https://doi.org/10.4081/gh.2023.1200
Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States
Submitted: 25 March 2023
Accepted: 30 April 2023
Published: 25 May 2023
Accepted: 30 April 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.
Anaele BI, Doran C, McIntire R, 2021. Visualizing COVID-19 mortality rates and African-American populations in the USA and Pennsylvania. J Racial Ethn Health Disparities 1:1356–63.
DOI: https://doi.org/10.1007/s40615-020-00897-2
CDC, 2022. United States COVID-19 Cases and Deaths by State over Time - ARCHIVED (version date: October 19, 2022)
Chen X, Yang Y, Zhang J, 2020. Mapping the spatiotemporal dynamics of the COVID-19 epidemic in Wuhan, China. IEEE Trans Med Imaging 39:2215-20.
Costa MA, Kulldorff M, 2014. Maximum linkage space-time permutation scan statistics for disease outbreak detection. Int. J. Health Geogr 13:1-14.
DOI: https://doi.org/10.1186/1476-072X-13-20
Cuadros DF, Branscum AJ, Mukandavire Z, 2021. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States. Ann. Epidemiol 59:16-20.
DOI: https://doi.org/10.1016/j.annepidem.2021.04.007
Hohl A, Delmelle EM, Desjardins M-R, 2020. Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. Spat Spatiotemporal Epidemiol 34:100354.
DOI: https://doi.org/10.1016/j.sste.2020.100354
Hu T, Li R, Chen J, 2018. Bayesian spatiotemporal modeling for small area estimation of disease prevalence using surveillance data with missing values. Spat Spatiotemporal Epidemiol 24:1-10.
Huang Y, Yang S, Zou Y, 2022. Spatiotemporal epidemiology of COVID-19 from an epidemic course perspective. Geospat Health 17;1023.
DOI: https://doi.org/10.4081/gh.2022.1023
Huang J, Gao X, Chen Y, Shi X, 2020. Spatiotemporal dynamics and determinants of COVID-19 transmission in China. Int J Infect Dis 97:321-7.
Huang L, Tian D, Liu Y, Lin Z, Kang D, Xu J, 2020. Spatial-temporal analysis of COVID-19 and meteorological factors in Hubei Province from January to April 2020. Sci Total Environ 754;142289.
Islam A, Sayeed M-A, Rahman M-K,2021. Geospatial dynamics of COVID‐19 clusters and hotspots in Bangladesh. Transbound Emerg Dis 68:3643-57.
DOI: https://doi.org/10.1111/tbed.13973
Jackson SL, Derakhshan S, Blackwood L, 2021. Spatial disparities of COVID-19 cases and fatalities in United States counties. IJERPH 18:8259.
DOI: https://doi.org/10.3390/ijerph18168259
Jaya IGNM, Andriyana Y, Tantular B, 2022. Post-pandemic COVID-19 estimated and forecasted hotspots in the Association of Southeast Asian Nations (ASEAN) countries in connection to vaccination rate. Geospat Health 17:1070.
DOI: https://doi.org/10.4081/gh.2022.1070
Jia L, Hu T, Wu J, Xu J, Xu X, 2020. Spatiotemporal dynamics and influencing factors of COVID-19 spread in China. Sci Rep 10:1-12.
Khan FM, Kumar A, Puppala H, 2021. Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods. JSSR 2:50-62.
DOI: https://doi.org/10.1016/j.jnlssr.2021.05.001
Kulldorff M,1997. A spatial scan statistic. Commun Star-Theor M 26:1481-96.
DOI: https://doi.org/10.1080/03610929708831995
Li Y, Zhang H, Zhang X, Huang Y, Pan H, 2016. Comparison of spatial-temporal scan analysis and circular distribution method in exploring the clustering of hand-foot-mouth disease. Disease Surveillance 31:638-641.
Li L, Yang Q, Sun H, Li X, 2021. Spatiotemporal analysis of the COVID-19 pandemic in China using a Bayesian spatiotemporal modelling approach. IJERPH 18:3108.
Li Y, Li Z, Li Y, Li W, 2020. Analysis of the COVID-19 epidemic using spatial autocorrelation: the case of China. Int J Infect Dis 95:391-398.
Liu W, Lei Y, Chen J, 2011. Spatiotemporal spread pattern of the COVID-19 outbreak in China: an analysis based on geographic information system and Google Trends. IJERPH 17:4235.
Liu Y, Yang D, Dong G, 2020. Spatiotemporal diffusion characteristics of COVID-19 outbreak and population mobility risk assessment in Henan Province: An analysis based on 1,243 cases report. Econ Geogr 40:24-32.
Lv Z, Cheng S, 2020. Spatiotemporal characteristics of COVID-19 outbreak in Hubei Province based on Crystal Ball and GIS. J Cent Chin Nor Univ 54:1059-71.
Mollalo A, Vahedi B, Rivera K-M, 2020. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ 728:138884.
DOI: https://doi.org/10.1016/j.scitotenv.2020.138884
Murugesan B, Karuppannan S, Mengistie A-T, 2020. Distribution and trend analysis of COVID-19 in India: geospatial approach. J Geogr Sci 4:1-9.
DOI: https://doi.org/10.21523/gcj5.20040101
Nevitte N, 2017. The North American Trajectory: Cultural, Economic, and Political Ties among the United States, Canada and Mexico. Routledge. ì
Pei S, Yamana T-K, Kandula S, 2021. Burden and characteristics of COVID-19 in the United States during 2020. Nature 598:338-41.
DOI: https://doi.org/10.1038/s41586-021-03914-4
Peng L, Yang W, Zhang D, Zhuge C, Hong L, 2020. Epidemic analysis of COVID-19 in China by dynamical modeling. Math Probl Eng. Available from: https://arxiv. org/pdf/2002.06563.
DOI: https://doi.org/10.1101/2020.02.16.20023465
Rehman A, Zia R, Naeem M-A, 2018. Mapping and predicting malaria using spatiotemporal modeling: A case study of district Swat, Pakistan. PLoS One 13:e0207605.
Sun Z, Thibodeaux QG, 2021. The spatiotemporal dynamics of COVID-19 transmission in China and its influence on the global outbreak. PLoS One 16:e0245559.
Wadhera R-K, Shen C, Gondi S, 2021. Cardiovascular deaths during the COVID-19 pandemic in the United States. J Am Coll Cardiol 77:159-69.
DOI: https://doi.org/10.1016/j.jacc.2020.10.055
Wang J, Tang K, Feng K, Lv W, 2020. Spatiotemporal pattern analysis and risk assessment of COVID-19 in mainland China: a case study in Henan Province. Sci Total Environ 728:138796.
DOI: https://doi.org/10.1016/j.scitotenv.2020.138915
Wang Q, Wang X, Lin X, Zhang C, 2020. A spatiotemporal analysis of the COVID-19 pandemic in China using geographic information systems. Int J Cardlol 19:1-11.
Wang Z, Liu C, Wang Z, 2020. COVID-19 epidemic: disease characteristics in children. J Med Virol 92:747-54.
DOI: https://doi.org/10.1002/jmv.25807
Wang Z, Ma W, Zheng X, 2020. Spatial-temporal analysis of COVID-19 epidemic in China: A case study of 33 provinces, autonomous regions and municipalities. J Geogr Sci 30:1399-412.
Wei X, Xiong L, Wan M, Liu P, 2009. Application of improved generalized likelihood uncertainty estimation method based on Markov chain Monte Carlo algorithm in basin hydrological model. J Hydraul Eng 40:464-473+480.
Xu M, Cao C, Zhang X, 2021. Fine-scale space-time cluster detection of COVID-19 in Mainland China using retrospective analysis. IJERPH 18:3583.
DOI: https://doi.org/10.3390/ijerph18073583
Yin F, Feng Z, Li X, 2007. An early warning system for infectious diseases based on prospective space-time permutation scan statistic. J Health Res 36:455-8.
Yin F, Li X, Feng Z,2009. Real-time monitoring and early warning of infectious diseases based on a network reporting system and spatial-temporal clustering detection. Chin J Pre Med 36:2204-07.
Zhang W, Li C, Ji G, Shi J, Ma Y, Zhang S, 2012. Application of retrospective space-time permutation scan statistic in the study of hand-foot-mouth disease clustering. CJDCP 16:73-76.
Zhang Y, Zheng L, Liu L., Zhao S, 2020. Investigation of the spatiotemporal distribution of COVID-19 cases in China using geographic information system and spatial autocorrelation analysis: implications for risk evaluation and management of public health emergencies. Sci Total Environ 739:140033.
Zheng JL, Zhang F, Du Z,2018. Multi-scale spatio-temporal characteristics analysis of infectious diseases--Taking gonorrhea, bacillary dysentery and mumps in Hangzhou as an example. Zhejiang Da Xue Xue Bao Li Xue Ban 45:605-616.
Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, Cao Z, Wang J, Yuan W, Zhu Y, Song C, Chen J, Xu J, Li F, Ma T, Jiang L, Yan F, Yi J, Hu Y, Liao Y, Xiao H, 2020. COVID-19: challenges to GIS with big data. Geography and Sustainability 1:77-87.
DOI: https://doi.org/10.1016/j.geosus.2020.03.005
How to Cite
Liu, C., Su, X. ., Dong, Z., Liu, X., & Qiu, C. (2023). Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1200
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