Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

Li, Huling; Li, Hui; Ding, Zhongxing; Hu, Zhibin; Chen, Feng; Wang, Kai; Peng, Zhihang; Shen, Hongbing

doi:10.4081/gh.2020.867

Cited by 40 publications

(33 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of GIS technologies and spatial analysis has substantially influenced the understanding of COVID-19, not only for the scientific community but also for the policymakers, and for the public in building a long term response to the ongoing pandemic 94 . Initially, spatial analysis techniques were used as part of predictive modeling to predicts the growth of COVID-19 cases 63 and to model the spatio-temporal variation of confirmed incidences 74 . With the increasing availability of COVID-19 data, a significant number of studies started to analyze the spatial transmission pattern and spread of the virus from Wuhan to other cities in China and the rest of the world 17,18,20,22,26,67,71,89,95 .…”

Section: Discussionmentioning

confidence: 99%

“…A substantial number of studies were found to apply computeraided spatial and statistical analysis and modeling techniques in analyzing distinct aspects of COVID-19. A total of 16 articles were identified that emphasized on computer-aided spatial and statistical analysis and modeling in COVID-19 [69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84] . Most of these works were focused on analyzing the spatial distribution pattern of COVID-19 cases using the confirmed cases data [72][73][74][75][76][77][78] , or news reports of COVID-19 cases as proxy data 79 .…”

Section: Computer-aided Spatial and Statistical Analysis And Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

et al. 2020

View full text Add to dashboard Cite

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic. Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining. Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Although GIS has substantial potential in planning to slow down the spread, surveillance, contact tracing, and identify the trends and hotspots of breakdowns, it was not employed as much as it could have been. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that this geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our systematic review also provides how both scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future. Conclusions: Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Computer-aided Spatial and Statistical Analysis And Modelingmentioning

confidence: 99%

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Both statistics indicate whether the highest/lowest numbers of COVID-19 cases tend to be spatially dependent (i.e. clustering) (Huang et al 2020;Huling et al 2020). Specifically, resultant Z score informs where locations wif either high or low incidence tend to be clustered over space.…”

Section: Spatially Integrated Statisticsmentioning

confidence: 99%

“…In contrast, statistically significant negative and smaller Z scores indicate more clustering of lower incidence of COVID-19 (me.e. cold spot) (Huling et al 2020). Herein, we calculated G * i statistic to analyze spatial clustering of COVID-19 cases for each week independently for the period from April 29 to June 30 and define the corresponding hotspots and cold spots sites.…”

Section: Spatially Integrated Statisticsmentioning

confidence: 99%

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Al‐Kindi

Alkharusi

Alshukaili³

et al. 2020

Earth Syst Environ

View full text Add to dashboard Cite

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran’s I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord $$G_{i}^{*}$$ G i ∗ statistic. The Moran’s I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran’s I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of $$G_{i}^{*}$$ G i ∗ showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans.

show abstract

“…Autocorrelation and cluster analysis regarding cumulative cases of COVID-19 in China have been investigated by means of Global Moran's I and Getis-Ord General G 17 . In 18 Local Moran's I is also used for mapping clusters, along with a logistic regression model to predict the growth of the infection curve and a SEIR model to calculate the rate of spread.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

An Environmental GIS-based Variable Analysis on SARS-CoV-2 in the City of Recife, Brazil

Souza

Mota

Rosa

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Given the increasing rates at which people have been infected by Covid-19 evolving to case-fatality rates on a global scale and the context of there being a world-wide socio-economic crisis, decision-making must be undertaken based on prioritizing effective measures to control and combat the disease since there is a lack of effective drugs. Method: This paper explores the determinant factors of the COVID-19 pandemic and its impacts on Recife, Pernambuco-Brazil by performing both local and global spatial regression analysis on two types of environmental data-sets. Data were obtained from ten specific days between late April and early July 2020, comprehending the ascending, peaking and descending behaviours of the curve of infections.Results: This study highlights clusters of the most affected neighbourhoods and their determinant effects. We have observed the increasing phase with hotspots of confirmed cases in a well-developed and heavily densely-populated neighbourhood of Recife city, then evolving for hotspots of case-fatality rates into areas characterised by having a precarious provision of public services and low-income population. The results also help to understand the influence of the age, income, level of education of the population and, additionally, the people’s access to public services, on the behaviour of the virus across neighbourhoods.Conclusion: This study supports government measures against the spread of Covid-19 in heterogeneous cities, evidencing social inequality as a driver for a high incidence of fatal cases of the disease. Understanding the variables which influence the local dynamics of the virus spread becomes vital for identifying the most vulnerable regions for which prevention actions need to be developed.

show abstract

Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

Cited by 40 publications

References 28 publications

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

An Environmental GIS-based Variable Analysis on SARS-CoV-2 in the City of Recife, Brazil

Contact Info

Product

Resources

About