Temporal and spatial distribution modeling of corona virus spread (Case study: Qom and Mazandaran provinces)

Document Type : Applied Article


1 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran

2 Faculty member of Remote Sensing and GIS Department, Faculty of Geography, University of Tehran

3 University of Tabriz

4 University of Tehran


Corona virus is one of the most contagious and infectious diseases of the 21st century, which has spread worldwide since late 2019 in the form of Wuhan pneumonia.The aim of the present study was to model the spatial and temporal dispersion of the corona using the weighted spatial regression model and compare it with the least squares model during the period from March 24, 2020 to late October 2020 using three corona indices including (patients, deceased and Improved) for Qom and Mazandaran provinces. The results showed that the rate of patients for Qom province during the period showed 44.025 percent. While the death toll reached 4.34 percent, which is more scattered in the northwestern and southern parts of Qom cities, but the rate of improvements for this province showed 61.7 percent, which is mostly the spatial distribution of this improvement. Findings can be seen in the central parts of the province. However, the results obtained from the spatial and temporal distribution of corona for Mazandaran province in the present study were different according to the mentioned models, so that the number of patients for this province during the mentioned period The number reached 35.57 percent, but the death toll showed 2.61 percent, most of which were spatially located in the northwestern and southern parts, including (Ramsar, Tonekabon, Kelardasht, Chalous, Noor and Amol). Mazandaran province is the ruler. While the amount of improvements in Mazandaran province using the least squares during the period from March 24, 2017 to late October 2016, is mostly seen in the central parts and small parts of the southern parts of the province.


[1]. پورقاسمی، حمیدرضا؛ پویان، سهیل؛ فرج‌زاده، زکریا؛ حیدری، بهرام؛ و بابایی، صدیقه (1400). «ارزیابی خطر شیوع و رفتار ویروس کووید 19 با استفاده از میانگین متحرک و مدل‌های چندجمله‌ای (مطالعۀ موردی: استان فارس)»، جغرافیا و برنامه‌ریزی محیطی، دورۀ 32، شمارۀ 1، ص 70-54.
[2]. رهنما، محمدرحیم؛ و بازرگان، مهدی (1399). «تحلیل الگوهای مکانی – زمانی اپیدمی ویروس کووید 19 و مخاطرات آن در ایران»، مدیریت مخاطرات محیطی، دورۀ 7، شمارۀ 2، ص 127‌–‌113.
[3]. سالنامۀ آماری استان مازندران (1395). مرکز آمار ایران.
[4].Abolfazl. M.; Behzad. V.; & Kiara M. R. (2020). “GIS-based spatial modeling of COVID-19 incidence rate in the continental United States”, https://doi.org/10.1016/j.scitotenv.2020.138884.
[5]. American Health Organization. Use of GIS in epidemiology. Epidemiological Bulletin. (2015). 17:1-7.
[6]. Bailley, T.; & Gatrell, A. (2015). Interactive spatial data analysis. 1st ed. Harlow. Longman.
[7]. Blackwood, JC.; Childs LM. (2018). “an introduction to compartmental modeling for the budding infectious disease modeler”, Lett Biomath 20(5), pp: 195-221.
[8]. Chen, S.; Yang, J.; Yang, W.; Wang, C.; & Bärnighausen, T. (2020). “COVID-19 control in China during mass population movements at New Year”, The Lancet 39(5), pp: 764-766.
[9]. Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; & Han, Y. (2020). “Epidemiological and clinical characteristics of 99 cases of 2019 novel corona virus pneumonia in Wuhan, China, a descriptive study Lancet. 39(7), pp: 507-513. Doi: 10.1016/S0140-6736(20)30211-7.
[10]. Fotheringham. A.S, Brunsdon, C.; & Charlton, M. (2002). Geographically Weighted Regression: the analysis of spatially varying relationships.
[11]. Ghaedamini, A. R.; Tofighi, S.; Ghaedamini, H.; Azizian, F.; Amerieon, A.; & Shokri, M. (2012). “A review of some infectious diseases distribution based on geographic information system (GIS) in the area of Chahar Mahal and Bakhtiari, Journal of Police Medicine, 1(2), pp: 113-123.
[12]. Gatto, M.; Bertuzzo, E.; Mari, L.; Miccoli, S.; Carraro, L.; Casagrandi, R.; Rinaldo, A. (2020). “Spread and dynamics of the COVID-19 epidemic in Italy, E ects of emergency containment measures. Proc. Natl. Acad. Sci. USA, 1(17), pp: 10484–10491.
[13]. Ibrahim, A. (2020). “GIs Application for modeling Covid -19 risk in the makkah region Saudi risk Arabiabased on population and densityhttps://doi. Org/ 10.21608/ejec.2020.115873.
[14]. Jia, J.; Ding, J.; Liu, S.; Liao, G.; Li, J.; & Duan, B. (2021). “Modeling the Control of COVID-19, Impact of Policy Interventions and Meteorological Factors, Vol. 151, No. 7, pp: 231-321.
[15]. Kistemann, T.; Dangendorf, F.; & Schweikart, J. (2015). “New perspectives on the use of Geographical Information Systems in environmental health sciences, Int J Hyg Environ Health 20(5), pp: 169 - 181. https:// doi. Org/10.1078/1438-4639-00145.
[16]. Kandwal, R.; Garg, PK.; & Garg, RD. (2009).  “Health GIS and HIV/ AIDS studies: Perspective and retrospective, Journal Biomed Inform, (4)2, pp: 748-755. https:// doi.org/ 10.1016/j.jbi.2009.04.008.
[17]. Lu, R.; Zhao, X.; Li, J.; Niu, P.; Yang, B.; & Wu, H. (2020). “Genomic characterization and epidemiology of 2019 novel corona virus: implications for virus origins and receptor binding, Lancet. (39)5, pp: 565-574. https:// doi. Org/ 10.1016/S0140-6736(20)30251-8
[18].Leung, M.K.; Xiong, H.Y.; Lee, L.J.; & Frey, B.J. (2020). “Deep learning of the tissue-regulated splicing code. Bioinformatics, 30, 121–129.
[19]. Lee, S.I. (2000). “Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I”, Journal of geographical systems, (3)4, pp: 369-385.
[20]. Mollalo, A.; Vahedi, B.; & Rivera, K. (2020). “GIS-based spatial modeling of COVID-19 incidence rate in the continental United States, Science of the Total Environment, (7)28, pp: 1-8. https://doi. Org/10.1016/j.scitotenv.138884.
[21]. Propastin, P.; & Kappas, M. (2008). “Reducing uncertainty in modeling the NDVI–precipitation relationship: a comparative study using global and local regression techniques, GISci Remote Sens, (4)5:47–67.
[22]. Wang, Z.; & Xu, X. (2020). “ScRNA-seq profiling of human testes reveals the presence of the ACE2 receptor, a target for SARS-CoV-2 infection in spermatogonia, Leydig and Sertoli Cells 9(4):920.
[23]. World Health Organization. (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Retrieved from. https://www.who.int/docs/defaultsource/coronaviruse/who-china-joint-mission-oncovid-19-final-report.pdf.
[24]. World Health Organization. 10 facts on neglected tropical diseases. (2011). Available from: URL: http://www.who.int/features/factfiles/ neglected_ tropical_ diseases/en.
[25]. Wu, F.; Chen, YM; Wang, W.; & Song, ZG.; Hu, Y. (2020). “A new corona virus associated with human respiratory disease in China, Nature. https:// doi. Org/ 10.1038/s41586-020-2202-3.
[26]. Zhou, P.; Yang, XL.; Wang, XG.; Hu, B.; Zhang, L.; & Zhang, W. (2020). “A pneumonia outbreak associated with a new corona virus of probable bat origin, Nature, (5)21, pp: 270-273.