based on the ADDIE model, on infection control knowledge, attitude, and performance among care workers in long-term care facilities nationwide. The program, developed based on the ADDIE model, was applied to 173 care workers directly responsible for nursing care of elderly residents in lomg-term care facilities. The distance ICEP for care workers was conducted through the website and lasted 30 minutes for each of the eight topics. To determine the effectiveness of the education, infection control knowledge, attitude, performance, and satisfaction were surveyed before and four weeks after the program. Differences in infection control knowledge, attitude, and performance before and after the distance ICEP were assessed by a t-test. A significant difference was observed in knowledge and infection control performance after the distance ICEP was administered to care workers. In the sub-domains of infection control performance, overall understanding of infection, regular infection control education, infection control by special pathogen (multidrug-resistant bacteria, tuberculosis, tick-borne infectious diseases), and detailed infection control education by infection site (pressure ulcers and urinary tract infections) were significantly improved. Infection control knowledge and performance improved through the distance ICEP applied to care workers. Satisfaction also displayed high scores on most items and indicated that it was helpful for infection control in facilities, confirming the effectiveness of infection control education. Based on the survey of care workers nationwide, the infection education program can be effectively used for care workers in the future.

• The Plant Pathology Journal
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• v.39 no.5
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• pp.504-512
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• 2023

After transitioning from periodic to model-based control policy for fire blight blossom infection, it is crucial to provide the timing of field application with easy and accurate information. To assess the risk of blossom infection, Maryblyt was employed in 31 sites across apple-producing regions nationwide, including areas prone to fire blight outbreaks, from 2021 to 2023. In 2021 and 2023, two and seven sites experienced Blossom Infection Risk-Infection warning occurrences among 31 sites, respectively. However, in 2022, most of the sites observed Blossom Infection Risk-Infection from April 25 to 28, highlighting the need for blossom infection control. For the comparison between the two model-based control approaches, we established treatment 1, which involved control measures according to the Blossom Infection Risk-Infection warning and treatment 2, aimed at maintaining the Epiphytic Infection Potential below 100. The analysis of control values between these treatments revealed that treatment 2 was more effective in reducing Blossom Infection Risk-Infection and the number of days with Epiphytic Infection Potential above 100, with respective averages of 95.6% and 93.0% over the three years. Since 2022, the implementation of the K-Maryblyt system and the deployment of Automated Weather Stations capable of measuring orchard weather conditions, with an average of 10 stations per major apple fire blight county nationwide, have taken place. These advancements will enable the provision of more accurate and timely information for farmers based on fire blight models in the future.

• The Plant Pathology Journal
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• v.37 no.6
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• pp.543-554
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• 2021

To preventively control fire blight in apple trees and determine policies regarding field monitoring, the Maryblyt ver. 7.1 model (MARYBLYT) was evaluated in the cities of Chungju, Jecheon, and Eumseong in Korea from 2015 to 2020. The number of blossom infection alerts was the highest in 2020 and the lowest in 2017 and 2018. And the common feature of MARYBLYT blossom infection risks during the flowering period was that the time of BIR-High or BIR-Infection alerts was the same regardless of location. The flowering periods of the trees required to operate the model varied according to the year and geographic location. The model predicts the risk of "Infection" during the flowering periods, and recommends the appropriate times to control blossom infection. In 2020, when flower blight was severe, the difference between the expected date of blossom blight symptoms presented by MARYBLYT and the date of actual symptom detection was only 1-3 days, implying that MARYBLYT is highly accurate. As the model was originally developed based on data obtained from the eastern region of the United States, which has a climate similar to that of Korea, this model can be used in Korea. To improve field utilization, however, the entire flowering period of multiple apple varieties needs to be considered when the model is applied. MARYBLYT is believed to be a useful tool for determining when to control and monitor apple cultivation areas that suffer from serious fire blight problems.

• The Plant Pathology Journal
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• v.28 no.2
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• pp.172-184
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• 2012

An infection risk model for Phytophthora blight on chili pepper was developed to estimate the first date of disease occurrence in the field. The model consisted of three parts including estimation of zoosporangium formation, soil water content, and amount of active inoculum in soil. Daily weather data on air temperature, relative humidity and rainfall, and the soil texture data of local areas were used to estimate infection risk level that was quantified as the accumulated amount of active inoculum during the prior three days. Based on the analysis on 190 sets of weather and disease data, it was found that the threshold infection risk of 224 could be an appropriate criterion for determining the primary infection date. The 95% confidence interval for the difference between the estimated date of primary infection and the observed date of first disease occurrence was $8{\pm}3$ days. In the model validation tests, the observed dates of first disease occurrence were within the 95% confidence intervals of the estimated dates in the five out of six cases. The sensitivity analyses suggested that the model was more responsive to temperature and soil texture than relative humidity, rainfall, and transplanting date. The infection risk model could be implemented in practice to control Phytophthora blight in chili pepper fields.

• The Plant Pathology Journal
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• v.33 no.2
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• pp.206-211
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• 2017

The effect of temperature on the rate of systemic infection of potatoes (Solanum tuberosum L. cv. Chu-Baek) by Potato virus Y (PVY) was studied in growth chambers. Systemic infection of PVY was observed only within the temperature range of $16^{\circ}C$ to $32^{\circ}C$. Within this temperature range, the time required for a plant to become infected systemically decreased from 14 days at $20^{\circ}C$ to 5.7 days at $28^{\circ}C$. The estimated lower thermal threshold was $15.6^{\circ}C$ and the thermal constant was 65.6 degree days. A systemic infection model was constructed based on experimental data, using the infection rate (Lactin-2 model) and the infection distribution (three-parameter Weibull function) models, which accurately described the completion rate curves to systemic infection and the cumulative distributions obtained in the PVY-potato system, respectively. Therefore, this model was useful to predict the progress of systemic infections by PVY in potato plants, and to construct the epidemic models.

• The Plant Pathology Journal
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• v.26 no.1
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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.

• The Korean Journal of Emergency Medical Services
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• v.24 no.1
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• pp.7-24
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• 2020

Purpose: Emergency medical service (EMS) personnel are at high risk of spreading infection. In this study, we used the PRECEDE model to identify the knowledge, status, and barriers to infection control among Korean paramedics to provide basic infection control data. Methods: A total of 164 respondents were analyzed for the study. A questionnaire was administered and collected through an online self-response platform. Descriptive analysis, t-test, ANOVA, multiple regression, and logistic regression analyses were performed to determine infection control practices and associated factors using SAS 9.4. To identify the pathways and direct, indirect, total effects based on the PRECEDE model, we used AMOS 26.0. Results: Highly rated self-efficacy (OR 8.82, 95% CI: 3.23-24.09), awareness (OR 6.05, 95% CI: 2.06-17.72), and enabling factors (OR 3.23, 95% CI: 1.18-8.78) led to superior infection control. As a result of the structural model analysis, the highly rated enabling factors and awareness led to superior practice patterns. Conclusion: Practice is related to self-efficacy, awareness, and enabling factors; however, further research is needed to develop strategies for infection control. In particular, institutional arrangements are needed to improve the enabling factors. Improving infection control performance may lead to better infection control and enhanced protection of EMS personnel and patients against infection risks.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.2
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• pp.137-170
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• 2015

In this paper, we propose and analyze three viral infection models with humoral immunity including an eclipse stage of infected cells. The incidence rate of infection is represented by bilinear incidence and saturated incidence in the first and second models, respectively, while it is given by a more general function in the third one. The neutralization rate of viruses is giv0en by bilinear form in the first two models, while it is given by a general function in the third one. For each model, we have derived two threshold parameters, the basic infection reproduction number which determines whether or not a chronic-infection can be established without humoral immunity and the humoral immune response activation number which determines whether or not a chronic-infection can be established with humoral immunity. By constructing suitable Lyapunov functions we have proven the global asymptotic stability of all equilibria of the models. For the third model, we have established a set of conditions on the threshold parameters and on the general functions which are sufficient for the global stability of the equilibria of the model. We have performed some numerical simulations for the third model with specific forms of the incidence and neutralization rates and have shown that the numerical results are consistent with the theoretical results.