Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea
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OBJECTIVES: The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator—namely, the number of close contacts per patient with confirmed COVID-19. METHODS: From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data’s characteristics. RESULTS: In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults. CONCLUSIONS: The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.
Abstract
OBJECTIVES: The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator—namely, the number of close contacts per patient with confirmed COVID-19. METHODS: From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data’s characteristics. RESULTS: In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults. CONCLUSIONS: The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.
참고문헌 (39)
- Zhou P, "A pneumonia outbreak associated with a new coronavirus of probable bat origin", Nature, 579, pp.270~273 (2020)
- Jin Yong Kim, "The First Case of 2019 Novel Coronavirus Pneumonia Imported into Korea from Wuhan, China: Implication for Infection Prevention and Control Measures", Journal of Korean Medical Science, 35(5), pp.1~4 (2020), 대한의학회[DOI]
- "Cases in Korea" (2022)[URL]
- "Resident population by city, country, and district" (2022)[URL]
- Delamater PL, "Complexity of the basic reproduction number(R0)", Emerg Infect Dis, 25, pp.1~4 (2019)
- Dietz K, "The estimation of the basic reproduction number for infectious diseases", Stat Methods Med Res, 2, pp.23~41 (1993)
- Yousef Alimohamadi, "Estimate of the Basic Reproduction Number for COVID-19: A Systematic Review and Meta-analysis", 예방의학회지, 53(3), pp.151~157 (2020), 대한예방의학회[DOI]
- Jing QL, "Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China : a retrospective cohort study", Lancet Infect Dis, 20, pp.1141~1150 (2020)
- Gatalo O, "Associations between phone mobility data and COVID-19 cases", Lancet Infect Dis, 21, pp.e111~ (2021)
- Muñoz-Cancino R, "Non-intrusive assessment of COVID-19 lockdown follow-up and impact using credit card information : case study in Chile", Int J Environ Res Public Health, 18, pp.5507~ (2021)
- Gao S, "Association of mobile phone location data indications of travel and stay-at-home mandates with COVID-19 infection rates in the US", JAMA Netw Open, 3, pp.e2020485~ (2020)
- Badr HS, "Association between mobility patterns and COVID-19 transmission in the USA : a mathematical modelling study", Lancet Infect Dis, 20, pp.1247~1254 (2020)
- Grantz KH, "The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology", Nat Commun, 11, pp.4961~ (2020)
- Wesolowski A, "Connecting mobility to infectious diseases : the promise and limits of mobile phone data", J Infect Dis, 214(suppl_4), pp.S414~S420 (2016)
- "Coronavirus disease 2019 response guidelines (for local governments)" (2021)[URL]
- "Press release: COVID-19"[URL]
- Quaife M, "The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements", BMC Med, 18, pp.316~ (2020)
- Jarvis CI, "Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK", BMC Med, 18, pp.124~ (2020)
- Aquino EM, "Social distancing measures to control the COVID-19 pandemic : potential impacts and challenges in Brazil", Cien Saude Colet, 25(suppl 1), pp.2423~2446 (2020)
- Wellenius GA, "Impacts of social distancing policies on mobility and COVID-19 case growth in the US", Nat Commun, 12, pp.3118~ (2021)
- Thunström L, "The benefits and costs of using social distancing to flatten the curve for COVID-19", J Benefit Cost Anal, 11, pp.179~195 (2020)
- Cohen J, "Countries test tactics in ‘war’ against COVID-19", Science, 367, pp.1287~1288 (2020)
- Kraemer MU, "The effect of human mobility and control measures on the COVID-19 epidemic in China", Science, 368, pp.493~497 (2020)
- Matrajt L, "Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease", Emerg Infect Dis, 26, pp.1740~1748 (2020)
- Social Distancing and Contagion in a Discrete Choice Model of COVID-19, "Baskozos, Giorgos", CAMA, ~, pp.CAMA Working Paper No. 35 (2020), 2020
- Hadjidemetriou GM, "The impact of government measures and human mobility trend on COVID-19 related deaths in the UK", Transp Res Interdiscip Perspect, 6, pp.100167~ (2020)
- Jiang J, "Influence of population mobility on the novel coronavirus disease(COVID-19)epidemic : based on panel data from Hubei, China", Glob Health Res Policy, 5, pp.30~ (2020)
- Seong H, "Comparison of the second and third waves of the COVID-19 pandemic in South Korea : importance of early public health intervention", Int J Infect Dis, 104, pp.742~745 (2021)
- Brooks SK, "The psychological impact of quarantine and how to reduce it : rapid review of the evidence", Lancet, 395, pp.912~920 (2020)
- Altena E, "Dealing with sleep problems during home confinement due to the COVID-19 outbreak : practical recommendations from a task force of the European CBT-I Academy", J Sleep Res, 29, pp.e13052~ (2020)
- Kim JH, "Looking beyond COVID-19 vaccine phase 3 trials", Nat Med, 27, pp.205~211 (2021)
- Lipsitch M, "Understanding COVID-19 vaccine efficacy", Science, 370, pp.763~765 (2020)
- Gemmetto V, "Mitigation of infectious disease at school : targeted class closure vs school closure", BMC Infect Dis, 14, pp.695~ (2014)
- Stehlé J, "High-resolution measurements of face-to-face contact patterns in a primary school", PLoS One, 6, pp.e23176~ (2011)
- Zagheni E, "Using time-use data to parameterize models for the spread of close-contact infectious diseases", Am J Epidemiol, 168, pp.1082~1090 (2008)
- Del Valle SY, "Mixing patterns between age groups in social networks", Soc Networks, 29, pp.539~554 (2007)
- "Coronavirus disease 2019 guidelines for vaccine completion" (2022)[URL]
- McAloon CG, "Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies", BMC Public Health, 21, pp.2238~ (2021)
- Crawford FW, "Impact of close interpersonal contact on COVID-19 incidence : evidence from 1 year of mobile device data", Sci Adv, 8, pp.eabi5499~ (2022)
