Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States
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....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2023-05-01
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| Series: | Geospatial Health |
| Subjects: | |
| Online Access: | https://geospatialhealth.net/index.php/gh/article/view/1200 |
| _version_ | 1827941186348777472 |
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| author | Chunhui Liu Xiaodi Su Zhaoxuan Dong Xingyu Liu Chunxia Qiu |
| author_facet | Chunhui Liu Xiaodi Su Zhaoxuan Dong Xingyu Liu Chunxia Qiu |
| author_sort | Chunhui Liu |
| collection | DOAJ |
| description | 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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| first_indexed | 2024-03-13T09:33:28Z |
| format | Article |
| id | doaj.art-79be83c3c08a4bfeb034cc332425ce33 |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-03-13T09:33:28Z |
| publishDate | 2023-05-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-79be83c3c08a4bfeb034cc332425ce332023-05-25T18:21:13ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962023-05-0118110.4081/gh.2023.1200Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United StatesChunhui Liu0Xiaodi Su1Zhaoxuan Dong2Xingyu Liu3Chunxia Qiu4College of Geomatics, Xi 'an University of Science and Technology, Xi 'anCollege of Geomatics, Xi 'an University of Science and Technology, Xi 'anSchool of Surveying and Land Information Engineering, Henan Polytechnic University, JiaozuoCollege of Geomatics, Xi 'an University of Science and Technology, Xi 'anCollege of Geomatics, Xi 'an University of Science and Technology, Xi 'anThis 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. https://geospatialhealth.net/index.php/gh/article/view/1200COVID-19spatio-temporal feature analysisIDWspatiotemporal scan statisticsBayesian spatio-temporal modelUSA |
| spellingShingle | Chunhui Liu Xiaodi Su Zhaoxuan Dong Xingyu Liu Chunxia Qiu Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States Geospatial Health COVID-19 spatio-temporal feature analysis IDW spatiotemporal scan statistics Bayesian spatio-temporal model USA |
| title | Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States |
| title_full | Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States |
| title_fullStr | Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States |
| title_full_unstemmed | Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States |
| title_short | Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States |
| title_sort | understanding covid 19 comparison of spatio temporal analysis methods used to study epidemic spread patterns in the united states |
| topic | COVID-19 spatio-temporal feature analysis IDW spatiotemporal scan statistics Bayesian spatio-temporal model USA |
| url | https://geospatialhealth.net/index.php/gh/article/view/1200 |
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