Understanding Travel Behavior Change during COVID-19 Using Spatio-temporal Cluster Analysis

다국어 초록 (Multilingual Abstract)

As COVID-19 has been prevalent around the world in recent years, many studies about monitoring and predicting the spread of disease have been conducted in various fields including geography. However, little research has been devoted to infectious disease prediction modeling that adopts constantly changing travel behavior patterns during epidemics. This is due to the limited methodologies to investigate spatio-temporal change in travel behaviors at large-scale and the difficulty in interpreting massive and diverse travel patterns. This study suggests an effective disease surveillance method based on cluster analysis to identify change in travel behaviors during the pandemic by implementing space-time cluster analysis. The results show that K-means++ well represent dynamic changes in travel behaviors at daily scale, whereas retrospective space-time scan statistics have the advantage of detecting travel behavior changes in each period at large spatial scale. Those results could inform decision makers to establish guidelines on travel behavior to curb individual contacts under potential future pandemic.

참고문헌 (Reference)

1 박진우 ; 최문기 ; 강전영, "코로나-19 팬데믹 시대의 사회적 거리두기 단계에 따른 대규모 스포츠 경기장 좌석 배치의 공간적 최적화" 대한지리학회 56 (56): 53-66, 2021

2 안재성 ; 최진무 ; 이상원 ; 함정수, "시공간 자료 지리적 시각화를 통한 코로나바이러스감염증-19 확진자 시공간 이동경로 분석에 관한 연구" 한국지도학회 20 (20): 13-22, 2020

3 KCDC (Korea Centers for Disease Control and Prevention), "Weekly Updates for Countries with Major Outbreaks"

4 Nakaya, T., "Visualising crime clusters in a space-time cube : An exploratory data-analysis approach using space-time kernel density estimation and scan statistics" 14 (14): 223-239, 2010

5 Coleman, M., "Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes" 8 (8): 1-6, 2009

6 Kai, D., "Universal masking is urgent in the COVID-19pandemic: SEIR and agent based models, empirical validation, policy recommendations. arXiv preprint arXiv:2004.13553"

7 Huang, X., "Twitter reveals human mobility dynamics during the COVID-19 pandemic" 15 (15): e0241957-, 2020

8 Junghwan Kim, "The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from March to September of 2020" Elsevier BV 93 : 103039-, 2021

9 CDC, "Symptoms of COVID-19, CS-334259-A"

10 Juhász, L., "Studying spatial and temporal visitation patterns of points of interest using SafeGraph data in Florida" Florida International University, FIU digital commons 2020

11 Engle, S., "Staying at home:Mobility effects of covid-19. Available at SSRN 3565703"

12 Kim, S., "Spatiotemporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020)" 98 : 328-333, 2020

13 Mao, L., "Spatial–temporal transmission of influenza and its health risks in an urbanized area" 34 (34): 204-215, 2010

14 Zhu, X., "Spatially explicit modeling of 2019-nCoV epidemic trend based on mobile phone data in mainland China"

15 Hohl, A., "Spatial distribution of hateful tweets against Asians and Asian Americans during the COVID-19 pandemic, November 2019 to May 2020" 112 (112): 646-649, 2022

16 Sami Ullah, "Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)" PAGEPress Publications 16 (16): 2021

17 Ballesteros, P., "Spatial and spatiotemporal clustering of the COVID-19pandemic in Ecuador" 69 (69): 2021

18 Chiou, L, "Social distancing, internet access and inequality" National Bureau of Economic Research 2020

19 Xiong, H., "Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model. Available at SSRN 3537083"

20 Bahmani, B., "Scalable K-means++"

21 Kulldorff, M, "SaTScanTM user guide for version 9.6, 2018" Department of Medicine, Harvard Medical School

22 Owusu, C., "Residential mobility impacts relative risk estimates of space-time clusters of chlamydia in Kalamazoo County, Michigan" 14 (14): 2019

23 Nouvellet, P., "Reduction in mobility and COVID-19 transmission" 12 (12): 1-9, 2021

24 M.R. Desjardins, "Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters" Elsevier BV 118 : 102202-, 2020

25 Hohl, A., "Rapid detection of COVID-19 clusters in the United States using a prospective space-time scan statistic : An update" 12 (12): 27-33, 2020

26 Kulldorff, M., "Prospective time periodic geographical disease surveillance using a scan statistic" 164 (164): 61-72, 2001

27 Hernández-Flores, M. D. L. L., "Prediction and potential spatially explicit spread of COVID-19 in Mexico’s Megacity North Periphery" 8 (8): 453-, 2020

28 SafeGraph, "Places Schema"

29 Gao, S., "Online GIS services for mapping and sharing disease information" 7 (7): 1-12, 2008

30 Zifeng Yang, "Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions" AME Publishing Company 12 (12): 165-174, 2020

31 Stephen Eubank, "Modelling disease outbreaks in realistic urban social networks" Springer Science and Business Media LLC 429 (429): 180-184, 2004

32 Mao, L., "Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—an agent-based simulation" 50 : 31-39, 2014

33 Warren, M. S., "Mobility changes in response to COVID-19"

34 Gao, S., "Mapping county-level mobility pattern changes in the United States in response to COVID-19" 12 (12): 16-26, 2020

35 Borkowski, P., "Lockdowned : Everyday mobility changes in response to COVID-19" 90 : 102906-, 2021

36 Vassilvitskii, S., "K-means++: The advantages of careful seeding" 1027-1035, 2006

37 Choi, M, "Investigating spatiotemporal indoor contact patterns using ABM and STKDE" 1-8, 2021

38 Reynolds, D. A., "Gaussian mixture models" 741 : 659-663, 2009

39 Irawan, M. Z., "Exploring activity-travel behavior changes during the beginning of COVID-19 pandemic in Indonesia" 49 (49): 529-553, 2022

40 Kulldorff, M., "Evaluating Cluster Alarms : A Spacetime Scan Statistic and Brain Cancer in Los Alamos" 88 (88): 1377-1380, 1998

41 Peng, L., "Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:2002.06563"

42 Li, Q., "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia" 382 : 1199-1207, 2020

43 Androniceanu, A., "E-Government clusters in the EU based on the Gaussian mixture models" 35 : 6-20, 2020

44 Sharon K. Greene, "Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June–July 2020" Centers for Disease Control and Prevention (CDC) 27 (27): 1500-, 2021

45 Alexander Hohl, "Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States" Elsevier BV 34 : 100354-, 2020

46 Bock, H.H., "Clustering methods: A history of K-means algorithms. Selected Contributions in Data Analysis and Classification"

47 Qiujie Shi, "Changes in population movement make COVID-19 spread differently from SARS" Elsevier BV 255 : 113036-, 2020

48 Bouman, C. A., "CLUSTER: An unsupervised algorithm for modeling Gaussian mixtures"

49 Badr, H. S., "Association between mobility patterns and COVID-19 transmission in the USA : a mathematical modelling study" 20 (20): 1247-1254, 2020

50 Liang, Y, "Assessing the validity of SafeGraph data for visitor monitoring in Yellowstone National Park"

51 Bhin, M. Y., "Analysis of the change in bus use pattern due to COVID-19" Gyeoggi Research Institute 1-124, 2021

52 Kulldorff, M., "An elliptic spatial scan statistic" 25 (25): 3929-3943, 2006

53 Berger, D. W, "An SEIR infectious disease model with testing and conditional quarantine" National Bureau of Economic Research 2020

54 Kulldorff, M., "A spatial scan statistic" 26 (26): 1481-1496, 1997

55 Kulldorff, M., "A space–time permutation scan statistic for disease outbreak detection" 2 (2): 216-224, 2005

56 Tango, T., "A flexibly shaped spatial scan statistic for detecting clusters" 4 (4): 1-15, 2005

57 Bernasconi, A., "A conceptual model for geo-online exploratory data visualization : The case of the covid-19 pandemic" 12 (12): 69-, 2021

58 Kim, Y. L., "A big data analysis of floating population network in the Seoul Metropolitan Area in the COVID era" Gyeonggi Res. Inst 1-76, 2021