Study on the Spatial Relationships in the Number of Covid-19 Patients based on Ordinary Least Squares Regression and Moran's Spatial Autocorrelation Test Case study (Iran, Lorestan Province)

Document Type : Original Research Article


1 Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti, Tehran, Iran

2 Laboratory expert, Shahid Beheshti University, Tehran, Iran


To examine the spatial relationships of the number of patients, effective factors in increasing the number of patients should be determined. The information layers of dependent variables and independent variables were plotted in the GIS system. In the next stage, ordinary least squares regression and the Moran spatial correlation test were used to investigate the significant relationship between the dependent variable and each of the explanatory variables. The results show that the most influential variables in increasing the number of patients are in the first place the urban working population variable and in the second and third place the total population and total working population. The average variable of ambient temperature along with the mentioned variables is an important factor in the release of Covid-19. In the study of the average ambient temperature in three periods, it was found that the number of patients increased with decreasing temperature. Hence, to vaccinate the target groups, it is recommended to vaccinate the urban working population in the first stage, and also the observance of health protocols is strongly recommended in areas where the average ambient temperature is lower.


Main Subjects

Adegboye, O. A., Gayawan, E., & Hanna, F., 2017. Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula. PloS One, 12(7), e0181215.
Ahmar, A. S., & Boj,  E., 2020. Will COVID-19 confirmed cases in the USA reach 3 million? A forecasting approach by using SutteARIMA Method. Current Research in Behavioral Sciences, 1, 100002.
Allain-Dupré, D., Chatry, I., Michalun, V., & Moisio, A., 2020. The territorial impact of COVID-19: Managing the crisis across levels of government. OECD.
Amdaoud, M., Arcuri, G., & Levratto, N.,2021. Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe. The European Journal of Health Economics, 1–14.
Analytica, O., 2020. The WHO’s COVID-19 pandemic declaration may be late. Expert Briefings.
Bhunia, G. S., Roy, S., & Shit,  P. K.,2021. Spatio-temporal analysis of COVID-19 in India--a geostatistical approach. Spatial Information Research, 1–12.
Biswas, K., & Sen, P., 2020. Space-time dependence of corona virus (COVID-19) outbreak. ArXiv Preprint ArXiv:2003.03149.
Boulos, M. N. K., & Geraghty, E. M., 2020. 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. BioMed Central.
Esri., 2011. Geographic information systems and pandemic influenza planning and response. Available from: https:// planning.pdf.
Esri., 2020. Mapping epidemics. Available from:
Estimated, U. S., 2009. Influenza planning and response. Future, 100, H3N2.
Hashtarkhani, S., Kiani, B., Bergquist, R., Bagheri, N., VafaeiNejad, R., & Tara, M., 2020. An age-integrated approach to improve measurement of potential spatial accessibility to emergency medical services for urban areas. The International Journal of Health Planning and Management, 35(3), 788–798.
Huo,X.N., Li,H.,Sun,D.F., Zhou,  L.D., & Li,  B.G.,2012. Combining geostatistics with Moran’s I analysis for mapping soil heavy metals in Beijing, China. International journal of environmental research and public health, 9(3): 995-1017.
Keikhosravi, G.,Fadavi, S.F.,2021. Impact of the inversion and air pollution on the number of patients with Covid-19 in the metropolitan city of Tehran. Urban Climate 37, 100867.
Kumar, A.,2020. Modeling geographical spread of COVID-19 in India using network-based approach. Medrxiv.
Li,  H., Xu, X.-L., Dai, D.-W., Huang, Z.-Y., Ma,  Z., & Guan, Y.-J., 2020. Air pollution and temperature are associated with increased COVID-19 incidence: a time series study. International Journal of Infectious Diseases, 97, 278–282.
Mohammad Ebrahimi, S., Mohammadi, A., Bergquist, R., Dolatkhah, F., Olia, M., Tavakolian, A., Pishgar, E., & Kiani, B.,2021. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health, 21(1), 1–18.
Nasiri, R., Akbarpour, S., Zali,  A. R., Khodakarami,  N., Boochani,  M. H., Noory, A. R., & Soori, H., 2021. Spatio-temporal analysis of COVID-19 incidence rate using GIS: a case study—Tehran metropolitan, Iran. GeoJournal, 1–15.
Parvin, F., Ali, S. A., Hashmi,  S. N. I., & Ahmad,  A.,2021. Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique. Spatial Information Research, 1–16.
Pohlmann,  J.T.,Leitner, D.W.,2003. A Comparison of Ordinary least Squares and Logistic Regression, Ohio Journal of Science.
Qu, G., Li, X., Hu, L., & Jiang, G., 2020. An imperative need for research on the role of environmental factors in transmission of novel coronavirus (COVID-19). ACS Publications.
Robertson, C., & Nelson, T. A., 2014. An overview of spatial analysis of emerging infectious diseases. The Professional Geographer, 66(4), 579–588.
Sarkar, S. K., Ekram, K. M. M., & Das,  P. C., 2021. Spatial modeling of COVID-19 transmission in Bangladesh. Spatial Information Research, 1–12.
Smith, C. D., & Mennis, J.,2020. Peer Reviewed: Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic. Preventing Chronic Disease, 17.
Sohrabi, C., Alsafi,  Z., neill, N., Khan, M., Kerwan,  A., Al-Jabir, A., Iosifidis, C., & Agha, R. .,2020. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery, 76, 71–76.
Su,  Z., McDonnell, D., Cheshmehzangi,  A., Abbas,  J., Li, X., & Cai, Y.,2021. The promise and perils of Unit 731 data to advance COVID-19 research. BMJ Global Health, 6(5), e004772.
Subramanian, S. V, .,Karlsson, O., Zhang, W., & Kim, R.,2020. Geo-mapping of COVID-19 risk correlates across districts and parliamentary constituencies in India. Harvard Data Science Review.
Shariati, M., Mesgari, T., Kasraee, M., & Jahangiri-Rad,  M.,2020. Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020). Journal of Environmental Health Science and Engineering, 18(2), 1499–1507.
Suleyman, G., Fadel, R. A., Malette,  K. M., Hammond,  C., Abdulla, H., Entz, A., Demertzis, Z., Hanna, Z., Failla, A., Dagher, C., & others., 2020. Clinical characteristics and morbidity associated with coronavirus disease 2019 in a series of patients in metropolitan Detroit. JAMA Network Open, 3(6), e2012270--e2012270.
Tang, W., Liao, H., Marley, G., Wang,  Z., Cheng, W., Wu, D., & Yu, R.,2020. The changing patterns of coronavirus disease 2019 (COVID-19) in China: a tempogeographic analysis of the severe acute respiratory syndrome coronavirus 2 epidemic. Clinical Infectious Diseases, 71(15), 818–824.
Tran,  A., Deparis, X., Dussart, P., Morvan, J., Rabarison, P., Remy, F., Polidori,  L., & Gardon, J., 2004. Dengue spatial and temporal patterns, French Guiana, 2001. Emerging Infectious Diseases, 10(4), 615.
Tu, J, Xia, Z-G.,2008.Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Science of the total environment 407, 358-378.
Wang, H., Du, Z., Wang, X., Liu, Y., Yuan, Z., Liu,Y., & Xue,  F.,2015. Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models. International Journal of Infectious Diseases, 34, 66–70.
Wu, M., Long, R., & Chen, H.,2021. Public psychological distance and spatial distribution characteristics during the COVID-19 pandemic: a Chinese context. Current Psychology, 1–20.
Wu, S., Wang, X., & Su,  J.,2021. Statistical analysis of the community lockdown for COVID-19 pandemic. Applied Intelligence, 1–18.
Wu, X., & Zhang, J.,2021. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research, 1–15.
Yang, C., Sha, D., Liu, Q., Li, Y., Lan, H., Guan, W. W., Hu, T., Li, Z., Zhang,  Z., Thompson, J. H., & others.,2020. Taking the pulse of COVID-19: A spatiotemporal perspective. International Journal of Digital Earth, 13(10), 1186–1211.
Zhang,C., Luo, L., Xu,  W., & Ledwith, V.,2008. Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of the total environment, 398(1): 212-221.