Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland
ABSTRACTThe coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our underst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-10-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2022.2066576 |
| _version_ | 1797310806538321920 |
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| author | Guangyuan Zhang Stefan Poslad Yonglei Fan Xiaoping Rui |
| author_facet | Guangyuan Zhang Stefan Poslad Yonglei Fan Xiaoping Rui |
| author_sort | Guangyuan Zhang |
| collection | DOAJ |
| description | ABSTRACTThe coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread, which can help in maintaining urban health within a county and between counties within a country. This distribution can be computed using the Volunteered Geographic Information (VGI) of the citizens in conjunction with other variables, such as climatic conditions, and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission. Based on the estimated population density, when the population density increases daily by 1 person/km2 in a county or prefectural-level administrative unit with an average size of 26,000 km2, the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days, which is the illness onset time for a new COVID-19 case. After 14 days, which is the maximum incubation period of the COVID-19 virus, there would be 5 new confirmed cases and 0.092 death cases. However, in neighboring regions, there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities. The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic. Additionally, the direct and indirect effects of the impact are estimated using spatial panel models. The models that control the unobserved factors improve the reliability of the estimation, as validated by random experiments and the use of the Baidu migration dataset. |
| first_indexed | 2024-03-08T01:48:24Z |
| format | Article |
| id | doaj.art-c6cc7c1818c143faa462a78e61f73847 |
| institution | Directory Open Access Journal |
| issn | 1009-5020 1993-5153 |
| language | English |
| last_indexed | 2024-03-08T01:48:24Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj.art-c6cc7c1818c143faa462a78e61f738472024-02-14T12:14:20ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532023-10-0126464266310.1080/10095020.2022.2066576Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainlandGuangyuan Zhang0Stefan Poslad1Yonglei Fan2Xiaoping Rui3IoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UKIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UKIoT Laboratory, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UKSchool of Earth Sciences and Engineering, Hohai University, Nanjing, ChinaABSTRACTThe coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread, which can help in maintaining urban health within a county and between counties within a country. This distribution can be computed using the Volunteered Geographic Information (VGI) of the citizens in conjunction with other variables, such as climatic conditions, and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission. Based on the estimated population density, when the population density increases daily by 1 person/km2 in a county or prefectural-level administrative unit with an average size of 26,000 km2, the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days, which is the illness onset time for a new COVID-19 case. After 14 days, which is the maximum incubation period of the COVID-19 virus, there would be 5 new confirmed cases and 0.092 death cases. However, in neighboring regions, there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities. The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic. Additionally, the direct and indirect effects of the impact are estimated using spatial panel models. The models that control the unobserved factors improve the reliability of the estimation, as validated by random experiments and the use of the Baidu migration dataset.https://www.tandfonline.com/doi/10.1080/10095020.2022.2066576COVID-19Geographic Information Systems (GIS)panel dataSpatial Durbin Model (SDM) |
| spellingShingle | Guangyuan Zhang Stefan Poslad Yonglei Fan Xiaoping Rui Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland Geo-spatial Information Science COVID-19 Geographic Information Systems (GIS) panel data Spatial Durbin Model (SDM) |
| title | Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland |
| title_full | Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland |
| title_fullStr | Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland |
| title_full_unstemmed | Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland |
| title_short | Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland |
| title_sort | quantitative spatiotemporal impact of dynamic population density changes on the covid 19 pandemic in china s mainland |
| topic | COVID-19 Geographic Information Systems (GIS) panel data Spatial Durbin Model (SDM) |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2022.2066576 |
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