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Statistical estimations for Plasmodium vivax malaria in South Korea

Asian Pacific Journal of Tropical Medicine 2015年第3期8卷 169-175页

作者： youngsaeng lee Hyeongap Jang Jeong Ae Rhee Jeong-Soo Park Department of Statistics Chonnam National University JW lee Center for Global Medicine College of MedicineSeoul National University Department of Preventive Medicine Chonnam National University

Objective:To calculate the numbers of weekly infections and prevalence of malaria,and to predict future trend of malaria incidences in South ***:Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were *** backcalculation equations were used with incubation period *** maximum likelihood estimation for Poisson model was also *** confidence intervals of the estimates were obtained by a bootstrap method.A regression model for time series of malaria incidences over13 years was fitted by the non-linear least squares method,and used to predict ***:The estimated infection curve is narrower and more concentrated in the summer than in the incidence *** started around the 19 th week and was over around the41 st *** maximum weekly infection 110 was obtained at the 29 th *** prevalence at the first week was around 496 persons,the minimum number was 366 at 22 nd week,and the maximum prevalence was 648 at 34 th *** drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new *** future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak(at maximum about 70 weekly cases) at year *** a decreasing trend after ***:This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of *** obtained results can be useful in establishing an efficient preventive program for malaria *** method presented here can be used in other countries where incidence data and incubation period are available.

关键词： Back calculation Incidence Incubation period Infection Poisson model Prevalence Regression model

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Spatial Modeling of the Highest Daily Maximum Temperature in Korea via Max-stable Processes

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Advances in Atmospheric Sciences 2013年第6期30卷 1608-1620页

作者： youngsaeng lee Sanghoo YOON Md.Sharwar MURSHED Maeng-Ki KIM ChunHo CHO Hee-Jeong BAEK Jeong-Soo PARK Department of Statistics Chonnam National University School of Mathematics University of Southampton Department of Business Administration Northern University Bangladesh Department of Atmospheric Science Kongju National University Climate Research Laboratory National Institute of Meteorological ResearchKMA Seoul Korea

This paper examines the annual highest daily maximum temperature （DMT） in Korea by using data from 56 weather stations and employing spatial extreme modeling. Our approach is based on max-stable processes （MSP） with Schlather＇s characterization. We divide the country into four regions for a better model fit and identify the best model for each region. We show that regional MSP modeling is more suitable than MSP modeling for the entire region and the pointwise generalized extreme value distribution approach. The advantage of spatial extreme modeling is that more precise and robust return levels and some indices of the highest temperatures can be obtained for observation stations and for locations with no observed data, and so help to determine the effects and assessment of vulnerability as well as to downscale extreme events.

关键词： extreme climate extremal coefficient generalized extreme value distribution prediction re- turn level spatial extremes

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