Food price trends in South Korea through time series analysis

Public Health ◽  
2018 ◽  
Vol 165 ◽  
pp. 67-73 ◽  
Author(s):  
T.H. Kim ◽  
Y. Park ◽  
J. Myung ◽  
E. Han
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
South Korea ◽  
Food Price ◽  
Series Analysis ◽  
Price Trends

scholarly journals Can CO2 emissiovns and energy consumption determine the economic performance of South Korea? A time series analysis

2021 ◽  
Author(s):  
Tomiwa Sunday Adebayo ◽  
Abraham Ayobamiji Awosusi ◽  
Dervis Kirikkaleli ◽  
Gbenga Daniel Akinsola ◽  
Madhy Nyota Mwamba
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Time Series Analysis ◽  
South Korea ◽  
Economic Performance ◽  
Series Analysis

scholarly journals Inter-mortality displacement hypothesis and short-term effect of ambient air pollution on mortality in seven major cities of South Korea: a time-series analysis

2020 ◽  
Author(s):  
Honghyok Kim ◽  
Jong-Tae Lee
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Analysis ◽  
South Korea ◽  
Ambient Air ◽  
Term Effect ◽  
Short Term ◽  
Short Term Effect ◽  
Series Analysis ◽  
All Cause Mortality

Abstract Background Inter-mortality displacement (IMD) between cause-specific mortalities has not been introduced in air pollution epidemiology. Investigation into IMD would provide insights on the actual health burden of air pollution and interpretation of associations. We aimed to investigate IMD regarding short-term effect of air pollution on mortality. Methods We illustrated manifestations and interpretations of lag-mortality associations. If IMD exists, a net increase of one cause-specific death can be offset by a net decrease of other cause-specific deaths. We conducted a time-series analysis to estimate associations of ambient particulate matter smaller than 10 µm (PM10), ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) and carbon monoxide (CO) with mortality, considering lags up to the previous 45 days, for seven major cities of South Korea from 2006 to 2013. Attributable mortality cases were identified. Results For O3, respiratory mortality [11 929 cases, 95% empirical confidence interval (eCI), 5358, 17 688 cases] was counterbalanced by cardiovascular mortality (-11 272 cases, 95% eCI: -22 444, -629 cases). All-cause mortality was 37 148 cases (95% eCI: 4448, 68 782 cases). For PM10, respiratory deaths were 9167 cases (95% eCI: 563, 16 521 cases), and cardiovascular deaths were 6929 cases (95% eCI: -11 793, 24 138 cases). Estimates for SO2 were comparable to those for PM10. All-cause mortality attributable to NO2 was explained by short-term mortality displacement. No associations with mortality were found for CO. Conclusions IMD may exist in the relationship between air pollution and mortality. The actual relationship between air pollution and cause-specific mortality may be masked by IMD.

scholarly journals Factors shaping the COVID-19 epidemic curve: a multi-country analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Su Yeon Jang ◽  
Laith Hussain-Alkhateeb ◽  
Tatiana Rivera Ramirez ◽  
Ahmed Asa’ad Al-Aghbari ◽  
Dhia Joseph Chackalackal ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
South Korea ◽  
Low Income ◽  
Human Mobility ◽  
Virus Transmission ◽  
Interrupted Time Series Analysis ◽  
Epidemic Curve ◽  
Series Analysis ◽  
Containment Measures

Abstract Background Lockdown measures are the backbone of containment measures for the COVID-19 pandemic both in high-income countries (HICs) and low- and middle-income countries (LMICs). However, in view of the inevitably-occurring second and third global covid-19 wave, assessing the success and impact of containment measures on the epidemic curve of COVID-19 and people’s compliance with such measures is crucial for more effective policies. To determine the containment measures influencing the COVID-19 epidemic curve in nine targeted countries across high-, middle-, and low-income nations. Methods Four HICs (Germany, Sweden, Italy, and South Korea) and five LMICs (Mexico, Colombia, India, Nigeria, and Nepal) were selected to assess the association using interrupted time series analysis of daily case numbers and deaths of COVID-19 considering the following factors: The “stringency index (SI)” indicating how tight the containment measures were implemented in each country; and the level of compliance with the prescribed measures using human mobility data. Additionally, a scoping review was conducted to contextualize the findings. Results Most countries implemented quite rigorous lockdown measures, particularly the LMICs (India, Nepal, and Colombia) following the model of HICs (Germany and Italy). Exceptions were Sweden and South Korea, which opted for different strategies. The compliance with the restrictions—measured as mobility related to home office, restraining from leisure activities, non-use of local transport and others—was generally good, except in Sweden and South Korea where the restrictions were limited. The endemic curves and time-series analysis showed that the containment measures were successful in HICs but not in LMICs. Conclusion The imposed lockdown measures are alarming, particularly in resource-constrained settings where such measures are independent of the population segment, which drives the virus transmission. Methods for examining people’s movements or hardships that are caused by covid- no work, no food situation are inequitable. Novel and context-adapted approach of dealing with the COVID-19 crisis are therefore crucial.

Time series analysis of human and bovine brucellosis in South Korea from 2005 to 2010

2013 ◽  
Vol 110 (2) ◽  
pp. 190-197 ◽  
Author(s):  
Hu Suk Lee ◽  
Moon Her ◽  
Michael Levine ◽  
George E. Moore
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
South Korea ◽  
Bovine Brucellosis ◽  
Series Analysis
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