Volume 8, Issue 1 (3-2020)                   Jorjani Biomed J 2020, 8(1): 24-33 | Back to browse issues page


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Nazari S S, norouzi S, Asghari Jafar-abadi M. How is Coronavirus distributed in the world? A Spatial-Temporal Assessment Using Geographic Information System Approach. Jorjani Biomed J 2020; 8 (1) :24-33
URL: http://goums.ac.ir/jorjanijournal/article-1-702-en.html
1- Department of Zanjan University of Medical Sciences
2- Department of Zanjan University of Medical Sciences , snorouzibiostatistics@gmail.com
3- Road Traffic Injury Research Center, Tabriz University of Medical Sciences,Tabriz, Iran
Abstract:   (5252 Views)
Background and objective: Prevalence and the spread of novel Coronavirus (2019-ncov) cause significant life and financial destruction worldwide and is the cause of severe respiratory infection in humans. The present study briefly reviews the latest information on how the virus is distributed around the world. The main question of the study are: 1- In which geographic regions of the world is the Coronavirus more concentrated? 2- Is the distribution of the Coronavirus geographically stable?
Material and Methods: To answer these questions, we first began collecting and studying the available scientific resources. The required data was obtained from a daily report of confirmed, recovered, and deaths by the Coronavirus separated by state which was collected from January 22, 2020 to Jun 19, 2020. Based on analyzing available patterns in spatial statistics tool in ArcGIS and geostatistical models, we examined how the Coronavirus was distributed around the world.
Results: The spread of the disease is increasing all over the world. Using the results of Map 1, it is seen that the spread of Corona virus has a trend and starts in China and then spreads to the Middle East, Europe and the United States in a linear manner. The results also show that the prevalence of mortality is higher than that of recovery. Central mean and median for all types (Confirmed, Recovered and death) are close to each other. Death mean and median was close to Western countries and Recovered mean and median was close to Eastern countries, while confirmed mean and median was located in the center.
Conclusion: Based on spatial statistics tool in ArcGIS and geostatistical models, we examined how the Coronavirus was distributed around the world. Our results showed that the spread of Corona virus had a trend and started in China and then spread to the Middle East, Europe and the United States in a likely linear manner.
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Type of Article: Original article | Subject: Bio-statistics
Received: 2020/01/10 | Accepted: 2020/03/1 | Published: 2020/03/1

References
1. Lu H, Stratton CW, Tang YA-O. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. 2020(1096-9071 (Electronic). [view at publisher] [DOI] [Google Scholar]
2. Bogoch, II, Watts A, Thomas-Bachli A, Huber C, Kraemer MUG, Khan K. Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. LID - 10.1093/jtm/taaa008. [Google Scholar]
3. Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. 2020(1878-3511 (Electronic). [view at publisher] [DOI] [Google Scholar]
4. Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. 2020(1095-9157 (Electronic.( [view at publisher] [DOI] [Google Scholar]
5. Gibson LA-O, Rush DA-O. Novel Coronavirus in Cape Town Informal Settlements: Feasibility of Using Informal Dwelling Outlines to Identify High Risk Areas for COVID-19 Transmission From A Social Distancing Perspective. 2020(2369-2960 (Electronic). [view at publisher] [DOI] [Google Scholar]
6. Sun J, He W-T, Wang L, Lai A, Ji X, Zhai X, et al. COVID-19: Epidemiology, Evolution, and Cross-Disciplinary Perspectives. Trends in Molecular Medicine. 2020;26(5):483-95. [view at publisher] [DOI] [Google Scholar]
7. Bai Y, Yao L, Wei T, Tian F, Jin D-Y, Chen L, et al. Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA. 2020;323(14):1406-7. [view at publisher] [DOI] [Google Scholar]
8. Liu T, Hu J, Kang M, Lin L, Zhong H, Xiao J, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv. 2020:2020.01.25.919787. [DOI] [Google Scholar]
9. Gralinski LE, Menachery VA-O. Return of the Coronavirus: 2019-nCoV. LID - 10.3390/v12020135 [doi] LID - 135. 2020(1999-4915 (Electronic.( [DOI] [Google Scholar]
10. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. 2020 [view at publisher] [Google Scholar]
11. Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment. 2020;739:140033. [view at publisher] [DOI] [Google Scholar]
12. Pirnazar M, Ostad-Ali-Askari K, Eslamian S, Singh V, Dalezios N, Ghane M, et al. Change Detection of Urban Land Use and Urban Expansion Using GIS and RS, Case Study: Zanjan Province, Iran. International Journal of Constructive Research in Civil Engineering. 2018;4. [DOI] [Google Scholar]
13. Lovett DA, Poots AJ, Clements JT, Green SA, Samarasundera E, Bell D. Using geographical information systems and cartograms as a health service quality improvement tool. 2014(1877-5853 (Electronic). [view at publisher] [DOI] [Google Scholar]
14. Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of The Total Environment. 2020;728:138884. [view at publisher] [DOI] [Google Scholar]
15. Kamel Boulos MN, Geraghty EM. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr. 2020;19(1):8-. [view at publisher] [DOI] [Google Scholar]
16. Lakhani A. Which Melbourne Metropolitan Areas Are Vulnerable to COVID-19 Based on Age, Disability, and Access to Health Services? Using Spatial Analysis to Identify Service Gaps and Inform Delivery. 2020(1873-6513 (Electronic). [view at publisher] [DOI] [Google Scholar]
17. Krivoruchko K, Gotway CA, Zhigimont A. Statistical tools for regional data analysis using GIS. Proceedings of the 11th ACM international symposium on Advances in geographic information systems; New Orleans, Louisiana, USA: Association for Computing Machinery; 2003. p. 41-8. [DOI] [Google Scholar]
18. Tewara MA, Mbah-Fongkimeh PN, Dayimu A, Kang F, Xue F. Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000-2015. BMC infectious diseases. 2018;18(1):636. [view at publisher] [DOI] [Google Scholar]
19. Saxena R, Nagpal BN, Das MK, Srivastava A, Gupta SK, Kumar A, et al. A spatial statistical approach to analyze malaria situation at micro level for priority control in Ranchi district, Jharkhand. The Indian journal of medical research. 2012;136(5):776-82. [PubMed] [Google Scholar]
20. Chung K, Yang D-H, Bell R. Health and GIS: Toward Spatial Statistical Analyses. Journal of medical systems. 2004;28:349-60. [DOI] [Google Scholar]

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