ArticleSustainable cities and society2021
Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal.Tropical medicine and health · 2025Article
- Marketing Strategies in Nanomaterials for Sensor Applications: Bridging Lab to Market.Global challenges (Hoboken, NJ) · 2025Review
- Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
- Article
- Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020.PloS one · 2024Article
- Global Challenges After a Global Challenge: Lessons Learned from the COVID-19 Pandemic.Advances in experimental medicine and biology · 2024Article
- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- Insights into parents' perceived worry before and during the COVID-19 pandemic in Australia: inequality and heterogeneity of influences.BMC public health · 2023Article
- Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates.ISPRS international journal of geo-information · 2023Article
- Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security.The Science of the total environment · 2023Article
- Understanding the spatial heterogeneity of COVID-19 vaccination uptake in England.BMC public health · 2023Article
- Spatiotemporal pattern of Covid-19 outbreak in Turkey.GeoJournal · 2023Article
- Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania.Letters in spatial and resource sciences · 2023Article
- Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units.Frontiers in public health · 2023Article
- Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities.Transportation research interdisciplinary perspectives · 2023Article
- Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events.Environmental science and pollution research international · 2023Article
- Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties.American journal of preventive medicine · 2022Article
- Deep learning modeling of public's sentiments towards temporal evolution of COVID-19 transmission.Applied soft computing · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R
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