FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203984103> ?p ?o ?g. }

• W3203984103 endingPage "150536" @default.
• W3203984103 startingPage "150536" @default.
• W3203984103 abstract "The coronavirus disease 2019 (COVID-19) has had a global impact that has been unevenly distributed among and even within countries. Multiple demographic and environmental factors have been associated with the risk of COVID-19 spread and fatality, including age, gender, ethnicity, poverty, and air quality among others. However, specific contributions of these factors are yet to be understood. Here, we attempted to explain the variability in infection, death, and fatality rates by understanding the contributions of a few selected factors. We compared the incidence of COVID-19 in New York State (NYS) counties during the first wave of infection and analyzed how different demographic and environmental variables associate with the variation observed across the counties. We observed that infection and death rates, two important COVID-19 metrics, to be highly correlated with both being highest in counties located near New York City, considered as one of the epicenters of the infection in the US. In contrast, disease fatality was found to be highest in a different set of counties despite registering a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on COVID-19 infection, death, or fatality, and compared the differences in the demographic and environmental variables such as ethnicity, age, population density, poverty, temperature, and air quality in each of these clusters. Furthermore, a regression model built on this data reveals PM2.5 and distance from the epicenter are significant risk factors for infection, while disease fatality has a strong association with age and PM2.5. Our results demonstrate that for the NYS, demographic components distinctly associate with specific aspects of COVID-19 burden and also highlight the detrimental impact of poor air quality. These results could help design and direct location-specific control and mitigation strategies." @default.
• W3203984103 created "2021-10-11" @default.
• W3203984103 creator A5015319020 @default.
• W3203984103 creator A5021692154 @default.
• W3203984103 creator A5022744318 @default.
• W3203984103 creator A5033823633 @default.
• W3203984103 creator A5043647420 @default.
• W3203984103 creator A5055350701 @default.
• W3203984103 creator A5083875068 @default.
• W3203984103 date "2022-02-01" @default.
• W3203984103 modified "2023-10-16" @default.
• W3203984103 title "COVID-19 in New York state: Effects of demographics and air quality on infection and fatality" @default.
• W3203984103 cites W1990130348 @default.
• W3203984103 cites W2031284780 @default.
• W3203984103 cites W2033269660 @default.
• W3203984103 cites W2102876987 @default.
• W3203984103 cites W2109045731 @default.
• W3203984103 cites W2139007084 @default.
• W3203984103 cites W2150751323 @default.
• W3203984103 cites W2265187329 @default.
• W3203984103 cites W2275537745 @default.
• W3203984103 cites W2289128632 @default.
• W3203984103 cites W2531469489 @default.
• W3203984103 cites W2883117606 @default.
• W3203984103 cites W2912034185 @default.
• W3203984103 cites W2942285739 @default.
• W3203984103 cites W2953856711 @default.
• W3203984103 cites W2981938452 @default.
• W3203984103 cites W3010655137 @default.
• W3203984103 cites W3013874785 @default.
• W3203984103 cites W3015807396 @default.
• W3203984103 cites W3016243898 @default.
• W3203984103 cites W3016535995 @default.
• W3203984103 cites W3016576256 @default.
• W3203984103 cites W3016748877 @default.
• W3203984103 cites W3017550043 @default.
• W3203984103 cites W3020264722 @default.
• W3203984103 cites W3024250998 @default.
• W3203984103 cites W3025271482 @default.
• W3203984103 cites W3033561420 @default.
• W3203984103 cites W3034006959 @default.
• W3203984103 cites W3036616490 @default.
• W3203984103 cites W3036656409 @default.
• W3203984103 cites W3036756844 @default.
• W3203984103 cites W3037524864 @default.
• W3203984103 cites W3042085096 @default.
• W3203984103 cites W3042835190 @default.
• W3203984103 cites W3043601429 @default.
• W3203984103 cites W3043805769 @default.
• W3203984103 cites W3045825506 @default.
• W3203984103 cites W3080219577 @default.
• W3203984103 cites W3080995561 @default.
• W3203984103 cites W3083348432 @default.
• W3203984103 cites W3087287908 @default.
• W3203984103 cites W3087321968 @default.
• W3203984103 cites W3087654809 @default.
• W3203984103 cites W3088423812 @default.
• W3203984103 cites W3090647375 @default.
• W3203984103 cites W3094685088 @default.
• W3203984103 cites W3095961138 @default.
• W3203984103 cites W3097453950 @default.
• W3203984103 cites W3097794852 @default.
• W3203984103 cites W3105596189 @default.
• W3203984103 cites W3118437969 @default.
• W3203984103 cites W3121159199 @default.
• W3203984103 cites W3132071024 @default.
• W3203984103 doi "https://doi.org/10.1016/j.scitotenv.2021.150536" @default.
• W3203984103 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8461036" @default.
• W3203984103 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34628294" @default.
• W3203984103 hasPublicationYear "2022" @default.
• W3203984103 type Work @default.
• W3203984103 sameAs 3203984103 @default.
• W3203984103 citedByCount "6" @default.
• W3203984103 countsByYear W32039841032022 @default.
• W3203984103 countsByYear W32039841032023 @default.
• W3203984103 crossrefType "journal-article" @default.
• W3203984103 hasAuthorship W3203984103A5015319020 @default.
• W3203984103 hasAuthorship W3203984103A5021692154 @default.
• W3203984103 hasAuthorship W3203984103A5022744318 @default.
• W3203984103 hasAuthorship W3203984103A5033823633 @default.
• W3203984103 hasAuthorship W3203984103A5043647420 @default.
• W3203984103 hasAuthorship W3203984103A5055350701 @default.
• W3203984103 hasAuthorship W3203984103A5083875068 @default.
• W3203984103 hasBestOaLocation W32039841031 @default.
• W3203984103 hasConcept C126314574 @default.
• W3203984103 hasConcept C137403100 @default.
• W3203984103 hasConcept C138816342 @default.
• W3203984103 hasConcept C142724271 @default.
• W3203984103 hasConcept C144024400 @default.
• W3203984103 hasConcept C149923435 @default.
• W3203984103 hasConcept C153294291 @default.
• W3203984103 hasConcept C159110408 @default.
• W3203984103 hasConcept C162324750 @default.
• W3203984103 hasConcept C179755657 @default.
• W3203984103 hasConcept C187316915 @default.
• W3203984103 hasConcept C189326681 @default.
• W3203984103 hasConcept C19165224 @default.
• W3203984103 hasConcept C205649164 @default.

• next