• Journal of Medicine and Life Science
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• v.16 no.3
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• pp.90-95
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• 2019

The Korea Centers for Diseases Control and Prevention interpreted that recent outbreaks of scarlet fever in Korea since 2011 was resulted from the expansion of scarlet fever notification criteria. To suggest a relevant hypothesis regarding this emerging outbreak, a time series analysis(TSA) of scarlet fever incidence between 2002 and 2016 was conducted. The raw data was the nationwide insurance claims database administered by the Korean National Health Insurance Service. The inclusion criteria were children aged ≤14 years residing in Jeju Province, Korea who received any form of healthcare for scarlet fever from 2002 to 2016. The season was defined as winter (December, January, February; Q1), spring (March, April, May; Q2), summer (June, July, August; Q3), and autumn (September, October, November; Q4). There were seasonal variations with showing peak season on Q1 and Q3. And three phases as 2002 Q2~2005 Q2, 2005 Q2~2009 Q4, and 2010 Q1~2016 Q4 were found between 2002 and 2016. The results from TSA suggested that the recent outbreak of scarlet fever among children in Jeju Province might be a phenomenon from 'unknown birth-related environmental factors' changed after 2010.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.1 s.6
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• pp.43-56
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• 2005

This research has examined a time series analysis(TSA) of an every hour traffic information such as occupancy, a traffic flow, and a speed, a statistical model of a surveyed data on the traffic fundamental diagram and an expand aspect of a traffic jam by many Parts of the traffic flow. Based on the detected data from traffic accidents on the Cheonan-Nonsan high way and events when the road volume decreases dramatically like traffic accidents it can be estimated from the change of occupancy right after accidents. When it comes to a traffic jam like events the changing gap of the occupancy and the mean speed is gentle, in addition to a quickness and an accuracy of a detection by the time series analyse of simple traffic index is weak. When it is a stable flow a relationship between the occupancy and a flow is a linear, which explain a very high reliability. In contrast, a platoon form presented by a wide deviation about an ideal speed of drivers is difficult to express by a statical model in a relationship between the speed and occupancy, In this case the speed drops shifty at 6$\~$8$\%$ occupancy. In case of an unstable flow, it is difficult to adopt a statistical model because the formation-clearance Process of a traffic jam is analyzed in each parts. Taken the formation-clearance process of a traffic jam by 2 parts division into consideration the flow having an accident is transferred to a stopped flow and the occupancy increases dramatically. When the flow recovers from a sloped flow to a free flow the occupancy which has increased dramatically decrease gradually and then traffic flow increases according as the result analyzed traffic flow by the multi regime as time series. When it is on the traffic jam the traffic flow transfers from an impeded free flow to a congested flow and then a jammed flow which is complicated more than on the accidents and the gap of traffic volume in each traffic conditions about a same occupancy is generated huge. This research presents a need of a multi-regime division when analyzing a traffic flow and for the future it needs a fixed quantity division and model about each traffic regimes.