• Journal of Preventive Medicine and Public Health
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• v.56 no.5
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• pp.481-484
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• 2023

Epidemiological models, also known as host-agent-vector-environment models, are utilized in public health to gain insights into disease occurrence and to formulate intervention strategies. In this paper, we propose an epidemiological model that incorporates both conventional measures and tobacco endgame policies. Our model suggests that conventional measures focus on relationships among agent-vector-host-environment components, whereas endgame policies inherently aim to change or eliminate those components at a fundamental level. We also found that the vector (tobacco industry) and environment (physical and social surroundings) components were insufficiently researched or controlled by both conventional measures and tobacco endgame policies. The use of an epidemiological model for tobacco control and the tobacco endgame is recommended to identify areas that require greater effort and to develop effective intervention measures.

• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1359-1378
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• 2010

The effect of impulse in the ecological models makes them more realistic. Recently, the eco-epidemiological models have become an important field of study from the both mathematical and ecological view points. In this article, we consider some eco-epidemiological systems under the influence of impulsive force. A set of sufficient conditions for the permanence of the system are derived. Stability of the trivial solution and at least one strictly positive periodic solution are obtained. Numerical examples are given in support to our analytical findings. Finally, a short discussion concludes the paper.

• Journal of Radiation Protection and Research
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• v.42 no.2
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• pp.130-140
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• 2017

Background: With the need for a domestic level 3 probabilistic safety assessment (PSA), it is essential to develop a Korea-specific code. Health effect assessments study radiation-induced impacts; in particular, long-term health effects are evaluated in terms of cancer risk. The objective of this study was to analyze the latest cancer risk models developed by foreign organizations and to compare the methodology of how they were developed. This paper also provides suggestions regarding the development of Korean cancer risk models. Materials and Methods: A review of cancer risk models was carried out targeting the latest models: the NUREG model (1993), the BEIR VII model (2006), the UNSCEAR model (2006), the ICRP 103 model (2007), and the U.S. EPA model (2011). The methodology of how each model was developed is explained, and the cancer sites, dose and dose rate effectiveness factor (DDREF) and mathematical models are also described in the sections presenting differences among the models. Results and Discussion: The NUREG model was developed by assuming that the risk was proportional to the risk coefficient and dose, while the BEIR VII, UNSCEAR, ICRP, and U.S. EPA models were derived from epidemiological data, principally from Japanese atomic bomb survivors. The risk coefficient does not consider individual characteristics, as the values were calculated in terms of population-averaged cancer risk per unit dose. However, the models derived by epidemiological data are a function of sex, exposure age, and attained age of the exposed individual. Moreover, the methodologies can be used to apply the latest epidemiological data. Therefore, methodologies using epidemiological data should be considered first for developing a Korean cancer risk model, and the cancer sites and DDREF should also be determined based on Korea-specific studies.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.295-308
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• 2015

We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.

• Journal of electromagnetic engineering and science
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• v.16 no.2
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• pp.87-99
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• 2016

This paper describes an implementation method and the results of numerical mobile phone models representing real phone models that have been released on the Korean market since 2002. The aim is to estimate the electromagnetic absorption in the human brain for case-control studies to investigate health risks related to mobile phone use. Specific absorption rate (SAR) compliance test reports about commercial phone models were collected and classified in terms of elements such as the external body shape, the antenna, and the frequency band. The design criteria of a numerical phone model representing each type of phone group are as follows. The outer dimensions of the phone body are equal to the average dimensions of all commercial models with the same shape. The distance and direction of the maximum SAR from the earpiece and the area above -3 dB of the maximum SAR are fitted to achieve the average obtained by measuring the SAR distributions of the corresponding commercial models in a flat phantom. Spatial peak 1-g SAR values in the cheek and tilt positions against the specific anthropomorphic mannequin phantom agree with average data on all of the same type of commercial models. Second criterion was applied to only a few types of models because not many commercial models were available. The results show that, with the exception of one model, the implemented numerical phone models meet criteria within 30%.

• Journal of applied mathematics & informatics
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• v.38 no.5_6
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• pp.375-406
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• 2020

The anti-predator factor due to fear of predator in eco- epidemiological models has a great importance and cannot be evaded. The present paper consists of a modified Lesli-Gower predator-prey model with contagious disease in the predator population only and also consider the fear effect in the prey population. Boundedness and positivity have been studied to ensure the eco-epidemiological model is well-behaved. The existence and stability conditions of all possible equilibria of the model have been studied thoroughly. Considering the fear constant as bifurcating parameter, the conditions for the existence of limit cycle under which the system admits a Hopf bifurcation are investigated. The detailed study for direction of Hopf bifurcation have been derived with the use of both the normal form and the central manifold theory. We observe that the increasing fear constant, not only reduce the prey density, but also stabilize the system from unstable to stable focus by excluding the existence of periodic solutions.

• Journal of Veterinary Science
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• v.24 no.2
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• pp.21.1-21.5
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• 2023

Under the current African swine fever (ASF) epidemic situation, a science-based ASF-control strategy is required. An ASF transmission mechanistic model can be used to understand the disease transmission dynamics among susceptible epidemiological units and evaluate the effectiveness of an ASF-control strategy by simulating disease spread results with different control options. The force of infection, which is the probability that a susceptible epidemiological unit becomes infected, could be estimated by applying an ASF transmission mechanistic model. The government needs to plan an ASF-control strategy based on an ASF transmission mechanistic model.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.295-304
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• 1994

This paper describes some preliminary attempts to formulate simple mathematical models of the transmission dynamics of HIV infection in homosexual communities. In conjunction with a survey of the available epidemiological data on HIV infection and the incidence of AIDS, the model is used to assess how various processes influence the course of the initial epidemic following the introduction of the virus. Models of the early stages of viral spread provide crude methods for estimating the basic reproductive rate of the virus, given a knowledge of the incubation period of AIDS and the initial doubling time of the epidemic. More complex models are formulated to assess the influence of heterogeneity in sexual activity. This latter factor is shown to have a major effect on the predicted pattern of the epidemic.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.