Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland

ABSTRACT

The 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.

KEYWORDS:

• COVID-19
• Geographic Information Systems (GIS)
• panel data
• Spatial Durbin Model (SDM)

Data Availability Statement

The authors confirm that the data and the code supporting the findings of this study are available within the article and its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Guangyuan Zhang: Experiment design and data curation, formal analysis, writing original draft; Stefan Poslad: Conceptualization, supervision, writing, reviews and editing; Yonglei Fan: Investigation, visualisation, software resources; Xiaoping Rui: Methodology, supervision, project administration.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10095020.2022.2066576

Additional information

Funding

This research received funding from the National Science and Technology Major Project of the Ministry of Science and Technology of China [grant number 2017YFB0503605], the National Natural Science Foundation of China [grant number 41771478], the Fundamental Research Funds for the Central Universities [grant number 2019B02514], Natural Science Foundation of Beijing, China [grant number 8172046], the China Scholarship Council (CSC), and Queen Mary University of London.

Notes on contributors

Guangyuan Zhang

Guangyuan Zhang received BSc and MSc in Geographical Information Science, from Hefei University of Technology, and University of Chinese Academy of Sciences, China. He received the PhD degree in Computer Science from Queen Mary University of London, UK. His research interest includes Internet of Behaviors and Urban Computing.

Stefan Poslad

Stefan Poslad received the PhD from Newcastle University. He is currently an Associate Professor at Queen Mary University of London, UK, where he heads the IoT Lab. His research interests are Internet of Things, ubiquitous computing, semantic Web, and distributed system management.

Yonglei Fan

Yonglei Fan received BSc and MSc from South China Normal University and University of Chinese Academy of Sciences, China. He is currently pursuing a PhD degree in Computer Science at Queen Mary University of London, UK. His research interests are Geographical Information Science, indoor poisoning technique, human activity recognition and Internet of Things.

Xiaoping Rui

Xiaoping Rui received PhD degree in Cartography and Geographic Information System from the Graduate University of Chinese Academy of Sciences, Beijing, China, in 2004. He is currently a full professor with the School of Earth Sciences and Engineering, Hohai University. His research interests include geographical big data mining, 3D visualization of spatial data, and remote sensing image understanding.