The role of built and social environmental factors in Covid-19 transmission: A look at America’s capital city

Hu, Ming; Roberts, Jennifer D.; Azevedo, Gesine Pryor; Milner, David

doi:10.1016/j.scs.2020.102580

Cited by 123 publications

(112 citation statements)

References 35 publications

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“…To inform the policy-makers at both national and state levels, understanding the explanatory drivers and related confounding factors with spatial patterns and is of paramount importance. Timely studies have done much work of doing so (e.g., Beria & Lunkar, 2020 ; Hu, Roberts, Azevedo, & Milner, 2020 ; Rahman et al, 2020 ). However, this may not uncover the full picture since most of the factors change over time, namely being time-variant 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

109

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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

109

View full text Add to dashboard Cite

show abstract

“…These obstacles have generally been defined as “social determinants of health” or the conditions that negatively or positively impact birth, growth, living, learning, working, playing, and aging ( Roberts and Tehrani, 2020 ). As illustrated in the COVID-19 determinants of health model ( Figure 1 ), an adaptation of the Kaiser Family Foundation model, COVID-19 risk can be attributed to both individual and social determinants of health ( Artiga, 2020 ; Hu and Roberts, 2020 ). Specifically, a social determinant amalgamation can move along a cascade of harmful circumstances positioned in economic (e.g.…”

Section: Introductionmentioning

confidence: 99%

Clinicians, cooks, and cashiers: Examining health equity and the COVID-19 risks to essential workers

et al. 2020

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In Spring/Summer 2020, most individuals living in the United States experienced several months of social distancing and stay-at-home orders because of the coronavirus (COVID-19) pandemic. Clinicians, restaurant cooks, cashiers, transit operators, and other essential workers (EWs), however, continued to work outside the home during this time in order to keep others alive and maintain a functioning society. In the United States, EWs are often low-income persons of color who are more likely to face socioeconomic vulnerabilities, systemic racism, and health inequities. To assess the various impacts of COVID-19 on EWs, an online survey was distributed to a representative sample of individuals residing in six states during May/June 2020. The sample included 990 individuals who identified as EWs and 736 nonessential workers (NWs). We assessed differences between EW and NW respondents according to three categories related to health equity and social determinants of health: (1) demographics (e.g. race/ethnicity); (2) COVID-19 exposure risk pathways (e.g. ability to social distance); and (3) COVID-19 risk perceptions (e.g. perceived risk of contracting COVID-19). EWs were more likely to be Black or Hispanic than NWs and also had lower incomes and education levels on average. Unsurprisingly, EWs were substantially more likely to report working outside the home and less likely to report social distancing and wearing masks indoors as compared to NWs. EWs also perceived a slightly greater risk of contracting COVID-19. These findings, which we discuss in the context of persistent structural inequalities, systemic racism, and health inequities within the United States, highlight ways in which COVID-19 exacerbates existing socioeconomic vulnerabilities faced by EWs.

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“… Leng, Wang, and Liu (2020) developed a numerical strategy to optimize the physical environment in a courtyard, considering the severe impact of COVID-19. Hu, Roberts, Azevedo, and Milner (2020) investigated the multifaceted interrelationships between the built and social environments and assessed their influence on population-level health during the COVID-19 epidemic.…”

Section: Discussionmentioning

confidence: 99%

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

Guo

Qian

Sun

et al. 2021

Sustainable Cities and Society

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The role of built and social environmental factors in Covid-19 transmission: A look at America’s capital city

Cited by 123 publications

References 35 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

Clinicians, cooks, and cashiers: Examining health equity and the COVID-19 risks to essential workers

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

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