• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.161-168
• /
• 2015

The epidemic is seemed to be extremely difficult for accurate predictions. The new models have been suggested that show quite different results. The basic reproductive number of epidemic for consequent time intervals are estimated based on stochastic processes. In this paper, we proposed a transmission distribution estimation for Ebola virus disease epidemic model. This estimation can be easier to obtain in real time which is useful for informing an appropriate public health response to the outbreak. Finally, we implement our proposed method with data from Guinea Ebola disease outbreak.

• Korean journal of applied entomology
• /
• v.26 no.1 s.70
• /
• pp.31-36
• /
• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.450-452
• /
• 2022

In this paper, we present a standard model of livestock epidemic service in the field of smart livestock, which is emerging as an important issue in smart agriculture. By using the network to identify the global livestock epidemic disease risk and provide relevant models to service users, it is expected that it will actually provide economic benefits to livestock owners and ultimately help the national livestock industry economy. In order to apply the standard livestock epidemic service standard model and the livestock infectious disease crisis mitigation standard model sharing method that is presented in conjunction with ICT to the standards in the domestic and international agricultural and livestock industries in the future, continuous research will be carried out.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.12-14
• /
• 2022

In this paper, we present a standard conceptual model of livestock epidemic service in the field of smart livestock, which is emerging as an important issue in smart agriculture. By using the network to identify the global livestock epidemic disease risk and provide relevant models to service users, it is expected that it will actually provide economic benefits to livestock owners and ultimately help the national livestock industry economy. In order to apply the standard livestock epidemic service standard model and the livestock infectious disease crisis mitigation standard model sharing method that is presented in conjunction with ICT to the standards in the domestic and international agricultural and livestock industries in the future, continuous research will be carried out.

• Convergence Security Journal
• /
• v.8 no.4
• /
• pp.173-181
• /
• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.4
• /
• pp.83-92
• /
• 2022

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model's short-term forecasting advantage with the LSTM model's long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model's forecast accuracy is lower than the SVR-SARIMA model's. As a result, the combined models' order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.5
• /
• pp.73-86
• /
• 2007

Numerous types of models have been developed in recent years in response to the cyber threat posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical modeling techniques such as Epidemic, AAWP (Analytical Active Worm Propagation Modeling) and LAAWP (Local AAWP). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the entire nv4 network and fail to consider the effects of countermeasures, making it difficult to analyze the extent of damage done by them and the effects of countermeasures in a specific network. This paper extends the equations and parameters of AAWP and LAAWP and suggests ALAAWP (Advanced LAAWP), a new worm simulation technique that rectifies the drawbacks of existing models.

• Asian-Australasian Journal of Animal Sciences
• /
• v.23 no.5
• /
• pp.672-679
• /
• 2010

Diabetes mellitus is a worldwide epidemic with high mortality. As concern over this disease rises, the number and value of research grants awarded by the National Research Foundation of Korea (NRF) have increased. Diabetes mellitus is classified into two groups. Type 1 diabetes requires insulin treatment, whereas type 2 diabetes, which is characterized by insulin resistance, can be treated using a variety of therapeutic approaches. Hyperglycemia is thought to be a primary factor in the onset of diabetes, although hyperlipidemia also plays a role. The major organs active in the regulation of blood glucose are the pancreas, liver, skeletal muscle, adipose tissue, intestine, and kidney. Diabetic complications are generally classified as macrovascular (e.g., stroke and heart disease) or microvascular (i.e., diabetic neuropathy, nephropathy, and retinopathy). Several animal models of diabetes have been used to develop oral therapeutic agents, including sulfonylureas, biguanides, thiazolidinediones, acarbose, and miglitol, for both type 1 and type 2 diseases. This review provides an overview of diabetes mellitus, describes oral therapeutic agents for diabetes and their targets, and discusses new developments in diabetic drug research.

• Nutrition Research and Practice
• /
• v.5 no.3
• /
• pp.253-259
• /
• 2011

The rapid rise in the incidence of obesity has emerged as one of the most pressing global public health issues in recent years. The underlying etiological causes of obesity, whether behavioral, environmental, genetic, or a combination of several of them, have not been completely elucidated. The obesity epidemic has been attributed to the ready availability, abundance, and overconsumption of high-energy content food. We determined here by Pearson's correlation the relationship between food type consumption and rising obesity using the loss-adjusted food availability data from the United States Department of Agriculture (USDA) Economic Research Services (ERS) as well as the obesity prevalence data from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES) at the Centers for Disease Control and Prevention (CDC). Our analysis showed that total calorie intake and consumption of high fructose com syrup (HFCS) did not correlate with rising obesity trends. Intake of other major food types, including chicken, dairy fats, salad and cooking oils, and cheese also did not correlate with obesity trends. However, our results surprisingly revealed that consumption of com products correlated with rising obesity and was independent of gender and race/ethnicity among population dynamics in the U.S. Therefore, we were able to demonstrate a novel link between the consumption of com products and rising obesity trends that has not been previously attributed to the obesity epidemic. This correlation coincides with the introduction of bioengineered corns into the human food chain, thus raising a new hypothesis that should be tested in molecular and animal models of obesity.

• Journal of the Korean Applied Science and Technology
• /
• v.30 no.4
• /
• pp.622-634
• /
• 2013

The multi-origin of obesity and its associated diseases made it's a complex area of biomedical science research and severe health disorder. From the 1970s to onwards this health problem turned to an epidemic without having any report of declining yet and it created a red alert to the health sector. Meanwhile, many animal models have been developed to study the lethal effect of obesity. In consequence, many drugs, therapies and strategies have already been adopted based on the findings of those animal models. However, many complicated things based on molecular and generic mechanism has not been clarified to the date. Thus, it is important to develop a need based animal model for the better understanding and strategic planning to eliminate/avoid the obesity disorder. Therefore, the present review would unveil the pros and cons of presently established animal models for obesity research. In addition, it would indicate the required turning direction for further obesity and obesity based disease research.