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Chang Soo Kim 3 Articles
Mathematical Modeling of the Novel Influenza A (H1N1) Virus and Evaluation of the Epidemic Response Strategies in the Republic of Korea.
Mina Suh, Jeehyun Lee, Hye Jin Chi, Young Keun Kim, Dae Yong Kang, Nam Wook Hur, Kyung Hwa Ha, Dong Han Lee, Chang Soo Kim
J Prev Med Public Health. 2010;43(2):109-116.
DOI: https://doi.org/10.3961/jpmph.2010.43.2.109
  • 15,748 View
  • 227 Download
  • 11 Crossref
AbstractAbstract PDF
OBJECTIVES
The pandemic of novel influenza A (H1N1) virus has required decision-makers to act in the face of the substantial uncertainties. In this study, we evaluated the potential impact of the pandemic response strategies in the Republic of Korea using a mathematical model. METHODS: We developed a deterministic model of a pandemic (H1N1) 2009 in a structured population using the demographic data from the Korean population and the epidemiological feature of the pandemic (H1N1) 2009. To estimate the parameter values for the deterministic model, we used the available data from the previous studies on pandemic influenza. The pandemic response strategies of the Republic of Korea for novel influenza A (H1N1) virus such as school closure, mass vaccination (70% of population in 30 days), and a policy for anti-viral drug (treatment or prophylaxis) were applied to the deterministic model. RESULTS: The effect of two-week school closure on the attack rate was low regardless of the timing of the intervention. The earlier vaccination showed the effect of greater delays in reaching the peak of outbreaks. When it was no vaccination, vaccination at initiation of outbreak, vaccination 90 days after the initiation of outbreak and vaccination at the epidemic peak point, the total number of clinical cases for 400 days were 20.8 million, 4.4 million, 4.7 million and 12.6 million, respectively. The pandemic response strategies of the Republic of Korea delayed the peak of outbreaks (about 40 days) and decreased the number of cumulative clinical cases (8 million). CONCLUSIONS: Rapid vaccination was the most important factor to control the spread of pandemic influenza, and the response strategies of the Republic of Korea were shown to delay the spread of pandemic influenza in this deterministic model.
Summary

Citations

Citations to this article as recorded by  
  • Estimation of optimal antiviral stockpile for a novel influenza pandemic
    Soyoung Kim, Yu Bin Seo, Jacob Lee, Yang Soo Kim, Eunok Jung
    Journal of Infection and Public Health.2022; 15(7): 720.     CrossRef
  • Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India
    Seema Patrikar, Deepti Poojary, D.R. Basannar, D.S. Faujdar, Renuka Kunte
    Medical Journal Armed Forces India.2020; 76(3): 268.     CrossRef
  • Prediction of the Transition From Subexponential to the Exponential Transmission of SARS-CoV-2 in Chennai, India: Epidemic Nowcasting
    Kamalanand Krishnamurthy, Bakiya Ambikapathy, Ashwani Kumar, Lourduraj De Britto
    JMIR Public Health and Surveillance.2020; 6(3): e21152.     CrossRef
  • Mathematical model of transmission dynamics and optimal control strategies for 2009 A/H1N1 influenza in the Republic of Korea
    Soyoung Kim, Jonggul Lee, Eunok Jung
    Journal of Theoretical Biology.2017; 412: 74.     CrossRef
  • A real option analysis for stochastic disease control and vaccine stockpile policy: An application to H1N1 in Korea
    Hojeong Park
    Economic Modelling.2016; 53: 187.     CrossRef
  • Stochastic methods for epidemic models: An application to the 2009 H1N1 influenza outbreak in Korea
    Hyojung Lee, Sunmi Lee, Chang Hyeong Lee
    Applied Mathematics and Computation.2016; 286: 232.     CrossRef
  • Schools’ Response to MERS(MERS-CoV) Outbreak: Schools’ Discretionary Response in Absence of Control Tower
    In Sook Lee, Jae Hee Yoon, Eun Joo Hong, Chae Yoon Kim
    Journal of the Korean Society of School Health.2015; 28(3): 188.     CrossRef
  • The Effects of School Closures on Influenza Outbreaks and Pandemics: Systematic Review of Simulation Studies
    Charlotte Jackson, Punam Mangtani, Jeremy Hawker, Babatunde Olowokure, Emilia Vynnycky, Gerardo Chowell
    PLoS ONE.2014; 9(5): e97297.     CrossRef
  • Uncertainty Quantification in Simulations of Epidemics Using Polynomial Chaos
    F. Santonja, B. Chen-Charpentier
    Computational and Mathematical Methods in Medicine.2012; 2012: 1.     CrossRef
  • Characteristics of Outpatients with Pandemic H1N1/09 Influenza in a Tertiary Care University Hospital in Korea
    Kyung Sun Park, Tae Sung Park, Jin Tae Suh, You Sun Nam, Mi Suk Lee, Hee Joo Lee
    Yonsei Medical Journal.2012; 53(1): 213.     CrossRef
  • Epidemiological Characteristics of Imported Influenza A (H1N1) Cases during the 2009 Pandemic in Korea
    Jun Kil Choi, Sang Won Lee, Bo Youl Choi
    Epidemiology and Health.2012; 34: e2012009.     CrossRef
Timing of Menarche and Physical Growth during Childhood and Adolescence: The Kangwha Study.
Chang Soo Kim, Chung Mo Nam, Duck Hi Kim, Hyun Chang Kim, Kang Hee Lee, Sun Ha Jee, Il Suh
Korean J Prev Med. 2000;33(4):521-529.
  • 2,078 View
  • 30 Download
AbstractAbstract PDF
OBJECTIVES
To assess height, weight and body mass index from childhood to adolescence according to the age at menarche and hence to study the influence of childhood growth on the menarche age. METHODS: "The Kangwha Study" was a community-based prospective cohort study which included the entire population of 219 female first graders in Kangwha county in 1986. Among the 219 girls, 119 girls who had received complete follow up checks during the study period(1986~1997), were included in this study, except one for whom menarche age information was unavailable. The remaining 118 girls were divided into three groups according to the timing of menarche : early(<25 percentile), intermediate and late(> or =75 percentile) maturers. RESULTS: The average age at menarche was 12.7 years : early 11.3 years, intermediate 12.6 years and late 13.7 years. The early maturers were taller and heavier between 6~8 years. But, the mean weight and body mass index at the menarche age did not differ statistically among the three groups. The weight and body mass index of the early maturers were consistently higher than those of the late maturers over the entire period of the study. CONCLUSIONS: Critical body weight and body mass index must be attained for menstruation to be attained and the age at menarche is largely determined by the childhood growth. In addition, it seems that childhood growth and age at menarche are associated with adolescent weight and body mass index.
Summary
Twelve-year Incidence of Hypertension and Its Risk Factors in a Lean Population: the Kangwha Study.
Hyeon Chang Kim, Il Suh, Kang Hee Lee, Sun Ha Jee, Chang Soo Kim, Chung Mo Nam
Korean J Prev Med. 1999;32(4):435-442.
  • 2,140 View
  • 25 Download
AbstractAbstract PDF
OBJECTIVES
The purpose of this study was to examine the twelve-year incidence of hypertension, and to find risk factors for the incidence in adult population in Kangwha County, Korea. METHODS: In 1986, 413 males(mean age 37 years) and 434 females(mean age 33 years) were examined in the Kangwha Study. Among 764 non-hypertensive participants, 164 males and 214 females were reexamined in 1998. Blood pressure(BP) was measured with standard mercury sphygmomanometers. Multiple logistic regression analysis was used to estimate the relative risk of risk factors on the incidence of hypertension. RESULTS: During the 12-year period, 68 of 164 males and 53 of 214 females developed hypertension. In a multiple logistic model adjusted for age and pulse rate, baseline BP, baseline body mass index(BMI) and BMI change during the follow-up period were significantly related to the incidence of hypertension. Adjusted relative risk(RR)s of baseline high-normal BP were 3.90(95% CI: 1.81-7.84) in males, and 12.72(95% CI: 3.70-30.73) in females. Compared with lower baseline BMI group, adjusted RRs of middle baseline BMI group were 2.66(95% CI: 1.19-5.70) in males, and 2.33(95% CI: 0.95-5.55) in females. Adjusted RRs of upper baseline BMI group were 3.52(95% CI: 1.53-7.67)in males and 3.63(95% CI: 1.50-8.43) in females. Increase of BMI was positively related to the incidence in males(adjusted RR=2.71, 95% CI: 1.00-6.71) and females(adjusted RR=3.05, 95% CI: 1.29-6.88). CONCLUSIONS: The twelve-year incidence of hypertension was 41.5% in males, and 25.8% in females. Baseline BP, baseline BMI, and BMI change were strongly related to the incidence of hypertension.
Summary

JPMPH : Journal of Preventive Medicine and Public Health