Page 34 - NGTU_paper_withoutVideo
P. 34

Modern Geomatics Technologies and Applications



          6.  References
          1.     Surveillances, V., The epidemiological characteristics of an outbreak of 2019 novel coronavirus
                 diseases (COVID-19)—China, 2020. China CDC weekly, 2020. 2(8): p. 113-122.
          2.     WHO.  World  Health  Organization  (WHO),  2021,  Coronavirus  disease  (COVID-19)  pandemic.
                 2021; Available from: www.who.int/emergencies/diseases/novel-coronavirus-2019.
          3.     Adegboye, O.A., E. Gayawan, and F. Hanna, Spatial modelling of contribution of individual level
                 risk  factors  for  mortality  from  Middle  East  respiratory  syndrome  coronavirus  in  the  Arabian
                 Peninsula. PloS one, 2017. 12(7): p. e0181215.
          4.     Vellingiri,  B.,  et  al.,  COVID-19:  A  promising  cure  for  the  global  panic.  Science  of  the  total
                 environment, 2020. 725: p. 138277.
          5.     Ramírez-Aldana, R., J.C. Gomez-Verjan, and O.Y. Bello-Chavolla, Spatial analysis of COVID-19
                 spread in Iran: Insights into geographical and structural transmission determinants at a province
                 level. PLoS neglected tropical diseases, 2020. 14(11): p. e0008875.
          6.    Ahmadi, M., et al., Investigation of effective climatology parameters on COVID-19 outbreak in Iran.
                 Science of the Total Environment, 2020. 729: p. 138705.
          7.     Mansour,  S.,  et  al.,  Sociodemographic  determinants  of  COVID-19  incidence  rates  in  Oman:
                 Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable
                 cities and society, 2021. 65: p. 102627.
          8.     Ramírez, I.J. and J. Lee, COVID-19 emergence and social and health determinants in Colorado: a
                 rapid spatial analysis.  International journal of environmental research and public health, 2020.
                 17(11): p. 3856.
          9.     Bashir, M.F., et al., Correlation between climate indicators and COVID-19 pandemic in New York,
                 USA. Science of the Total Environment, 2020. 728: p. 138835.
          10.    Jia, J.S., et al., Population flow drives spatio-temporal distribution of COVID-19 in China. Nature,
                 2020. 582(7812): p. 389-394.
          11.    Zhang, C.H. and G.G. Schwartz, Spatial disparities in coronavirus incidence and mortality in the
                 United States: an ecological analysis as of May 2020. The Journal of Rural Health, 2020. 36(3): p.
                 433-445.
          12.    Miller, L.E., R. Bhattacharyya, and A.L. Miller, Spatial analysis of global variability in Covid-19
                 burden. Risk Management and Healthcare Policy, 2020. 13: p. 519.
          13.    Iran's Minister of Health. 2020; Available from: https://behdasht.gov.ir/.
          14.    Statistical Center of Iran. 2020; Available from: https://www.amar.org.ir/.
          15.    Coviello, V. and M. Boggess, Cumulative incidence estimation in the presence of competing risks.
                 The Stata Journal, 2004. 4(2): p. 103-112.
          16.    Zulu, L.C., E. Kalipeni, and E. Johannes, Analyzing spatial clustering and the spatiotemporal nature
                 and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010. BMC infectious
                 diseases, 2014. 14(1): p. 1-21.
          17.    Getis,  A.  and  J.K.  Ord,  The  analysis  of  spatial  association  by  use  of  distance  statistics,  in
                 Perspectives on spatial data analysis. 2010, Springer. p. 127-145.
          18.    Ord,  J.K.  and  A.  Getis,  Local  spatial  autocorrelation  statistics:  distributional  issues  and  an
                 application. Geographical analysis, 1995. 27(4): p. 286-306.
          19.    Songchitruksa, P. and X. Zeng, Getis–Ord spatial statistics to identify hot spots by using incident
                 management data. Transportation research record, 2010. 2165(1): p. 42-51.
          20.    Peeters,  A.,  et  al.,  Getis–Ord’s  hot-and  cold-spot  statistics  as  a  basis  for  multivariate  spatial
                 clustering of orchard tree data. Computers and Electronics in Agriculture, 2015. 111: p. 140-150.


                                                                                                               9
   29   30   31   32   33   34   35   36   37   38   39