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.
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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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