Research

Jump to: Review

17 pages, 3338 KiB

Open AccessArticle

Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data

by Ru Wang, Lingbo Liu, Hao Wu and Zhenghong Peng

Int. J. Environ. Res. Public Health 2022, 19(9), 5208; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19095208 - 25 Apr 2022

Cited by 3 | Viewed by 1525

Abstract

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from [...] Read more.

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

16 pages, 4102 KiB

Open AccessArticle

A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective

by Sui Zhang, Minghao Wang, Zhao Yang and Baolei Zhang

Int. J. Environ. Res. Public Health 2021, 18(24), 13294; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182413294 - 16 Dec 2021

Cited by 2 | Viewed by 2283

Abstract

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, [...] Read more.

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R²) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

21 pages, 5778 KiB

Open AccessArticle

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farbod Farhangi and Soo-Mi Choi

Int. J. Environ. Res. Public Health 2021, 18(18), 9657; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189657 - 14 Sep 2021

Cited by 18 | Viewed by 2575

Abstract

The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms [...] Read more.

The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease—public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

14 pages, 3589 KiB

Open AccessArticle

Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

by Abolfazl Mollalo and Moosa Tatar

Int. J. Environ. Res. Public Health 2021, 18(18), 9488; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189488 - 08 Sep 2021

Cited by 40 | Viewed by 5666

Abstract

Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to [...] Read more.

Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to the resurgence of COVID-19 cases to over 100,000 people during early August 2021. To our knowledge, there are limited nationwide studies that examined the spatial distribution of vaccination rates, mainly based on the social vulnerability index (SVI). In this study, we compiled a database of the percentage of fully vaccinated people at the county scale across the continental United States as of 29 July 2021, along with SVI data as potential significant covariates. We further employed multiscale geographically weighted regression to model spatial nonstationarity of vaccination rates. Our findings indicated that the model could explain over 79% of the variance of vaccination rate based on Per capita income and Minority (%) (with positive impacts), and Age 17 and younger (%), Mobile homes (%), and Uninsured people (%) (with negative effects). However, the impact of each covariate varied for different counties due to using separate optimal bandwidths. This timely study can serve as a geospatial reference to support public health decision-makers in forming region-specific policies in monitoring vaccination programs from a geographic perspective. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

15 pages, 5628 KiB

Open AccessArticle

COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability

by Yoo Min Park, Gregory D. Kearney, Bennett Wall, Katherine Jones, Robert J. Howard and Ray H. Hylock

Int. J. Environ. Res. Public Health 2021, 18(17), 8987; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18178987 - 26 Aug 2021

Cited by 9 | Viewed by 4728

Abstract

The geographic areas most impacted by COVID-19 may not remain static because public health measures/behaviors change dynamically, and the impacts of pandemic vulnerability also may vary geographically and temporally. The nature of the pandemic makes spatiotemporal methods essential to understanding the distribution of [...] Read more.

The geographic areas most impacted by COVID-19 may not remain static because public health measures/behaviors change dynamically, and the impacts of pandemic vulnerability also may vary geographically and temporally. The nature of the pandemic makes spatiotemporal methods essential to understanding the distribution of COVID-19 deaths and developing interventions. This study examines the spatiotemporal trends in COVID-19 death rates in the United States from March 2020 to May 2021 by performing an emerging hot spot analysis (EHSA). It then investigates the effects of the COVID-19 time-dependent and basic social vulnerability factors on COVID-19 death rates using geographically and temporally weighted regression (GTWR). The EHSA results demonstrate that over the three phases of the pandemic (first wave, second wave, and post-vaccine deployment), hot spots have shifted from densely populated cities and the states with a high percentage of socially vulnerable individuals to the states with relatively relaxed social distancing requirements, and then to the states with low vaccination rates. The GTWR results suggest that local infection and testing rates, social distancing interventions, and other social, environmental, and health risk factors show significant associations with COVID-19 death rates, but these associations vary over time and space. These findings can inform public health planning. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

13 pages, 348 KiB

Open AccessArticle

Factors Associated with Financial Security, Food Security and Quality of Daily Lives of Residents in Nigeria during the First Wave of the COVID-19 Pandemic

by Morenike Oluwatoyin Folayan, Olanrewaju Ibigbami, Maha El Tantawi, Brandon Brown, Nourhan M. Aly, Oliver Ezechi, Giuliana Florencia Abeldaño, Eshrat Ara, Martin Amogre Ayanore, Passent Ellakany, Balgis Gaffar, Nuraldeen Maher Al-Khanati, Ifeoma Idigbe, Anthonia Omotola Ishabiyi, Mohammed Jafer, Abeedha Tu-Allah Khan, Zumama Khalid, Folake Barakat Lawal, Joanne Lusher, Ntombifuthi P. Nzimande, Bamidele Emmanuel Osamika, Mir Faeq Ali Quadri, Mark Roque, Ala’a B. Al-Tammemi, Muhammad Abrar Yousaf, Jorma I. Virtanen, Roberto Ariel Abeldaño Zuñiga, Joseph Chukwudi Okeibunor and Annie Lu Nguyen Show full author list Hide full author list

Int. J. Environ. Res. Public Health 2021, 18(15), 7925; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18157925 - 27 Jul 2021

Cited by 28 | Viewed by 5212

Abstract

An online survey was conducted to identify factors associated with financial insecurity, food insecurity and poor quality of daily lives of adults in Nigeria during the first wave of the COVID-19 pandemic. The associations between the outcome (experience of financial loss, changes in [...] Read more.

An online survey was conducted to identify factors associated with financial insecurity, food insecurity and poor quality of daily lives of adults in Nigeria during the first wave of the COVID-19 pandemic. The associations between the outcome (experience of financial loss, changes in food intake and impact of the pandemic on daily lives) and the explanatory (age, sex, education level, anxiety, depression, HIV status) variables were determined using logistic regression analysis. Of the 4439 respondents, 2487 (56.0%) were financially insecure, 907 (20.4%) decreased food intake and 4029 (90.8%) had their daily life negatively impacted. Males (AOR:0.84), people who felt depressed (AOR:0.62) and people living with HIV -PLHIV- (AOR:0.70) had significantly lower odds of financial insecurity. Older respondents (AOR:1.01) had significantly higher odds of financial insecurity. Those depressed (AOR:0.62) and PLHIV (AOR:0.55) had significantly lower odds of reporting decreased food intake. Respondents who felt anxious (AOR:0.07), depressed (AOR: 0.48) and who were PLHIV (AOR:0.68) had significantly lower odds of reporting a negative impact of the pandemic on their daily lives. We concluded the study findings may reflect a complex relationship between financial insecurity, food insecurity, poor quality of life, mental health, and socioeconomic status of adults living in Nigeria during the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

Review

Jump to: Research

18 pages, 2517 KiB

Open AccessReview

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

by Farrukh Saleem, Abdullah Saad AL-Malaise AL-Ghamdi, Madini O. Alassafi and Saad Abdulla AlGhamdi

Int. J. Environ. Res. Public Health 2022, 19(9), 5099; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19095099 - 22 Apr 2022

Cited by 13 | Viewed by 3924

Abstract

COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted [...] Read more.

COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

14 pages, 8210 KiB

Open AccessReview

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

by Abolfazl Mollalo, Alireza Mohammadi, Sara Mavaddati and Behzad Kiani

Int. J. Environ. Res. Public Health 2021, 18(22), 12024; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182212024 - 16 Nov 2021

Cited by 16 | Viewed by 4142

Abstract

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this [...] Read more.

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques. Full article

(This article belongs to the Special Issue Advances in Spatial Epidemiology of COVID-19)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advances in Spatial Epidemiology of COVID-19

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Related Special Issue

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI