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


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

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, Faculty of Health, Tabriz University of Medical Sciences,Tabriz, Iran
Abstract:   (287 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?
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.
Full-Text [PDF 871 kb]   (108 Downloads) |   |   Full-Text (HTML)  (87 Views)  
Type of Article: Original article | Subject: Bio-statistics
Received: 2020/01/10 | Revised: 2020/02/5 | Accepted: 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). [DOI:10.1002/jmv.25678]
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.
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). [DOI:10.1101/2020.01.23.916395]
4. Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. 2020(1095-9157 (Electronic.( [DOI:10.1016/j.jaut.2020.102433]
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). [DOI:10.2196/preprints.18844]
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. [DOI:10.1016/j.molmed.2020.02.008]
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. [DOI:10.1001/jama.2020.2565]
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:10.2139/ssrn.3526307]
9. Gralinski LE, Menachery VA-O. Return of the Coronavirus: 2019-nCoV. LID - 10.3390/v12020135 [doi] LID - 135. 2020(1999-4915 (Electronic.( [DOI:10.3390/v12020135]
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
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. [DOI:10.1016/j.scitotenv.2020.140033]
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:10.20431/2454-8693.0401003]
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). [DOI:10.1016/j.sste.2014.05.004]
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. [DOI:10.1016/j.scitotenv.2020.138884]
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-. [DOI:10.1186/s12942-020-00202-8]
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.( [DOI:10.1016/j.jpainsymman.2020.03.041]
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:10.1145/956676.956682]
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. [DOI:10.1186/s12879-018-3534-6]
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.
20. Chung K, Yang D-H, Bell R. Health and GIS: Toward Spatial Statistical Analyses. Journal of medical systems. 2004;28:349-60. [DOI:10.1023/B:JOMS.0000032850.04124.33]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2020 All Rights Reserved | Jorjani Biomedicine Journal

Designed & Developed by : Yektaweb