Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic

Smith, Charlotte; Mennis, Jeremy

doi:10.5888/pcd17.200246

Cited by 67 publications

(50 citation statements)

References 36 publications

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“…Since the COVID-19 spreading represented geographical dependence, geographic information systems (GIS) can combine divergent spatial datasets based on georeferencing, promoting the integration of health data with contextual characteristics. At the same time, descriptive modeling research that depends on GIS strength has examined the spatial associations of COVID-19 with socioeconomic and environmental characteristics (Smith and Mennis 2020 ). Currently, the uneven distribution of the COVID-19 pandemic is well enough to represent patterns of spatial heterogeneity with GIS spatial tools, which incorporate multidimensional social, economic, and health consequences, exposing geographical inequity and a long-term impact on global health accurately, regardless of linear or nonlinear regressions (Cássaro and Pires 2020 ; Rosenkrantz et al 2020 ; Smith and Mennis 2020 ; Guliyev 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, descriptive modeling research that depends on GIS strength has examined the spatial associations of COVID-19 with socioeconomic and environmental characteristics (Smith and Mennis 2020 ). Currently, the uneven distribution of the COVID-19 pandemic is well enough to represent patterns of spatial heterogeneity with GIS spatial tools, which incorporate multidimensional social, economic, and health consequences, exposing geographical inequity and a long-term impact on global health accurately, regardless of linear or nonlinear regressions (Cássaro and Pires 2020 ; Rosenkrantz et al 2020 ; Smith and Mennis 2020 ; Guliyev 2020 ). For instance, Ahmar and Boj 2020 predicted COVID-19 confirmed cases in the USA with the SutteARIMA method.…”

Section: Introductionmentioning

confidence: 99%

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Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)

Zhang

2021

Environ Sci Pollut Res

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Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial–temporal scale, this research appropriately analyzed the spatial–temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial–temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)

Zhang

2021

Environ Sci Pollut Res

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“…In order to seek solutions for the control of the disease, several studies have been done, such as the development of GIS (Geographic Information System) platforms for monitoring the number of confirmed cases as well as deaths and recoveries of Covid-19 in regions of China, USA, Australia, Brazil, and Canada (6)(7)(8)(9)(10)(11)(12)(13)(14). Other studies include the analysis of the Covid-19 virus genome, like the researches carried out by Randhawa et al (15), in order to analyze and understand the virus.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Silva

Lima

Silva

et al. 2021

Front. Public Health

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Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

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“…Since the COVID-19 preading represented geographical dependence, GIS can combine divergent spatial data sets based on georeferencing, promoting the integration of health data with contextual characteristics. At the same time, descriptive modeling research that depends on GIS strength has examined the spatial associations of COVID-19 with socioeconomic and environmental characteristics (Smith et al, 2020). Currently, the uneven distribution of the COVID-19 pandemic is well enough to represent patterns of spatial heterogeneity with GIS spatial tools, which incorporate multidimensional social, economic, and health consequences, exposing to geographical inequity and a long-term impact on global health accurately, no matter what linear-regressions or non-linear regressions (Rosenkrantz et al, 2020;Smith, 2020;Guliyev, 2020 ) For instance, Ansari Saleh Ahmar predicted COVID-19 confirmed cases in the U.S with SutteARIMA method (Ahmar et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The drawback is the lack of environmental variables to underpin the model. Although population mobility, age, and race, as significant factors, are mentioned in the research, Mollalo only considered black females infection risk of COVID as an explanatory variable, it is limited to get the outcome on the most vulnerable groups of COVID-19 (Mollalo 2020, Smith 2020, Lakhani 2020, Rosenkrantz et al, 2000. Different methods are used to observe the goodness-of-fit test of the regression (e.g., multiple geographical weighted regression method and geographical weighted random forest method, but they do not account for the significance of the single variable.…”

mentioning

confidence: 99%

Exploration of Temporal-Spatially Varying Impacts on COVID-19 Cumulative Case in Texas Using Geographically Weighted Regression (GWR)

Wu¹,

Zhang

2021

Preprint

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Since COVID-19 is extremely menacing human’s health, it is a significant to expose on its fator’s impacts for curbing the virus spreading. To tackle the complexity of COVID-19 expansion in spatial-temporal scale, This research is approriatedly analyzed the spatial-temporal heterogeneity at county-level in Texas. First,factors impacts of COVID-19 are captured on social, economic, and environmental multiple-facets and the Communality is extracted through Principal Component Analysis (PCA). Second, this research is used COVID-19 CC as the dependent variable and the common factors as the independent variable. According to the virus prevailing hierarchy, spatial-temporal disparity is are categorized four quarters in the modeling GWR analysis according to the virus prevailing hierarchy. The findings are exibited that GWR models provided higher fitness, more geodata-oriented information than OLS models. In Texas El Paso, Odessa, Midland, Randall and Potter County areas, population, hospitalization, and age structure presented static, positive influences on COVID-19 cumulative casesm, indicating they should be adopt stringent stratgies in curbing COVID-19. Winter is the most sensitive season for the virus spreading, implying the last quarter should be pay more attention to prevent the virus and take pracutions. This research are expected to provide references for preventing and controlling COVID-19 and related infectious dieseaces, evidences for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.

show abstract

Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic

Cited by 67 publications

References 36 publications

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Exploration of Temporal-Spatially Varying Impacts on COVID-19 Cumulative Case in Texas Using Geographically Weighted Regression (GWR)

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