Analyzing the spatial determinants of local Covid-19 transmission in the United States

Andersen, Lauren M.; Harden, Stella R.; Sugg, Margaret M.; Runkle, Jennifer D.; Lundquist, Taylor E.

doi:10.1016/j.scitotenv.2020.142396

Cited by 174 publications

(189 citation statements)

References 32 publications

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“…Previously, several methodological approaches have evolved to capture the influence of explanatory variables on the response variables in the epidemiological study ( Bashir et al, 2020 ). Among these are Spearman’s, Pearson’s and Kendall’s Correlation Coefficient, Ordinary Least Square regression (Méndez-Arriaga, 2020), Poisson regression, Distributed Lag Nonlinear Model (Runkle et al, 2020), cluster-based analysis ( Andersen, Harden, Sugg, Runkle, & Lundquist, 2021 ), spatial lag model, spatial error model ( Sun et al, 2020 ). These models are mainly global models in nature and therefore have proven ineffective to capture the local or spatial patterns between explanatory and response variables.…”

Section: Discussionmentioning

confidence: 99%

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

121

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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 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 has 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 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 the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R 2 values (for cases, R 2 = 0.961; for deaths, R 2 = 0.962), compared to GWR’s Adj. R 2 values (for cases, R 2 = 0.954; for deaths, R 2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since COVID-19 is not the first time we are facing public health emergency, the findings of the present research therefore could be used as a reference for policy designing and effective decision making.

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Section: Discussionmentioning

confidence: 99%

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Maiti

Zhang

Sannigrahi

et al. 2021

Sustainable Cities and Society

121

View full text Add to dashboard Cite

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“…Using geospatial methods, Anderson et al [ 24 ] analyzed the spatial determinants of local COVID-19 transmission among US counties. Based on cluster analysis, COVID-19 hotspots were predominant in New England and the US’s Southeast and Southwest regions.…”

Section: Determinants Of Covid-19 Disparitymentioning

confidence: 99%

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis

Iyanda

Boakye

et al. 2021

J. Racial and Ethnic Health Disparities

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The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors—rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties ( M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as “completely rural” (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and “mostly rural” (IRR = 1.26; 95% CI: 1.15-1.38) relative to “mostly urban” counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health. Supplementary Information The online version contains supplementary material available at 10.1007/s40615-021-01006-7.

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“…Within environmental and sustainability science, new and relatively unexplored topics are continually emerging, such as factors determining the diffusion of novel coronavirus disease 2019 (COVID- 19), which generates a severe respiratory disorder and is causing the deaths of many individuals worldwide [1][2][3]. Manifold studies suggest a possible relation between unsustainable environments with high air pollution and the spread of COVID-19 [3][4][5].…”

Section: Introduction and Related Workmentioning

confidence: 99%

How (Un)sustainable Environments Are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy

Coccia

2020

Sustainability

122

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The pandemic caused by novel coronavirus disease 2019 (COVID-19) is generating a high number of cases and deaths, with negative effects on public health and economic systems. One of the current questions in the contemporary environmental and sustainability debate is how high air pollution and reduced use of renewable energy can affect the diffusion of COVID-19. This study endeavors to explain the relation between days of air pollution, wind resources and energy, and the diffusion of COVID-19 to provide insights into sustainable policy to prevent future epidemics. The statistical analysis here focuses on a case study of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. The results reveal two main findings: (1) cities with high wind speed and high wind energy production have a lower number of cases of COVID-19 in the context of a more sustainable environment; (2) cities located in hinterland zones with high air pollution, low wind speed and less wind energy production have a greater number of cases and total deaths. The results presented here suggest that the pandemic caused by novel coronavirus (SARS-CoV-2) and future epidemics similar to COVID-19 cannot be solved only with research in medicine but the solution also needs advanced capabilities and technologies for supporting sustainable development based on the reduction of air pollution and increase of production in renewable energy to improve air quality and as a consequence public health.

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Analyzing the spatial determinants of local Covid-19 transmission in the United States

Cited by 174 publications

References 32 publications

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis

How (Un)sustainable Environments Are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy

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