• Journal of Environmental Science International
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• v.28 no.7
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• pp.617-627
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• 2019

This study investigated the relationship between heat-related illnesses obtained from healthcare big data and daily maximum temperature observed in seven metropolitan cities in summer during 2013~2015. We found a statistically significant positive correlation (r = 0.4~0.6) between daily maximum temperature and number of the heat-related patients from Pearson's correlation analyses. A time lag effect was not observed. Relative Risk (RR) analysis using the Generalized Additive Model (GAM) showed that the RR of heat-related illness increased with increasing threshold temperature (maximum RR = 1.21). A comparison of the RRs of the seven cities, showed that the values were significantly different by geographical location of the city and had different variations for different threshold temperatures. The RRs for elderly people were clearly higher than those for the all-age group. Especially, a maximum value of 1.83 was calculated at the threshold temperature of $35^{\circ}C$ in Seoul. In addition, relatively higher RRs were found for inland cities (Seoul, Gwangju, Daegu, and Daejeon), which had a high frequency of heat waves. These results demonstrate the significant risk of heat-related illness associated with increasing daily maximum temperature and the difference in adaptation ability to heat wave for each city, which could help improve the heat wave advisory and warning system.

• Journal of Preventive Medicine and Public Health
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• v.52 no.2
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• pp.82-91
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• 2019

Objectives: Many studies have explored the relationship between short-term weather and its health effects (including pneumonia) based on mortality, although both morbidity and mortality pose a substantial burden. In this study, the authors aimed to describe the influence of meteorological factors on the number of emergency room (ER) visits due to pneumonia in Seoul, Korea. Methods: Daily records of ER visits for pneumonia over a 6-year period (2009-2014) were collected from the National Emergency Department Information System. Corresponding meteorological data were obtained from the National Climate Data Service System. A generalized additive model was used to analyze the effects. The percent change in the relative risk of certain meteorological variables, including pneumonia temperature (defined as the change in average temperature from one day to the next), were estimated for specific age groups. Results: A total of 217 776 ER visits for pneumonia were identified. The additional risk associated with a $1^{\circ}C$ increase in pneumonia temperature above the threshold of $6^{\circ}C$ was 1.89 (95% confidence interval [CI], 1.37 to 2.61). Average temperature and diurnal temperature range, representing within-day temperature variance, showed protective effects of 0.07 (95% CI, 0.92 to 0.93) and 0.04 (95% CI, 0.94 to 0.98), respectively. However, in the elderly (65+ years), the effect of pneumonia temperature was inconclusive, and the directionality of the effects of average temperature and diurnal temperature range differed. Conclusions: The term 'pneumonia temperature' is valid. Pneumonia temperature was associated with an increased risk of ER visits for pneumonia, while warm average temperatures and large diurnal temperature ranges showed protective effects.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.271-280
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• 2009

For bivariate binomial data with both intra and inter-class correlation, Danaher and Hardie (2005) proposed a bivariate beta-binomial model. However, the model is limited to the situation where the intra-class correlation is strictly positive. Thus it might be seriously inadequate for data with a negative intra-class correlation. Several authors have considered generalized binomial distributions covering a wider range of intra-class correlation which could relax the possible model restrictions imposed. Among others there are the additive/multiplicative and the beta/extended beta binomial model. In this study, bivariate models of the Sarmanov (1966) type are formed by combining each of those univariate models to take care of the inter-class correlation, and are evaluated in terms of the goodness-of-fit. As a result, B-mB and B-ebB are fitted, successfully, to real data and that B-mB, which has a wider permissible range than B-ebB for the intra-class correlation is relatively preferred.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.4
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• pp.319-325
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• 1991

A nonparametric detection scheme which uses rank statistics for detection of known signals is considered in a special case of a generalized observation model. Specifically locally optimum rank detectors for detection of known deterministic singals in a singnal-dependent noise model are derived, and compared to those derived for the purely-additive noise model. Examples of the score functions are given, which constitutes the test statistics of the locally optimum rank detectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.3
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• pp.270-276
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• 1988

The problem of detecting weak random signals is addressed in a generalized observation model incorporating multiplicative noised which has recently been introduced. It is shown that the locally optimum random-signal detectors in the multiplicative-noise model are interseting generalizations of those which would be obtained in the purely-additive noise model. Examples of explicits results for the locally optimum detector test statistics are given for two typical cases of well-known pdfs.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.108-119
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• 2024

Objectives: Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous. Methods: This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels. Results: Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 ㎛ or less (PM_2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 ㎍/m³ increase in PM_2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM_2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM_2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak. Conclusions: Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM_2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM_2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1125-1131
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• 1991

In a generalized model with which we can represent multiplicative noise as well as purely additive noise, we consider detection of composite signals which contain both deterministic and stochastic signal components. To illustrate the performance of the locally optimum detectors, finite sample-size performance characteristics are obtained and compared with those of other detectors.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.