GIS-based spatial modeling of COVID-19 incidence rate in the continental United States
During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of...
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| Published in | The Science of the total environment Vol. 728; p. 138884 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.08.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-9697 1879-1026 1879-1026 |
| DOI | 10.1016/j.scitotenv.2020.138884 |
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| Abstract | During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
[Display omitted]
•To explore relationship between 35 environmental, socioeconomic, and demographic variables and COVID-19 incidence in US•Multiscale geographically weighted regression could explain 68.1% of the total variations of COVID-19 incidence in US•Income inequality was an influential factor in explaining COVID-19 incidence particularly in the tri-state area |
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| AbstractList | During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R
2
: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
Unlabelled Image During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R²: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R : 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model, these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions. [Display omitted] •To explore relationship between 35 environmental, socioeconomic, and demographic variables and COVID-19 incidence in US•Multiscale geographically weighted regression could explain 68.1% of the total variations of COVID-19 incidence in US•Income inequality was an influential factor in explaining COVID-19 incidence particularly in the tri-state area |
| ArticleNumber | 138884 |
| Author | Mollalo, Abolfazl Vahedi, Behzad Rivera, Kiara M. |
| Author_xml | – sequence: 1 givenname: Abolfazl orcidid: 0000-0001-5092-0698 surname: Mollalo fullname: Mollalo, Abolfazl email: amollalo@bw.edu organization: Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA – sequence: 2 givenname: Behzad surname: Vahedi fullname: Vahedi, Behzad email: behzad@ucsb.edu organization: Department of Geography, University of California Santa Barbara (UCSB), Santa Barbara, CA, USA – sequence: 3 givenname: Kiara M. surname: Rivera fullname: Rivera, Kiara M. email: krivera19@bw.edu organization: Department of Public Health and Prevention Sciences, School of Health Sciences, Baldwin Wallace University, Berea, OH, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32335404$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.2139/ssrn.3562340 10.1007/s11135-006-9018-6 10.1016/S2468-2667(20)30085-2 10.1001/jama.2020.4978 10.1186/s12942-020-00204-6 10.1111/j.1435-5957.2012.00480.x 10.1016/j.scitotenv.2020.138226 10.1016/j.habitatint.2015.10.013 10.1111/1467-9884.00145 10.1017/S0950268816000224 10.3390/ijerph16010157 10.1177/0160017602250972 10.1111/j.1538-4632.1996.tb00936.x 10.3390/ijgi8060269 10.2196/18844 10.1068/b35137 10.1016/j.sste.2014.05.004 10.1016/j.dsx.2020.03.002 10.1016/j.actatropica.2018.09.004 10.1080/24694452.2017.1352480 10.1111/zph.12109 10.1111/gean.12189 10.3390/jcm9030841 10.1007/s10109-016-0239-5 10.1186/s12942-020-00202-8 10.1056/NEJMoa2001191 |
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| References | Brunsdon, Fotheringham, Charlton (bb0045) 1998; 47 Ahmed, Ahmed, Pissarides, Stiglitz (bb0010) 2020 Taghizadeh-Hesary, Akbari (bb0175) 2020; 2020 Lakhani (bb0125) 2020; S0885-3924 United Nations (bb0185) 2020 Holshue, DeBolt, Lindquist, Lofy, Wiesman, Bruce, Diaz (bb0110) 2020; 382 World Health Organization (WHO) (bb0210) 2020 Dowd, Rotondi, Andriano, Brazel, Block, Ding, Mills (bb0075) 2020 Kostov (bb0120) 2010; 37 Mollalo, Mao, Rashidi, Glass (bb0155) 2019; 16 Lovett, Poots, Clements, Green, Samarasundera, Bell (bb0130) 2014; 10 World Health Organization (WHO) (bb0205) 2020 Anselin, Arribas-Bel (bb0020) 2013; 92 Ma, Zhao, Liu, He, Wang, Fu, Luo (bb0135) 2020; 724 Bayne, Norris, Timmons (bb0025) 2020 Buja, Hastie, Tibshirani (bb0055) 1989 Wang, Tang, Feng, Lv (bb0190) 2020 World Health Organization (WHO) (bb0200) 2020 Oshan, Li, Kang, Wolf, Fotheringham (bb0165) 2019; 8 Brake, Barnsley, Lu, McAlinden, Eapen, Sohal (bb0035) 2020; 9 Buerhaus, Auerbach, Staiger (bb0050) 2020 O’brien (bb0160) 2007; 41 Oshan, Smith, Fotheringham (bb0170) 2020; 19 The COVID Tracking Project (bb0180) 2020 Mollalo, Khodabandehloo (bb0140) 2016; 144 Mollalo, Alimohammadi, Shirzadi, Malek (bb0145) 2015; 62 Yu, Fotheringham, Li, Oshan, Kang, Wolf (bb0220) 2019; 52 Anselin (bb0015) 2003; 26 Ward, Gleditsch (bb0195) 2018; 155 Congessional Research Service (bb0070) 2020 Abir, Nelson, Chan, Al-Ibrahim, Cutter, Patel, Bogart (bb0005) 2020 Fotheringham, Yang, Kang (bb0085) 2017; 107 Mollalo, Sadeghian, Israel, Rashidi, Sofizadeh, Glass (bb0150) 2018; 188 Brunsdon, Fotheringham, Charlton (bb0040) 1996; 28 Fotheringham, Oshan (bb0080) 2016; 18 Gupta, Ghosh, Singh, Misra (bb0100) 2020; 14 Hastie, Tibshirani (bb0105) 1986; 1 Gibson, Rush (bb0095) 2020; 6 Wu, Nethery, Sabath, Braun, Dominici (bb0215) 2020 Zheng, Ma, Zhang, Xie (bb0225) 2020; 1–2 Boulos, M. N. K., & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Johns Hopkins University Center for Systems Science and Engineering (bb0115) 2020 Center for Infectious Disease Research and Policy (bb0060) 2020 Chen, Chang, Han, Karacsonyi, Qian (bb0065) 2016; 51 Gangopadhyaya, Garrett (bb0090) 2020 Gangopadhyaya (10.1016/j.scitotenv.2020.138884_bb0090) 2020 Mollalo (10.1016/j.scitotenv.2020.138884_bb0150) 2018; 188 Kostov (10.1016/j.scitotenv.2020.138884_bb0120) 2010; 37 Bayne (10.1016/j.scitotenv.2020.138884_bb0025) 2020 Ward (10.1016/j.scitotenv.2020.138884_bb0195) 2018; 155 Brunsdon (10.1016/j.scitotenv.2020.138884_bb0040) 1996; 28 Mollalo (10.1016/j.scitotenv.2020.138884_bb0155) 2019; 16 Anselin (10.1016/j.scitotenv.2020.138884_bb0020) 2013; 92 Oshan (10.1016/j.scitotenv.2020.138884_bb0170) 2020; 19 Zheng (10.1016/j.scitotenv.2020.138884_bb0225) 2020; 1–2 Lovett (10.1016/j.scitotenv.2020.138884_bb0130) 2014; 10 Wu (10.1016/j.scitotenv.2020.138884_bb0215) 2020 10.1016/j.scitotenv.2020.138884_bb0030 Abir (10.1016/j.scitotenv.2020.138884_bb0005) Gibson (10.1016/j.scitotenv.2020.138884_bb0095) 2020; 6 Lakhani (10.1016/j.scitotenv.2020.138884_bb0125) 2020; S0885-3924 Buerhaus (10.1016/j.scitotenv.2020.138884_bb0050) 2020 Oshan (10.1016/j.scitotenv.2020.138884_bb0165) 2019; 8 World Health Organization (WHO) (10.1016/j.scitotenv.2020.138884_bb0200) Congessional Research Service (10.1016/j.scitotenv.2020.138884_bb0070) World Health Organization (WHO) (10.1016/j.scitotenv.2020.138884_bb0210) Center for Infectious Disease Research and Policy (10.1016/j.scitotenv.2020.138884_bb0060) Johns Hopkins University Center for Systems Science and Engineering (10.1016/j.scitotenv.2020.138884_bb0115) Ma (10.1016/j.scitotenv.2020.138884_bb0135) 2020; 724 Yu (10.1016/j.scitotenv.2020.138884_bb0220) 2019; 52 United Nations (10.1016/j.scitotenv.2020.138884_bb0185) Anselin (10.1016/j.scitotenv.2020.138884_bb0015) 2003; 26 Hastie (10.1016/j.scitotenv.2020.138884_bb0105) 1986; 1 Holshue (10.1016/j.scitotenv.2020.138884_bb0110) 2020; 382 O’brien (10.1016/j.scitotenv.2020.138884_bb0160) 2007; 41 World Health Organization (WHO) (10.1016/j.scitotenv.2020.138884_bb0205) Ahmed (10.1016/j.scitotenv.2020.138884_bb0010) 2020 Mollalo (10.1016/j.scitotenv.2020.138884_bb0145) 2015; 62 Taghizadeh-Hesary (10.1016/j.scitotenv.2020.138884_bb0175) 2020; 2020 The COVID Tracking Project (10.1016/j.scitotenv.2020.138884_bb0180) Fotheringham (10.1016/j.scitotenv.2020.138884_bb0080) 2016; 18 Fotheringham (10.1016/j.scitotenv.2020.138884_bb0085) 2017; 107 Dowd (10.1016/j.scitotenv.2020.138884_bb0075) 2020 Mollalo (10.1016/j.scitotenv.2020.138884_bb0140) 2016; 144 Brake (10.1016/j.scitotenv.2020.138884_bb0035) 2020; 9 Gupta (10.1016/j.scitotenv.2020.138884_bb0100) 2020; 14 Brunsdon (10.1016/j.scitotenv.2020.138884_bb0045) 1998; 47 Buja (10.1016/j.scitotenv.2020.138884_bb0055) 1989 Chen (10.1016/j.scitotenv.2020.138884_bb0065) 2016; 51 Wang (10.1016/j.scitotenv.2020.138884_bb0190) 2020 |
| References_xml | – year: 2020 ident: bb0070 article-title: Global Econoic Effects of COVID-19 – volume: 16 start-page: 157 year: 2019 ident: bb0155 article-title: A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States publication-title: Int. J. Environ. Res. Public Health – year: 2020 ident: bb0205 article-title: Coronavirus Disease 2019 (COVID-19) Situation Report – 83 – volume: 47 start-page: 431 year: 1998 end-page: 443 ident: bb0045 article-title: Geographically weighted regression publication-title: Journal of the Royal Statistical Society: Series D (The Statistician) – year: 2020 ident: bb0075 article-title: Demographic Science Aids in Understanding the Spread and Fatality Rates of COVID-19 – year: 2020 ident: bb0005 article-title: Critical Care Surge Response Strategies for the 2020 COVID-19 Outbreak in the United States – volume: 41 start-page: 673 year: 2007 end-page: 690 ident: bb0160 article-title: A caution regarding rules of thumb for variance inflation factors publication-title: Qual. Quant. – volume: 92 start-page: 3 year: 2013 end-page: 17 ident: bb0020 article-title: Spatial fixed effects and spatial dependence in a single cross-section publication-title: Pap. Reg. Sci. – volume: 2020 year: 2020 ident: bb0175 article-title: The powerful immune system against powerful COVID-19: a hypothesis publication-title: Preprints – volume: 28 start-page: 281 year: 1996 end-page: 298 ident: bb0040 article-title: Geographically weighted regression: a method for exploring spatial nonstationarity publication-title: Geogr. Anal. – year: 2020 ident: bb0115 article-title: COVID-19 Dashboard – volume: 1–2 year: 2020 ident: bb0225 article-title: COVID-19 and the cardiovascular system publication-title: Nat. Rev. Cardiol. – volume: 62 start-page: 18 year: 2015 end-page: 28 ident: bb0145 article-title: Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran publication-title: Zoonoses Public Health – volume: 18 start-page: 303 year: 2016 end-page: 329 ident: bb0080 article-title: Geographically weighted regression and multi-collinearity: dispelling the myth publication-title: J. Geogr. Syst. – volume: 724 year: 2020 ident: bb0135 article-title: Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China publication-title: Science of The Total Environment – volume: 144 start-page: 2217 year: 2016 end-page: 2229 ident: bb0140 article-title: Zoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach publication-title: Epidemiology & Infection – year: 2020 ident: bb0180 – start-page: 453 year: 1989 end-page: 510 ident: bb0055 article-title: Linear smoothers and additive models publication-title: Ann. Stat. – volume: 51 start-page: 59 year: 2016 end-page: 69 ident: bb0065 article-title: Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China publication-title: Habitat International – year: 2020 ident: bb0050 article-title: Older clinicians and the surge in novel coronavirus disease 2019 (COVID-19) publication-title: JAMA – volume: 14 start-page: 211 year: 2020 end-page: 212 ident: bb0100 article-title: Clinical considerations for patients with diabetes in times of COVID-19 epidemic publication-title: Diabetes & metabolic syndrome – volume: 107 start-page: 1247 year: 2017 end-page: 1265 ident: bb0085 article-title: Multiscale geographically weighted regression (MGWR) publication-title: Annals of the American Association of Geographers – volume: 10 start-page: 67 year: 2014 end-page: 74 ident: bb0130 article-title: Using geographical information systems and cartograms as a health service quality improvement tool publication-title: Spatial and Spatio-temporal Epidemiology – volume: 52 start-page: 87 year: 2019 end-page: 106 ident: bb0220 article-title: Inference in multiscale geographically weighted regression publication-title: Geogr. Anal. – year: 2020 ident: bb0010 article-title: Why inequality could spread COVID-19 publication-title: Lancet Public Health – year: 2020 ident: bb0060 article-title: US COVID-19 cases surge past 82,000, highest total in world – volume: 188 start-page: 187 year: 2018 end-page: 194 ident: bb0150 article-title: Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran publication-title: Acta Trop. – volume: 9 start-page: 841 year: 2020 ident: bb0035 article-title: Smoking Upregulates angiotensin-converting Enzyme-2 receptor: a potential adhesion site for novel coronavirus SARS-CoV-2 (Covid-19) publication-title: J. Clin. Med. – year: 2020 ident: bb0190 article-title: High Temperature and High Humidity Reduce the Transmission of COVID-19 – reference: Boulos, M. N. K., & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. – volume: 382 start-page: 929 year: 2020 end-page: 936 ident: bb0110 article-title: First case of 2019 novel coronavirus in the United States publication-title: New England Journal of Medicine – volume: 19 start-page: 1 year: 2020 end-page: 17 ident: bb0170 article-title: Targeting the spatial context of obesity determinants via multiscale geographically weighted regression publication-title: Int. J. Health Geogr. – year: 2020 ident: bb0200 article-title: Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) – year: 2020 ident: bb0185 article-title: The Social Impact of COVID-19 – volume: 37 start-page: 533 year: 2010 end-page: 549 ident: bb0120 article-title: Model boosting for spatial weighting matrix selection in spatial lag models publication-title: Environment and Planning B: Planning and Design – year: 2020 ident: bb0025 article-title: A primer on emergency occupational licensing reforms for combating COVID-19 publication-title: SSRN Electron. J. – volume: S0885-3924 start-page: 30194 year: 2020 end-page: 30199 ident: bb0125 article-title: Which Melbourne metropolitan areas are vulnerable to COVID-19 based on age, disability and access to health services? Using spatial analysis to identify service gaps and inform delivery publication-title: J. Pain Symptom Manag. – volume: 6 year: 2020 ident: bb0095 article-title: Novel coronavirus in Cape Town informal settlements: feasibility of using informal dwelling outlines to identify high risk areas for COVID-19 transmission from a social distancing perspective publication-title: JMIR Public Health Surveill. – year: 2020 ident: bb0090 article-title: Unemployment, Health Insurance, and the COVID-19 Recession – year: 2020 ident: bb0215 article-title: Exposure to Air Pollution and COVID-19 Mortality in the United States – year: 2020 ident: bb0210 article-title: Rolling Updates on Coronavirus Disease (COVID-19) – volume: 8 start-page: 269 year: 2019 ident: bb0165 article-title: Mgwr: a Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale publication-title: ISPRS Int. J. Geo Inf. – volume: 155 year: 2018 ident: bb0195 article-title: Spatial regression models – volume: 1 start-page: 297 year: 1986 end-page: 310 ident: bb0105 article-title: Generalized additive models publication-title: Stat. Sci. – volume: 26 start-page: 153 year: 2003 end-page: 166 ident: bb0015 article-title: Spatial externalities, spatial multipliers and spatial econometrics publication-title: Int. Reg. Sci. Rev. – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0025 article-title: A primer on emergency occupational licensing reforms for combating COVID-19 publication-title: SSRN Electron. J. doi: 10.2139/ssrn.3562340 – volume: 41 start-page: 673 issue: 5 year: 2007 ident: 10.1016/j.scitotenv.2020.138884_bb0160 article-title: A caution regarding rules of thumb for variance inflation factors publication-title: Qual. Quant. doi: 10.1007/s11135-006-9018-6 – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0090 – volume: 1 start-page: 297 issue: 3 year: 1986 ident: 10.1016/j.scitotenv.2020.138884_bb0105 article-title: Generalized additive models publication-title: Stat. Sci. – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0010 article-title: Why inequality could spread COVID-19 publication-title: Lancet Public Health doi: 10.1016/S2468-2667(20)30085-2 – ident: 10.1016/j.scitotenv.2020.138884_bb0200 – ident: 10.1016/j.scitotenv.2020.138884_bb0180 – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0050 article-title: Older clinicians and the surge in novel coronavirus disease 2019 (COVID-19) publication-title: JAMA doi: 10.1001/jama.2020.4978 – start-page: 453 year: 1989 ident: 10.1016/j.scitotenv.2020.138884_bb0055 article-title: Linear smoothers and additive models publication-title: Ann. Stat. – volume: 19 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0170 article-title: Targeting the spatial context of obesity determinants via multiscale geographically weighted regression publication-title: Int. J. Health Geogr. doi: 10.1186/s12942-020-00204-6 – volume: 2020 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0175 article-title: The powerful immune system against powerful COVID-19: a hypothesis publication-title: Preprints – volume: 92 start-page: 3 issue: 1 year: 2013 ident: 10.1016/j.scitotenv.2020.138884_bb0020 article-title: Spatial fixed effects and spatial dependence in a single cross-section publication-title: Pap. Reg. Sci. doi: 10.1111/j.1435-5957.2012.00480.x – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0075 – volume: 724 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0135 article-title: Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China publication-title: Science of The Total Environment doi: 10.1016/j.scitotenv.2020.138226 – volume: 51 start-page: 59 year: 2016 ident: 10.1016/j.scitotenv.2020.138884_bb0065 article-title: Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China publication-title: Habitat International doi: 10.1016/j.habitatint.2015.10.013 – volume: 47 start-page: 431 issue: 3 year: 1998 ident: 10.1016/j.scitotenv.2020.138884_bb0045 article-title: Geographically weighted regression publication-title: Journal of the Royal Statistical Society: Series D (The Statistician) doi: 10.1111/1467-9884.00145 – volume: 144 start-page: 2217 issue: 10 year: 2016 ident: 10.1016/j.scitotenv.2020.138884_bb0140 article-title: Zoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach publication-title: Epidemiology & Infection doi: 10.1017/S0950268816000224 – ident: 10.1016/j.scitotenv.2020.138884_bb0205 – volume: 16 start-page: 157 issue: 1 year: 2019 ident: 10.1016/j.scitotenv.2020.138884_bb0155 article-title: A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph16010157 – volume: 26 start-page: 153 issue: 2 year: 2003 ident: 10.1016/j.scitotenv.2020.138884_bb0015 article-title: Spatial externalities, spatial multipliers and spatial econometrics publication-title: Int. Reg. Sci. Rev. doi: 10.1177/0160017602250972 – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0190 – volume: 28 start-page: 281 issue: 4 year: 1996 ident: 10.1016/j.scitotenv.2020.138884_bb0040 article-title: Geographically weighted regression: a method for exploring spatial nonstationarity publication-title: Geogr. Anal. doi: 10.1111/j.1538-4632.1996.tb00936.x – volume: 8 start-page: 269 issue: 6 year: 2019 ident: 10.1016/j.scitotenv.2020.138884_bb0165 article-title: Mgwr: a Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale publication-title: ISPRS Int. J. Geo Inf. doi: 10.3390/ijgi8060269 – volume: 6 issue: 2 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0095 article-title: Novel coronavirus in Cape Town informal settlements: feasibility of using informal dwelling outlines to identify high risk areas for COVID-19 transmission from a social distancing perspective publication-title: JMIR Public Health Surveill. doi: 10.2196/18844 – ident: 10.1016/j.scitotenv.2020.138884_bb0005 – volume: 1–2 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0225 article-title: COVID-19 and the cardiovascular system publication-title: Nat. Rev. Cardiol. – volume: 37 start-page: 533 issue: 3 year: 2010 ident: 10.1016/j.scitotenv.2020.138884_bb0120 article-title: Model boosting for spatial weighting matrix selection in spatial lag models publication-title: Environment and Planning B: Planning and Design doi: 10.1068/b35137 – ident: 10.1016/j.scitotenv.2020.138884_bb0115 – volume: 10 start-page: 67 year: 2014 ident: 10.1016/j.scitotenv.2020.138884_bb0130 article-title: Using geographical information systems and cartograms as a health service quality improvement tool publication-title: Spatial and Spatio-temporal Epidemiology doi: 10.1016/j.sste.2014.05.004 – volume: 14 start-page: 211 issue: 3 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0100 article-title: Clinical considerations for patients with diabetes in times of COVID-19 epidemic publication-title: Diabetes & metabolic syndrome doi: 10.1016/j.dsx.2020.03.002 – volume: 188 start-page: 187 year: 2018 ident: 10.1016/j.scitotenv.2020.138884_bb0150 article-title: Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran publication-title: Acta Trop. doi: 10.1016/j.actatropica.2018.09.004 – volume: 155 year: 2018 ident: 10.1016/j.scitotenv.2020.138884_bb0195 – ident: 10.1016/j.scitotenv.2020.138884_bb0210 – volume: 107 start-page: 1247 issue: 6 year: 2017 ident: 10.1016/j.scitotenv.2020.138884_bb0085 article-title: Multiscale geographically weighted regression (MGWR) publication-title: Annals of the American Association of Geographers doi: 10.1080/24694452.2017.1352480 – ident: 10.1016/j.scitotenv.2020.138884_bb0070 – volume: 62 start-page: 18 issue: 1 year: 2015 ident: 10.1016/j.scitotenv.2020.138884_bb0145 article-title: Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran publication-title: Zoonoses Public Health doi: 10.1111/zph.12109 – volume: 52 start-page: 87 year: 2019 ident: 10.1016/j.scitotenv.2020.138884_bb0220 article-title: Inference in multiscale geographically weighted regression publication-title: Geogr. Anal. doi: 10.1111/gean.12189 – volume: 9 start-page: 841 issue: 3 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0035 article-title: Smoking Upregulates angiotensin-converting Enzyme-2 receptor: a potential adhesion site for novel coronavirus SARS-CoV-2 (Covid-19) publication-title: J. Clin. Med. doi: 10.3390/jcm9030841 – volume: 18 start-page: 303 issue: 4 year: 2016 ident: 10.1016/j.scitotenv.2020.138884_bb0080 article-title: Geographically weighted regression and multi-collinearity: dispelling the myth publication-title: J. Geogr. Syst. doi: 10.1007/s10109-016-0239-5 – volume: S0885-3924 start-page: 30194 issue: 20 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0125 article-title: Which Melbourne metropolitan areas are vulnerable to COVID-19 based on age, disability and access to health services? Using spatial analysis to identify service gaps and inform delivery publication-title: J. Pain Symptom Manag. – ident: 10.1016/j.scitotenv.2020.138884_bb0030 doi: 10.1186/s12942-020-00202-8 – ident: 10.1016/j.scitotenv.2020.138884_bb0185 – year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0215 – volume: 382 start-page: 929 year: 2020 ident: 10.1016/j.scitotenv.2020.138884_bb0110 article-title: First case of 2019 novel coronavirus in the United States publication-title: New England Journal of Medicine doi: 10.1056/NEJMoa2001191 – ident: 10.1016/j.scitotenv.2020.138884_bb0060 |
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| Snippet | During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented... During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been reported, posing unprecedented... |
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| Title | GIS-based spatial modeling of COVID-19 incidence rate in the continental United States |
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