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      Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic

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      Geographia Polonica
      Institute of Geography and Spatial Organization, Polish Academy of Sciences

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          Abstract

          The spread of the COVID-19 pandemic has a simultaneous temporal and spatial component. This pattern results from a complex combination of factors, including social ones, that lead to significant differences in the evolution of space-time distributions, both between and within countries. The aim of this study was to assess changes in the regularity of the spatial distribution of the number of diagnosed COVID-19 cases in Poland over more than a year of the pandemic. The analysis utilized daily and weekly data for 380 counties (poviats), using the local – Poisson risk semivariogram – measure of spatial autocorrelation. Despite the heterogeneity and errors in the source data, it was possible to identify clear patterns of temporal changes in the spatial distribution of COVID-19 cases, manifested by differences in the nature and extent of their autocorrelation.

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          Most cited references47

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          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 announced, 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.
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            Spatial analysis and GIS in the study of COVID-19. A review

            This study entailed a review of 63 scientific articles on geospatial and spatial-statistical analysis of the geographical dimension of the 2019 coronavirus disease (COVID-19) pandemic. The diversity of themes identified in this paper can be grouped into the following categories of disease mapping: spatiotemporal analysis, health and social geography, environmental variables, data mining, and web-based mapping. Understanding the spatiotemporal dynamics of COVID-19 is essential for its mitigation, as it helps to clarify the extent and impact of the pandemic and can aid decision making, planning and community action. Health geography highlights the interaction of public health officials, affected actors and first responders to improve estimations of disease propagation and likelihoods of new outbreaks. Attempts at interdisciplinary correlation examine health policy interventions for the siting of health/sanitary services and controls, mapping/tracking of human movement, formulation of appropriate scientific and political responses and projection of spatial diffusion and temporal trends. This review concludes that, to fight COVID-19, it is important to face the challenges from an interdisciplinary perspective, with proactive planning, international solidarity and a global perspective. This review provides useful information and insight that can support future bibliographic queries, and also serves as a resource for understanding the evolution of tools used in the management of this major global pandemic of the 21 Century. It is hoped that its findings will inspire new reflections on the COVID-19 pandemic by readers.
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              Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

              Highlights • The spatial association between demographic composition and COVID-19 deaths and cases were evaluated. • Five spatial regression models were implemented for spatial regression modelling. • Demographic composition significantly impacting the overall casualties caused by COVID-19. • Among the variables, population and ageing factors are found to be the most important component. • The spatially predicted COVID-19 cases and deaths were found highly consistent.
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                Author and article information

                Contributors
                Journal
                Geographia Polonica
                Geogr. Pol.
                Institute of Geography and Spatial Organization, Polish Academy of Sciences
                23007362
                00167282
                September 27 2021
                2021
                September 27 2021
                2021
                : 94
                : 3
                : 355-380
                Affiliations
                [1 ]Department of Geoinformation, Institute of Geoecology and Geoinformation Faculty of Geographical and Geological Sciences of the Adam Mickiewicz University
                Article
                10.7163/GPol.0209
                7478e388-4bf9-4adb-b13e-50ef2b9c025e
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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