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Spatial Analytics for COVID-19 Studies
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Spatial Analytics for COVID-19 Studies

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 12265

Special Issue Editors

Dr. Tao Hu

E-Mail Website
Guest Editor
Center for Geographic Analysis, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA
Interests: spatial modelling; geospatial big data analysis; health geography; urban crime analysis
Dr. Zhenlong Li

E-Mail Website
Guest Editor
Department of Geography, University of South Carolina, Columbia, SC 29208, USA
Interests: GIScience; geospatial big data; spatial computing; GeoAI
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Huang

E-Mail Website
Guest Editor
Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
Interests: spatial analysis; remote sensing; human–environment interactions; social sensing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coronavirus disease 2019 (COVID-19) is a global threat that has led to many health, economic, and social challenges. The spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that caused the COVID-19 pandemic is inherently a spatial process. Therefore, geospatial data, algorithms, models, tools, and platforms play an irreplaceable role in providing situational awareness that benefits decision making. The notable advances in Geographical Information Sciences (GIScience) have encouraged the incorporation of spatial analytics into various epidemiological studies over the past decade.

In this Special Issue, we focus on the development and application of advanced spatial analytics towards understanding the transmission and impacts of COVID-19. We invite contributions that address this general topic from a broad spectrum of data sources (public health, economics, socio-demographics, social media, mobile phone data, transportation records, surveys, etc.) and via a variety of spatial analytics including (but not limited to) spatial statistics, agent-based simulation, digital contact tracing, case forecasting, disease transmission modeling, geo-aware analysis, spatiotemporal prediction, intelligent algorithms (i.e., machine learning and deep learning), and big data analytics. We also welcome studies that produce, design, and develop shareable COVID-19 modeling-related data, online visualization/analytical platforms, and reusable analytical tools, packages, and models.

Dr. Tao Hu
Dr. Zhenlong Li
Dr. Xiao Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geospatial data
  • spatial analysis
  • geographical information science (GIS)
  • public health
  • COVID-19

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Published Papers (4 papers)

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Research

22 pages, 3323 KiB  
Article
Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan
by Mengyue Yuan, Tong Liu and Chao Yang
Int. J. Environ. Res. Public Health 2022, 19(11), 6523; https://doi.org/10.3390/ijerph19116523 - 27 May 2022
Cited by 6 | Viewed by 1891
Abstract
It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina [...] Read more.
It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan. We considered human-activity patterns and related demographic information as the COVID-19 influencing determinants, and we used spatial regression models to evaluate the relationships between COVID-19 morbidity and the related factors. Furthermore, we traced Weibo users’ check-in trajectories to characterize the spatial interaction between high-morbidity residential areas and activity venues with POI (point of interest) sites, and we located a series of potential at-risk places in Wuhan. The results provide statistical evidence regarding the utility of human activity and demographic factors for the determination of COVID-19 morbidity patterns in the early pandemic stage in Wuhan. The spatial interaction revealed a general transmission pattern in Wuhan and determined the high-risk areas of COVID-19 transmission. This article explores the human-activity characteristics from social media check-in data and studies how human activities played a role in COVID-19 transmission in Wuhan. From that, we provide new insights for scientific prevention and control of COVID-19. Full article
(This article belongs to the Special Issue Spatial Analytics for COVID-19 Studies)
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16 pages, 3433 KiB  
Article
Deciphering Multifactorial Correlations of COVID-19 Incidence and Mortality in the Brazilian Amazon Basin
by Blanca Elena Guerrero Daboin, Italla Maria Pinheiro Bezerra, Tassiane Cristina Morais, Isabella Portugal, Jorge de Oliveira Echeimberg, André Evaristo Marcondes Cesar, Matheus Paiva Emidio Cavalcanti, Lucas Cauê Jacintho, Rodrigo Daminello Raimundo, Khalifa Elmusharaf, Carlos Eduardo Siqueira and Luiz Carlos de Abreu
Int. J. Environ. Res. Public Health 2022, 19(3), 1153; https://doi.org/10.3390/ijerph19031153 - 20 Jan 2022
Cited by 11 | Viewed by 2846
Abstract
Amazonas suffered greatly during the COVID-19 pandemic. The mortality and fatality rates soared and scarcity of oxygen and healthcare supplies led the health system and funerary services to collapse. Thus, we analyzed the trends of incidence, mortality, and lethality indicators of COVID-19 and [...] Read more.
Amazonas suffered greatly during the COVID-19 pandemic. The mortality and fatality rates soared and scarcity of oxygen and healthcare supplies led the health system and funerary services to collapse. Thus, we analyzed the trends of incidence, mortality, and lethality indicators of COVID-19 and the dynamics of their main determinants in the state of Amazonas from March 2020 to June 2021. This is a time-series ecological study. We calculated the lethality, mortality, and incidence rates with official and public data from the Health Department. We used the Prais–Winsten regression and trends were classified as stationary, increasing, or decreasing. The effective reproduction number (Rt) was also estimated. Differences were considered significant when p < 0.05. We extracted 396,772 cases of and 13,420 deaths from COVID-19; 66% of deaths were in people aged over 60; 57% were men. Cardiovascular diseases were the most common comorbidity (28.84%), followed by diabetes (25.35%). Rural areas reported 53% of the total cases and 31% of the total deaths. The impact of COVID-19 in the Amazon is not limited to the direct effects of the pandemic itself; it may present characteristics of a syndemic due to the interaction of COVID-19 with pre-existing illnesses, endemic diseases, and social vulnerabilities. Full article
(This article belongs to the Special Issue Spatial Analytics for COVID-19 Studies)
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14 pages, 1396 KiB  
Article
Phased Implementation of COVID-19 Vaccination: Rapid Assessment of Policy Adoption, Reach and Effectiveness to Protect the Most Vulnerable in the US
by Yun Li, Moming Li, Megan Rice, Yanfang Su and Chaowei Yang
Int. J. Environ. Res. Public Health 2021, 18(14), 7665; https://doi.org/10.3390/ijerph18147665 - 19 Jul 2021
Cited by 13 | Viewed by 3237
Abstract
The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as [...] Read more.
The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet became economically and administratively difficult to enforce as the pandemic continued. In late December 2020, COVID-19 vaccines were first approved in the US and states began a phased implementation of COVID-19 vaccination. However, there is limited quantitative evidence regarding the effectiveness of the phased COVID-19 vaccination. This study aims to provide a rapid assessment of the adoption, reach, and effectiveness of the phased implementation of COVID-19 vaccination. We utilize an event-study analysis to evaluate the effect of vaccination on the state-level daily COVID-19 case growth rate. Through this analysis, we assert that vaccination was effective in reducing the spread of COVID-19 shortly after the first shots were given. Specifically, the case growth rate declined by 0.124, 0.347, 0.345, 0.464, 0.490, and 0.756 percentage points corresponding to the 1–5, 6–10, 11–15, 16–20, 21–25, and 26 or more day periods after the initial shots. The findings could be insightful for policymakers as they work to optimize vaccine distribution in later phases, and also for the public as the COVID-19 related health risk is a contentious issue. Full article
(This article belongs to the Special Issue Spatial Analytics for COVID-19 Studies)
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16 pages, 3625 KiB  
Article
Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
by Han Yue and Tao Hu
Int. J. Environ. Res. Public Health 2021, 18(13), 6832; https://doi.org/10.3390/ijerph18136832 - 25 Jun 2021
Cited by 17 | Viewed by 2800
Abstract
Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from [...] Read more.
Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19. Full article
(This article belongs to the Special Issue Spatial Analytics for COVID-19 Studies)
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