Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)
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RESEARCH ARTICLE
Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020) Mohsen Shariati 1,2 & Tahoora Mesgari 3 & Mahboobeh Kasraee 3 & Mahsa Jahangiri-rad 4,5 Received: 26 July 2020 / Accepted: 7 October 2020 # Springer Nature Switzerland AG 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. Keywords COVID-19 . Cumulative incidence rates (CIR) . Cumulative mortality rate (CMR) . Hot/cold spots . Spatial analysis
Introduction The “novel coronavirus-infected pneumonia (NCIP)” with unknown causes was reported to the World Health Organization
* Mahsa Jahangiri-rad [email protected] 1
College of Engineering, Faculty of Environment, Department of Environmental Planning, Management and Education, University of Tehran, Tehran, Iran
2
Student Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
3
Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
4
Department of Environmental Health Engineering, School of Health and Medical Engineering, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
5
Water Purification Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
(WHO) by Wuhan
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