Topical Collection "Spatial Components of COVID-19 Pandemic"

Editors

Prof. Dr. Fazlay S. Faruque
E-Mail Website
Collection Editor
Department of Preventive Medicine, University of Mississippi Medical Center, 2500 North State Street Jackson, MS 39216-4505, USA
Interests: geospatial health; environmental health; landscape epidemiology; population health geography; geospatial health disparities; geospatial analysis of eco-social determinants; application of Earth observation resources in health studies
Special Issues and Collections in MDPI journals
Prof. Dr. Alan D. Penman
E-Mail Website
Collection Editor
Department of Preventive Medicine, John D. Bower School of Population Health, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA
Interests: chronic disease epidemiology; community health; public health; global health; evolutionary medicine
Prof. Dr. Peng Jia
E-Mail Website
Collection Editor
International Initiative on Spatial Lifecourse Epidemiology (ISLE), 181 Chatham Road South, Hong Kong, China
Interests: Spatial lifecourse epidemiology, emerging infectious diseases, epidemic control and prevention, COVID-19 epidemiology, spatio-temporal modeling
Special Issues and Collections in MDPI journals
Dr. Mo Ashifur Rahim
E-Mail Website
Collection Editor
Sydney Medical Center, 580 George St, Sydney, NSW 2000, Australia
Interests: general clinical practice; preventive medicine; public health; community health; public health education; mental health; nutritional health

Topical Collection Information

Dear Colleagues,

The COVID-19 pandemic is challenging economies and current healthcare systems in countries all over the world. Predicting the spread of SARS-COV-2 and efficient deployment of resources have become critically important. Understanding the spatial components of the disease and the virus can lead to the effective application of spatial technologies in modeling disease transmission and developing projections of cases, hospitalizations, and required resources.

This Special Collection of IJGI is expected to serve as a knowledge exchange platform presenting the latest advances in spatial technological applications to combat this unprecedented situation of COVID-19. This collection hopefully will guide professionals if in the future there is another pandemic of this nature to better prepare saving lives. 

We invite authors to submit original scientific papers on the application of spatial technologies for managing COVID-19 including, but not limited to, the following topics: 

  • Understanding the spatial components of the disease and the virus;
  • Spatio-temporal prediction models;
  • Healthcare resource requirements during spatio-temporal dynamics;
  • Spatial variability in risk factors;
  • Spatial tracking;
  • Spatial distance vs social distance;
  • Global vs local patterns;
  • Prevention plan;
  • Environmental factors impacting the survival and spread of SARS-COV-2;
  • Environmental exposures on vulnerability of COVID-19;
  • Impact of COVID-19 lockdown on environment.

Prof. Dr. Fazlay S. Faruque
Prof. Dr. Alan D. Penman
Prof. Dr. Peng Jia
Dr. Mo Ashifur Rahim
Collection 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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection 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. ISPRS International Journal of Geo-Information 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 1400 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.

Published Papers (8 papers)

2021

Jump to: 2020

Open AccessArticle
Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America
ISPRS Int. J. Geo-Inf. 2021, 10(3), 121; https://doi.org/10.3390/ijgi10030121 - 01 Mar 2021
Viewed by 794
Abstract
Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in [...] Read more.
Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees. Full article
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Open AccessArticle
Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition
ISPRS Int. J. Geo-Inf. 2021, 10(2), 81; https://doi.org/10.3390/ijgi10020081 - 18 Feb 2021
Viewed by 464
Abstract
Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute [...] Read more.
Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models. Full article
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Open AccessArticle
Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain
ISPRS Int. J. Geo-Inf. 2021, 10(2), 73; https://doi.org/10.3390/ijgi10020073 - 13 Feb 2021
Cited by 2 | Viewed by 1042
Abstract
The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of [...] Read more.
The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain. Full article
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Open AccessArticle
AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing
ISPRS Int. J. Geo-Inf. 2021, 10(1), 34; https://doi.org/10.3390/ijgi10010034 - 14 Jan 2021
Cited by 1 | Viewed by 1134
Abstract
The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool [...] Read more.
The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process. Full article
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2020

Jump to: 2021

Open AccessArticle
Intracity Pandemic Risk Evaluation Using Mobile Phone Data: The Case of Shanghai during COVID-19
ISPRS Int. J. Geo-Inf. 2020, 9(12), 715; https://doi.org/10.3390/ijgi9120715 - 01 Dec 2020
Cited by 2 | Viewed by 765
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the [...] Read more.
The coronavirus disease 2019 (COVID-19) pandemic has provided an opportunity to rethink the development of a sustainable and resilient city. A framework for comprehensive intracity pandemic risk evaluation using mobile phone data is proposed in this study. Four steps were included in the framework: identification of high-risk groups, calculation of dynamic population flow and construction of a human mobility network, exposure and transmission risk assessment, and pandemic prevention guidelines. First, high-risk groups were extracted from mobile phone data based on multi-day activity chains. Second, daily human mobility networks were created by aggregating population and origin-destination (OD) flows. Third, clustering analysis, time series analysis, and network analysis were employed to evaluate pandemic risk. Finally, several solutions are proposed to control the pandemic. The outbreak period of COVID-19 in Shanghai was used to verify the proposed framework and methodology. The results show that the evaluation method is able to reflect the different spatiotemporal patterns of pandemic risk. The proposed framework and methodology may help prevent future public health emergencies and localized epidemics from evolving into global pandemics. Full article
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Open AccessArticle
Is Crowdsourcing a Reliable Method for Mass Data Acquisition? The Case of COVID-19 Spread in Greece During Spring 2020
ISPRS Int. J. Geo-Inf. 2020, 9(10), 605; https://doi.org/10.3390/ijgi9100605 - 14 Oct 2020
Cited by 2 | Viewed by 1215
Abstract
We present a GIS-based crowdsourcing application that was launched soon after the first COVID-19 cases had been recorded in Greece, motivated by the need for fast, location-wise data acquisition regarding COVID-19 disease spread during spring 2020, due to limited testing. A single question [...] Read more.
We present a GIS-based crowdsourcing application that was launched soon after the first COVID-19 cases had been recorded in Greece, motivated by the need for fast, location-wise data acquisition regarding COVID-19 disease spread during spring 2020, due to limited testing. A single question was posted through a web App, to which the anonymous participants subjectively answered whether or not they had experienced any COVID-19 disease symptoms. Our main goal was to locate geographical areas with increased number of people feeling the symptoms and to determine any temporal changes in the statistics of the survey entries. It was found that the application was rapidly disseminated to the entire Greek territory via social media, having, thus, a great public reception. The higher percentages of participants experiencing symptoms coincided geographically with the highly populated urban areas, having also increased numbers of confirmed cases, while temporal variations were detected that accorded with the restrictions of activities. This application demonstrates that health systems can use crowdsourcing applications that assure anonymity, as an alternative to tracing apps, to identify possible hot spots and to reach and warn the public within a short time interval, increasing at the same time their situational awareness. However, a continuous reminder for participation should be scheduled. Full article
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Open AccessReview
The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil
ISPRS Int. J. Geo-Inf. 2020, 9(9), 557; https://doi.org/10.3390/ijgi9090557 - 20 Sep 2020
Cited by 4 | Viewed by 2712
Abstract
The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO [...] Read more.
The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys. Full article
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Open AccessArticle
Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak
ISPRS Int. J. Geo-Inf. 2020, 9(6), 382; https://doi.org/10.3390/ijgi9060382 - 10 Jun 2020
Cited by 7 | Viewed by 1529
Abstract
The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed [...] Read more.
The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed throughout WA and the USA. Lack of access to publicly available spatial data at finer scales has prevented scientists from implementing spatial analytical techniques to gain insights into the spread of COVID-19. Datasets were available only as counts at county levels. The spatial response to COVID-19 using coarse-scale publicly available datasets was limited to web mapping applications and dashboards to visualize infected cases from state to county levels only. This research approaches data availability issues by creating proxy datasets for COVID-19 using publicly available news articles. Further, these proxy datasets are used to perform spatial analyses to unfolding events in space and time and to gain insights into the spread of COVID-19 in WA during the initial stage of the outbreak. Spatial analysis of theses proxy datasets from 21 January to 23 March 2020, suggests the presence of a clear space–time pattern. From 21 January to 6 March, a strong presence of community spread of COVID-19 is observed only in close proximity of the outbreak source in Snohomish and King Counties, which are neighbors. Infections diffused to farther locations only after a month, i.e., 6 March. The space–time pattern of diffusion observed in this study suggests that implementing strict social distancing measures during the initial stage in infected locations can drastically help curb the spread to distant locations. Full article
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