• Abstract and Figures
• Public Full-text

Journal of Clinical Medicine Article Correlation between the Level of Social Distancing and Activity of Influenza Epidemic or COVID-19 Pandemic: A Subway Use-Based Assessment Hye Seong 1,†, Jin-Wook Hong 2,3,† , Hak-Jun Hyun 1, Jin-Gu Yoon 1, Ji-Yun Noh 1,4, Hee-Jin Cheong 1,4, Woo-Joo Kim 1,4 , Jae-Hun Jung 2,3,* and Joon-Young Song 1,4,* 1 Department of Internal Medicine, Korea University College of Medicine, Guro Hospital, Seoul 08308, Korea; [email protected] (H.S.); [email protected] (H.-J.H.); [email protected] (J.-G.Y.); [email protected] (J.-Y.N.); [email protected] (H.-J.C.); [email protected] (W.-J.K.) 2 Artificial Intelligence and Big-Data Convergence Center, Gachon University College of Medicine and Science, Incheon 21565, Korea; [email protected] 3 Department of Preventive Medicine, Gachon University College of Medicine, Incheon 21565, Korea 4 Asian Pacific Influenza Institute, Seoul 08308, Korea * Correspondence: [email protected] (J.-H.J.); [email protected] (J.-Y.S.); Tel.: +82-2-2626-3052 (J.-Y.S.); Fax: +82-2-2626-1105 (J.-Y.S.) † These authors contributed equally to this work. Abstract: Social distancing is an effective measure to mitigate the spread of novel viral infections in the absence of antiviral agents and insufficient vaccine supplies. Subway utilization density may reflect social activity and the degree of social distancing in the general population.; This study Citation: Seong, H.; Hong, J.-W.; aimed to evaluate the correlations between subway use density and the activity of the influenza Hyun, H.-J.; Yoon, J.-G.; Noh, J.-Y.; epidemic or coronavirus disease 2019 (COVID-19) pandemic using a time-series regression method. Cheong, H.-J.; Kim, W.-J.; Jung, J.-H.; The subway use-based social distancing score (S-SDS) was calculated using the weekly ridership of Song, J.-Y. Correlation between the 11 major subway stations. The temporal association of S-SDS with influenza-like illness (ILI) rates Level of Social Distancing and or the COVID-19 pandemic activity was analyzed using structural vector autoregressive modeling Activity of Influenza Epidemic or and the Granger causality (GC) test. During three influenza seasons (2017–2020), the time-series COVID-19 Pandemic: A Subway Use-Based Assessment. J. Clin. Med. regression presented a significant causality from S-SDS to ILI (p = 0.0484). During the COVID-19 2021, 10, 3369. https://doi.org/ pandemic in January 2020, S-SDS had been suppressed at a level similar to or below the average of the 10.3390/jcm10153369 previous four years. In contrast to the ILI rate, there was a negative correlation between COVID-19 activity and S-SDS. GC analysis revealed a negative causal relationship between COVID-19 and Academic Editor: Venerando Rapisarda S-SDS (p = 0.0098).; S-SDS showed a significant time-series association with the ILI rate but not with COVID-19 activity. When public transportation use is sufficiently suppressed, additional social Received: 22 June 2021 mobility restrictions are unlikely to significantly affect COVID-19 pandemic activity. It would be Accepted: 27 July 2021 more important to strengthen universal mask-wearing and detailed public health measures focused Published: 29 July 2021 on risk activities, particularly in enclosed spaces. Publisher’s Note: MDPI stays neutral Keywords: COVID-19; severe acute respiratory syndrome coronavirus 2; social distancing; subway; with regard to jurisdictional claims in public health published maps and institutional affil- iations. 1. Introduction The severe acute respiratory syndrome coronavirus 2 outbreak has spread worldwide Copyright: © 2021 by the authors. in a short period of time after its first documented emergence in December 2019. The Licensee MDPI, Basel, Switzerland. World Health Organization declared it a pandemic on 11 March 2020 [1]. During the early This article is an open access article distributed under the terms and stages of the coronavirus disease (COVID-19) pandemic, each country conducted different conditions of the Creative Commons public health interventions, which contributed to the disparities pandemic’s incidence and Attribution (CC BY) license (https:// mortality [2,3]. creativecommons.org/licenses/by/ In the absence of effective antiviral agents and sufficient vaccine supplies, nonphar- 4.0/). macologic interventions, especially social distancing, are the essential measures to mitigate J. Clin. Med. 2021, 10, 3369. https://doi.org/10.3390/jcm10153369 https://www.mdpi.com/journal/jcm J. Clin. Med. 2021, 10, 3369 2 of 9 epidemic viral infections, such as COVID-19 and novel influenza [4]. Social distancing measures include school closures, workplace measures/closures, and avoidance of crowd- ing. The government can also ban public gatherings and restrict visits to public places, such as churches, theaters, and gymnastics, to avoid crowding. Since people often move to places of interest using public transportation, including subways and buses, it is usually congested, thereby raising concerns regarding COVID-19 transmission through public transportation [5]. In addition, the presence of a high-speed railway station was associ- ated with the pandemic spread of COVID-19 according to the early studies conducted in China [6]. Because subway use density reflects the social activities including daily commuting, eating out, and personal meeting, it would be a proxy for social distancing. However, irrespective of subway use density, crowding could happen in bars, restaurants, sports events, and any places of interest. Thus, the level of social distancing can be influenced by various factors such as subway usage rate, visit rate to indoor places of interest, and weather (increase in indoor activities). Herein, we evaluated the association between subway use density and the activity of the influenza epidemic or COVID-19 pandemic using the time-series regression method. 2. Materials and Methods 2.1. Study Design and Data Collection In this study, we quantitatively measured social distancing levels using subway ridership for 11 major subway stations and assessed their time-series associations with influenza or COVID-19 pandemic activity. For the influenza activity, weekly influenza-like illness (ILI) rates (cases/1000 patients) were estimated from week 41 of 2017 to week 20 of 2020, using data from the Korea Disease Control and Prevention Agency (KDCA) [7]. The number of weekly domestic COVID-19 cases was counted from week 5 of 2020 to week 5 of 2021 based on the KDCA reports [8]. We estimated the level of social distancing using the subway use-based social distancing score (S-SDS). The subway passenger ridership recorded by five regional metropolitan express transit corporations was collected to determine the S-SDS. 2.2. Subway Use-Based Social Distancing Score From week 41 of 2017 to week 5 of 2021, we identified the ridership of 11 major subway stations in five cities (Seoul: 4, Daejeon: 1, Daegu: 2, Gwangju: 1, and Busan: 3) to calculate the weekly S-SDS (Figure1)[ 9–13]. To calculate S-SDS, we first obtained the average number of weekly passengers for each station during the last four years. Thereafter, we divided the number of weekly subway passengers by the average weekly value to normalize the score, as shown in the following equation: The number of passengers at the subway stations per week Subway use − based SDS = The average number of weekly passengers in the last 4 years J. Clin. Med. 2021, 10, x FOR PEER REVIEW 3 of 9 J. Clin. Med. 2021, 10, 3369 3 of 9 Figure 1. Major subway stations selected to estimate S-SDS. S-SDS was determined based on the number of passengers for Figure 1. Major subway stations selected to estimate S-SDS. S-SDS was determined based on the num- 11 major subway stations in five cities of the Republic of Korea. S-SDS, subway use-based social distancing score. ber of passengers for 11 major subway stations in five cities of the Republic of Korea. S-SDS, subway use-based social distancing score. 2.3. Data Analysis 2.3. Data Analysis The levels of social distancing for influenza or COVID-19 were suggested and statis- tically analyzed using the vector autoregressive (VAR) model, which is a useful tool for The levels of thesocial analysis distancing of multivariate for influe timenza series or COVID-19 [14] and is were especially suggested used to and describe statisti- the dynamic cally analyzed usingbehavior the vector of time autoregressive series and forecasting. (VAR) model, Unlike which the general is a correlationuseful tool function,for the the VAR analysis of multivariatemodel time has appliedseries [14] autocorrelation and is especially as serial used correlation,to describe whichthe dynamic correlates behav- a signal with ior of time series anda lagged forecasting. of itself Unlike as a function the general of lag. correlation This means func thattion, the the VAR VAR model model is instantaneoushas applied autocorrelationinteraction as serial between correlation,Y and X whichthrough correlates the process a signal at different with times. a lagged For of example, itself suppose as a function of lag.both ThisY meansand X are that endogenous; the VAR model in that is case,instantaneous the regressor interaction includes between the current Y value of and X through theendogenous process at variablesdifferent in times. the structural For example, form. Furthermore, suppose both our Y VAR and modelX are doesen- not need covariates due to describing only the dynamic relationships between two time-series as dogenous; in that case, the regressor includes the current value of endogenous variables in S-SDS and ILI or COVID-19 without forecasting. The detailed VAR model is as follows: the structural form. Furthermore, our VAR model does not need covariates due to describ- 2 3 2 3 2 3 2 3 ing only the dynamic relationshipsX1,t betweenC1 two time-series0 as S-SDS and1i ILI Xor1, tCOVID-19−i #1,t j11 ..

Load more

  9

Copy Link