ArticleGeoHealth2021
Modeling the Spatiotemporal Association Between COVID-19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression.
Article in GeoHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Mammography attendance in China's national breast cancer screening program: a county-level analysis of the Fujian province.BMC women's health · 2025Article
- Observational
- An analysis of rodent density patterns and its spatial-temporal correlations using geographically and temporally weighted regression in southeastern China.Frontiers in veterinary science · 2025Article
- Exploring the spatiotemporal effects of meteorological factors on hand, foot and mouth disease: a multiscale geographically and temporally weighted regression study.BMC public health · 2024Article
- Assessing eco-geographic influences on COVID-19 transmission: a global analysis.Scientific reports · 2024Article
- Modeling the Impacts of Governmental and Human Responses on COVID-19 Spread Using Statistical Machine Learning.International journal of digital earth · 2024Article
- Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city.Sustainable cities and society · 2023Article
- COVID-19 spread prediction using socio-demographic and mobility-related data.Cities (London, England) · 2023Article
- Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California.Applied sciences (Basel, Switzerland) · 2023Article
- A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China.Frontiers in public health · 2023Article
- Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19.Sustainable cities and society · 2022Article
- The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2022Review
- Global Distribution, Dispersal Patterns, and Trend of Several Omicron Subvariants of SARS-CoV-2 across the Globe.Tropical medicine and infectious disease · 2022Article
- Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022.Journal of medical virology · 2022Article
- Identifying the Determinants of Distribution ofPathogens (Basel, Switzerland) · 2022Article
- Meteorological Influences on Spatiotemporal Variation of PMInternational journal of environmental research and public health · 2022Article
- Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ongoing Coronavirus Disease 2019 (COVID-19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID-19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID-19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID-19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID-19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra-city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial-temporal-varying relationships between COVID-19 and population mobility. Moreover, the association between COVID-19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio-temporal transmission of COVID-19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk.
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