한국의 사회적 거리두기 정책이 코로나19 확진자 및 사망자 수에 미치는 영향
Impact of Social Distancing Policy on COVID-19 Pandemic in South Korea
1
조회
0
다운로드
10
인용
0
참고문헌
키워드
초록
본 연구는 코로나19 대응을 위해 한국 정부가 시행한 사회적 거리두기 정책의 효과를 실증적으로 분석하였다. 분석기간은 2020년 2월 28일부터 백신접종의 영향이 시작되기 이전인 2021년 3월 31일이다. Interrupted Time Series(ITS) 모형을 사용하여 사회적 거리두기 정책이 일별 신규 확진자수와 사망자수의 수준 및 기울기에 미치는 영향을 살펴보았다. 설명변수는 ITS 모형의 기본 변수인 분석일까지의 축적기간, 정책전후 더미변수 뿐만아니라, 계절성을 통제하기 위해 일평균 온도, 월, 요일, 공휴일 더미를 포함하고, 다른 정책 변수(일일 검사수, 마스크정책, GSI), 역의 인과관계 교정을 위해 재생산지수를 포함한다. 전체 샘플, 연령별(20세 단위), 지역별(수도권, 비수도권)로 구분하여 분석한 결과, 사회적 거리두기 정책은 코로나19 신규 확진자 수와 사망자 수의 증가 추세를 감소시키는 효과가 있는 것으로 나타났다. 사회적 거리두기 정책 도입 7일 이후 시점에서 신규 확진자 수 증가세는 제2차 대유행 기간에는 일평균 5.79명, 제3차 대유행 기간에는 36.35명 감소하였다. 연령별로는 40대 이상 중고령층, 지역별로는 수도권에서 정책 효과가 큰 것으로 보인다. 또한 사회적 거리두기 정책 이후 코로나 신규 사망자 수 증가세는 제1차 대유행 기간에는 일평균 0.15명, 제3차 대유행 기간에는 0.78명 감소한 것으로 나타났다.
Abstract
This paper examines the impact of social distancing policy on COVID-19 pandemic in South Korea, using interrupted time series analysis. The study period covers from February 28, 2020 to March 31, 2021 (one month after COVID-19 vaccine dissemination). The dependent variables are daily numbers of newly confirmed case and daily numbers of new death from COVID-19. The explanatory variables include time elapsed since the start of the study, a dummy variable for pre- and post- policy intervention, covariates for controlling seasonality (daily average temperature, month, day of the week and holiday), three other policy variables that could possibly affect the number of newly confirmed case (number of new tests, mask policy, and GSI), and R0 to deal with reverse causality. We analyze the whole sample and two sub-samples by age and region, respectively. Our findings suggest that the social distancing policies in Korea were effective in controlling the increasing trend of daily number of newly confirmed cases (slope decreased by 5.79 for the 2nd wave and 36.35 for the 3rd wave), more so among middle-aged and elderly people and those living in metropolitan areas. Also, the social distancing policies attenuated the increasing rate of COVID-19 death by 0.15 for the 1st wave and 0.78 for the 3rd wave.
참고문헌 (32)
- 권순만, "지속가능한 COVID-19 대응 정책을 위하여", Korean Journal of Public Health, 57(2), pp.25~37 (2020)
- "기상청 자료"[URL]
- 보건복지부, "코로나바이러스10 중앙재난안전대책본부 정례브리핑", ~ (2020)
- 보건복지부, "코로나바이러스10 중앙재난안전대책본부 정례브리핑", ~ (2020)
- 건강위험요인의 사회경제적 비용 연구: 2015-2019년을 대상으로, "이선미", ~ (2021)
- 감염병(코로나) 위기 대응을 위한 보건의료 정책 과제, "이은경", ~ (2021)
- 질병관리청, "코로나바이러스-19 국내 발생 현황", ~ (2020)
- "질병관리청 자료"[URL]
- Alene, Muluneh, "Serial Interval and incubation period of COVID-19 : a systematic review and meta-analysis", BMC Infection Diseases, 21, pp.257~ (2021)
- Testing the validity of the single interrupted time series design, "Baiker, Katherine", NBER, ~, pp.NBER Working Paper 26080 (2019)
- Bernal, James Lopez, "Interrupted Time Series regression for the evaluation of public health interventions : a tutorial", International Journal of Epidemiology, pp.348~355 (2017)
- Covid-19 : Stockpiling antivirals risks repeating Tamiflu mistakes, experts warn, "BMJ", 377~ (2022)
- The Effects of Containment Measures on the COVID19 Pandemic, "Deb, Pragyan", IMF, ~, pp.IMF WP (2020), 20[DOI]
- Guidelines for the implementation of non-pharmaceutical interventions against COVID-19, "ECDC(European Center for Disease and Control)", ~ (2020)
- Emeto, Theophilus, "Evaluation of the effects of border closure on COVID-19 incidence rates across nine African countries: an interrupted time series study", Transactions of the Royal Society of Tropical Medicine and Hygiene, 115, pp.1174~1183 (2021)
- Gerken Jacob, "Comorbidities, sociodemographic factors, and determinants of health on COVID-19 fatalities in the United States", Frontiers in public health, 10 (2022)
- Ulrich Glogowsky, "How effective are social distancing policies? Evidence on the fight against COVID-19", PLOS ONE, 16(9), pp.e0257363~ (2021), Public Library of Science (PLoS)[DOI]
- Impacts of Social Distancing Policy and Vaccination During the COVID-19 Pandemic in the Republic of Korea, "Kim, Kijin", ADB, ~, pp.ADB Economics Working Paper Series No. 659 (2022)
- Koppel Stephen, "Disentangling the Impact of Covid-19 : An Interrupted Time Series Analysis of Crime in New York City", American Journal of Criminal Justice (2021)
- The Impact of China’s Lockdown Policy on the Incidence of COVID-19 : An Interrupted Time Series Analysis, "Molefi Mooketsi", BioMed Research International, ~ (2021)
- "Our World in Data (OWID)"[URL]
- "Oxford Covid-19 Government Response Tracker"[URL]
- Quesada, J. A., "Incubation period of COVID-19 : A systematic review and meta-analysis", Revista Clinica Espanola, 221 (2021)
- Rai Balram, "Incubation period for COVID-19 : a systematic review and meta-analysis", Journal of Public Health: From Theory to Practice (2021)
- Shadish WR, "Experimental and quasi-experimental designs for generalized causal inference, Boston" (2002), Houghton Mifflin
- Siedner, Mark J., "Social Distancing to Slow the U.S. COVID-19 Epidemic: An Interrupted Time-Series Analysis" (2020), PLOS Medicine
- An Estimation of the Economic Costs of Social-Distancing Policies, "Strong Aaron", RAND, ~ (2020)
- Turner Simon L, "Comparison of six statstical methods for interrupted time series studies : empirical evaluation of 190 published series", BMC Medical Research Methodology, 21, pp.134~ (2021)
- Thayer, Winter M., "An Interrupted time series analysis of the lockdown policies in India : a national-level analysis of COVID-19 incidence", Health Policy and Planning, 36, pp.620~629 (2021)
- COVID-19 situation report – 73, "WHO", ~ (2020)
- Wu Yu, "Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains : A systematic review and Meta-analysis", JAMA Network Open, 5(8) (2022)
- Zaki, Nazar, "the estimation of the COVID-19 incubation period : a scoping reviews of the literature", Journal of Infection and Public Health, 14, pp.638~646 (2021)