Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Abstract

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

“…According to previous studies (Meng et al, 2005;Wang et al, 2008), researchers have focused on the existence of spatial effects in the diffusion through Moran's I statistic for COVID-19 and other pathogens in China (Kang et al, 2020;Li et al, 2007;Zhang et al, 2020). The spatial dynamics of COVID-19 outbreak have been also tested using global Moran's I statistic for other countries (Bag et al, 2020;Shariati et al, 2020).…”

“…According to previous studies (Meng, Wang, Liu, Wu, & Zhong, 2005; Wang, Christakos, Han, & Meng, 2008), researchers have focused on the existence of spatial effects in the diffusion through Moran's I statistic for COVID‐19 and other pathogens in China (Kang, Choi, Kim, & Choi, 2020; Li, Calder, & Cressie, 2007; Zhang, Rao, Wu, Huang, & Dai, 2020). The spatial dynamics of COVID‐19 outbreak have been also tested using global Moran's I statistic for other countries (Bag, Ghosh, Biswas, & Chatterjee, 2020; Rahman, Islam, & Islam, 2020; Shariati, Mesgari, Kasraee, & Jahangiri‐rad, 2020).…”

Spatio-temporal analysis between the incidence of COVID-19 and human development in Mato Grosso do Sul, Brazil

“…The hierarchical method of Ward (Ward, 1963) (28) was used for agglomeration, due to the success of this method in studies that aim to obtain similar groups, mainly in biometeorological and collective health studies (Oliveira Júnior et al, 2019 ; Holh et al, 2020; Shariati et al, 2020)(29, 17, 30).…”

Spatial Analysis of Skin Cancer: Separating Body Parts Exposed and Not Exposed to the Sun

“…This measure examines spatial association at a local scale by comparing the sum of all feature values and the local sum of the values for the relevant features and the importance of its surrounding features (30). Using this statistic, we measured hotspots or high-risk areas (31,32)…”