Spatial analysis of risk of morbidity and mortality by COVID-19 in Europe and  the Mediterranean in the year 2020

Andrades-Grassi, Jesús E.; Cuesta-Herrera, L; Bianchi-Pérez, Guillermo; Grassi, Hilda Cristina; López-Hernández, Juan Ygnacio; Torres-Mantilla, H A

doi:10.30827/cuadgeo.v60i1.15492

Cited by 12 publications

(13 citation statements)

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“…It can evaluate whether the data tend to be clustered, dispersed, or spatially random. It has been used to identify clustering of COVID‐19 (Alcântara et al., 2020 ; Baum & Henry, 2020 ; Kang, Choi, Kim, & Choi, 2020 ; Liu, Fang, & Gao, 2020 ; Wu et al., 2020 ) and facilitate the production of vulnerability and risk maps (e.g., Andrades‐Grassi et al., 2020 ; Gomes et al., 2020 ; Shariati, Mesgari, Kasraee, & Jahangiri‐Rad, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…It can evaluate whether the data tend to be clustered, dispersed, or spatially random. It has been used to identify clustering ofCOVID-19 (Alcântara et al, 2020;Baum & Henry, 2020;Kang, Choi, Kim, & Choi, 2020;Liu, Fang, & Gao, 2020;Wu et al, 2020) and facilitate the production of vulnerability and risk maps (e.g.,Andrades-Grassi et al, 2020;Gomes et al, 2020;Shariati, Mesgari, Kasraee, & Jahangiri-Rad, 2020).As a global indicator, Moran's I neglects the instability of local spatial processes, which led to the development of the local version of Moran's I(Anselin, 1995) which identifies both the spatial clustering of entities with similar values and the occurrence of divergent values. This latter version is also known as a local indicator of spatial association (LISA) Xie et al (2020).…”

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confidence: 99%

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A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

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COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

View full text Add to dashboard Cite

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“…Online ISSN 2412-0731 development associated with democracy and personal freedom (Ang et al, 2021;Jain & Singh, 2020), social inequality (Martines et al, 2021), restrictions introduced by the national governments (Andrades-Grassi et al, 2021;Jain & Singh, 2020). Some studies focused on geographical factors, such as climate (Ozkan et al, 2021), population density (Kaplan et al, 2021) and urbanization (Boterman et al, 2021).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Excess mortality in Russian regions during the COVID-19 pandemic

Макарова

Pyshmintseva

2021

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Relevance. In Russia, there are significant regional variations in the socio-economic impact of the COVID-19 pandemic (e.g. the growth in the number of deaths ranged from 7% in Novgorod Region to 43% in the Chechen Republic). In this paper, we focus on excess mortality and the key factors that contributed to it in order to get a better understanding of reasons behind such dramatic regional variations. The research objective is to assess excess mortality due to the COVID-19 pandemic and identify the key factors influencing the growth in mortality and regional variations in this indicator. Data and methods. The paper uses the analytical, statistical and correlation methods and the statistical data on Russian regions provided by the Federal State Statistics Service (Rosstat). Results. The analysis of excess mortality in Russian regions during the COVID-19 pandemic, in particular the correlation between excess mortality and intensity of human flows, has been used to build a typology of regions. In total, the regions were divided into four groups, and for each group the main factors that contributed to the growth in mortality were identified. The research findings can be used by policy-makers to devise more efficient regional policies in response to the COVID-19 crisis.

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“…Currently, increased access to mobility leads to a rapid spread of epidemics [2,3]. COVID-19 is an infectious pandemic caused by the SARS-CoV-2 pathogen, first detected during the 2019 epidemic in Wuhan, China, which was reported as an emerging pneumonic coronavirus disease with high morbidity and mortality rates [4,5]. Unlike other epidemics with a shortage of investigators, many scientists are now studying this pandemic globally and regionally.…”

Section: Introductionmentioning

confidence: 99%

Analysis of SEIR-type models used at the beginning of COVID-19 pandemic reported in high-impact journals

Cuesta-Herrera¹,

Pastenes²,

Neira³

et al. 2022

Medwave

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Introduction The Susceptible-Exposed-Infected-Recovered (SEIR) mathematical-epidemiological model has been exhaustively used since de beggining of the COVID-19 pandemic. These models intended to predict hospital burden and evaluate health measures to contain its spread. In this sense, flaws have been evidenced in the predictions of the first published models. It is considered necessary to evaluate the differences in the approach and verification of the models. Objectives We carried out a systematic review of the articles published in journals indexed in the Web of Science, of the first quartile and with an impact factor greater than two, that met the selection and inclusion criteria following the PRISMA-ScR standards. We included a total of 32 articles, which were evaluated according to demographic characteristics such as the month of receipt and publication, the country of origin of the information, the subject matter of the journal, and the characteristics of the modeling such as the presence of additional compartments, graphical analysis, conceptual model approach, interpretation of the basic reproductive number, and estimation of parameters. Methods Articles published in medical and health journals were predominant from February to July 2020. These articles most frequently used data from China and mostly focused on SEIR or full quarantine compartment models. The articles published in journals in mathematics were predominant from August to December 2020. Models used data from different world regions, considering a greater diversity of compartments such as asymptomatic patients or partial or complete quarantine. Results The articles analyzed mostly use SEIR-type models expanded with additional compartments. There are discrepancies in the breadth and methodological quality of the articles published according to the journal’s subject matter. The unification of quality criteria for describing the models in any journal is recommended.

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Spatial analysis of risk of morbidity and mortality by COVID-19 in Europe and the Mediterranean in the year 2020

Cited by 12 publications

References 0 publications

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Excess mortality in Russian regions during the COVID-19 pandemic

Analysis of SEIR-type models used at the beginning of COVID-19 pandemic reported in high-impact journals

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