1. Bherwani, H., Anjum, S., Kumar, S., Gautam, S., Gupta, A., Kumbhare, H., ... & Kumar, R. (2021). "Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective," Environment, Development and Sustainability, 23(4), 5846-5864. [DOI:10.1007/s10668-020-00849-0]

2. Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Shamsi, S. R. F., Babaei, S., ... & Sadeghian, F. (2020). "Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)," International Journal of Infectious Diseases, 98, 90-108. [DOI:10.1016/j.ijid.2020.06.058]

3. Tabasi, M., & Alesheikh, A. A. (2017). "A Review of the Applications of Agent Based Simulation in Epidemic Diseases (Case study: Cutaneous Leishmaniasis)," Geospatial Engineering Journal, 8(2), 11-23, (in persian).

4. Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). "Spatial analysis and GIS in the study of COVID-19. A review," Science of The Total Environment, 739, 140033. [DOI:10.1016/j.scitotenv.2020.140033]

5. Giuliani, D., Dickson, M. M., Espa, G., & Santi, F. (2020). "Modelling and predicting the spatio-temporal spread of COVID-19 in Italy," BMC infectious diseases, 20(1), 1-10. [DOI:10.1186/s12879-020-05415-7]

6. Pourghasemi, H. R., Pouyan, S., Farajzadeh, Z., Sadhasivam, N., Heidari, B., Babaei, S., & Tiefenbacher, J. P. (2020). "Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models," Plos one, 15(7), e0236238. [DOI:10.1371/journal.pone.0236238]

7. Ramírez, I. J., & Lee, J. (2020). "COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis," International journal of environmental research and public health, 17(11), 3856. [DOI:10.3390/ijerph17113856]

8. Melin, P., Monica, J. C., Sanchez, D., & Castillo, O. (2020). "Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps," Chaos, Solitons & Fractals, 138, 109917. [DOI:10.1016/j.chaos.2020.109917]

9. Mollalo, A., Vahedi, B., & Rivera, K. M. (2020). "GIS-based spatial modeling of COVID-19 incidence rate in the continental United States," Science of the total environment, 728, 138884. [DOI:10.1016/j.scitotenv.2020.138884]

10. Mollalo, A., Rivera, K. M., & Vahedi, B. (2020). "Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States," International journal of environmental research and public health, 17(12), 4204. [DOI:10.3390/ijerph17124204]

11. Saba, A. I., & Elsheikh, A. H. (2020). "Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks," Process safety and environmental protection, 141, 1-8. [DOI:10.1016/j.psep.2020.05.029]

12. Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020). "Neural network based country wise risk prediction of COVID-19," Applied Sciences, 10(18), 6448. [DOI:10.3390/app10186448]

13. Charandabi, N. K., & Gholami, A. (2021). "COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network," In COVID-19 Pandemic, Geospatial Information, and Community Resilience (pp. 211-226). CRC Press. [DOI:10.1201/9781003181590-19]

14. Samany, N. N., Toomanian, A., Maher, A., Hanani, K., & Zali, A. R. (2021). "The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment-case study: Tehran city," Land use policy, 109, 105725. [DOI:10.1016/j.landusepol.2021.105725]

15. Borghi, P. H., Zakordonets, O., & Teixeira, J. P. (2021). "A COVID-19 time series forecasting model based on MLP ANN," Procedia Computer Science, 181, 940-947. [DOI:10.1016/j.procs.2021.01.250]

16. Kafieh, R., Arian, R., Saeedizadeh, N., Amini, Z., Serej, N. D., Minaee, S., ... & Haghjooy Javanmard, S. (2021). "Covid-19 in iran: Forecasting pandemic using deep learning," Computational and mathematical methods in medicine, 2021. [DOI:10.1155/2021/6927985]

17. Shao, Q., Xu, Y., & Wu, H. (2021). "Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression," Computational and Mathematical Methods in Medicine, 2021. [DOI:10.1155/2021/7196492]

18. Shariati, M. et al. (2020) "Spatial analysis of COVID-19 and exploration of its environmental and Socio-demographic risk factors using spatial statistical methods: A case study of Iran," Health in Emergencies & Disasters Quarterly, 5(3), pp. 145-154. [DOI:10.32598/hdq.5.3.358.1]

19. Talkhi, N. et al. (2021) "Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods," Biomedical signal processing and control, 66(102494), p. 102494. [DOI:10.1016/j.bspc.2021.102494]

20. Mansour, S. et al. (2021) "Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR)," Sustainable cities and society, 65(102627), p. 102627. [DOI:10.1016/j.scs.2020.102627]

21. Scott, L. M., & Janikas, M. V. (2010). "Spatial statistics in ArcGIS," In Handbook of applied spatial analysis (pp. 27-41). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-03647-7_2]

22. Kyriakidis, P. C., & Journel, A. G. (1999). "Geostatistical space-time models: a review," Mathematical geology, 31(6), 651-684. [DOI:10.1023/A:1007528426688]

23. Shojaei, S. R. H., Waghei, Y., & Mohammadzadeh, M. (2018). "Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran," Journal of Applied Statistics, 45(8), 1476-1483. [DOI:10.1080/02664763.2017.1375468]

24. Mitchel, A. (2005). "The ESRI Guide to GIS analysis, Volume 2: Spartial measurements and statistics," ESRI Guide to GIS analysis.

25. Saxena, R., Nagpal, B. N., Das, M. K., Srivastava, A., Gupta, S. K., Kumar, A., ... & Baraik, V. K. (2012). "A spatial statistical approach to analyze malaria situation at micro level for priority control in Ranchi district, Jharkhand," The Indian journal of medical research, 136(5), 776.

26. Zhou, H., Deng, Z., Xia, Y., & Fu, M. (2016). "A new sampling method in particle filter based on Pearson correlation coefficient," Neurocomputing, 216, 208-215. [DOI:10.1016/j.neucom.2016.07.036]

27. Wheeler, D. C., & Páez, A. (2010). "Geographically weighted regression," In Handbook of applied spatial analysis (pp. 461-486). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-03647-7_22]

28. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). "Geographically weighted regression: the analysis of spatially varying relationships," John Wiley & Sons.

29. Mohammadinia, A., Saeidian, B., Pradhan, B., & Ghaemi, Z. (2019). "Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches," BMC infectious diseases, 19(1), 1-18. [DOI:10.1186/s12879-019-4580-4]

30. Mas, J. F., & Flores, J. J. (2008). "The application of artificial neural networks to the analysis of remotely sensed data," International Journal of Remote Sensing, 29(3), 617-663. [DOI:10.1080/01431160701352154]

31. Ahmed, A. R. (2021). "Dynamics of Infectivity and Fatality of COVID-19 Pandemic," Int J Cur Res Rev|, Vol, 13(05), 109. [DOI:10.31782/IJCRR.2021.13516]

32. Lin, J. M., & Billa, L. (2021). "Spatial prediction of flood-prone areas using geographically weighted regression," Environmental Advances, 6, 100118. [DOI:10.1016/j.envadv.2021.100118]

33. Steel, R. G. (1960). "Principles and procedures of statistics: with special reference to the biological sciences," (No. 04; QA276, S82.).

34. Glantz, S. A., Slinker, B. K., & Neilands, T. B. (1990). "Primer of Applied Regression and Analysis of Variance," McGraw-Hill. Inc., New York.

35. Draper, N. R., & Smith, H. (1998). "Applied regression analysis," (Vol. 326). John Wiley & Sons. [DOI:10.1002/9781118625590]

36. Willmott, C. J., & Matsuura, K. (2005). "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance," Climate research, 30(1), 79-82. [DOI:10.3354/cr030079]

37. Hyndman, R. J., & Koehler, A. B. (2005). "Another look at measures of forecast accuracy," Monash University.

38. Hyndman, R. J., & Koehler, A. B. (2006). "Another look at measures of forecast accuracy," International journal of forecasting, 22(4), 679-688. [DOI:10.1016/j.ijforecast.2006.03.001]

39. Pontius, R. G., Thontteh, O., & Chen, H. (2008). "Components of information for multiple resolution comparison between maps that share a real variable," Environmental and Ecological Statistics, 15(2), 111-142. [DOI:10.1007/s10651-007-0043-y]

40. Willmott, C. J., & Matsuura, K. (2006). "On the use of dimensioned measures of error to evaluate the performance of spatial interpolators," International Journal of Geographical Information Science, 20(1), 89-102. [DOI:10.1080/13658810500286976]

41. Tiktak, A., van der Linden, A. M., & van der Pas, L. J. (1998). "Application of the pesticide transport assessment model to a field study in a humic sandy soil in Vredepeel, The Netherlands," Pesticide science, 52(4), 321-336. https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T [DOI:10.1002/(SICI)1096-9063(199804)52:43.0.CO;2-T]

42. https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T [DOI:10.1002/(SICI)1096-9063(199804)52:43.0.CO;2-T]

43. Bherwani, H., Anjum, S., Kumar, S., Gautam, S., Gupta, A., Kumbhare, H., ... & Kumar, R. (2021). "Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective," Environment, Development and Sustainability, 23(4), 5846-5864. [DOI:10.1007/s10668-020-00849-0]

44. Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Shamsi, S. R. F., Babaei, S., ... & Sadeghian, F. (2020). "Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)," International Journal of Infectious Diseases, 98, 90-108. [DOI:10.1016/j.ijid.2020.06.058]

45. Tabasi, M., & Alesheikh, A. A. (2017). "A Review of the Applications of Agent Based Simulation in Epidemic Diseases (Case study: Cutaneous Leishmaniasis)," Geospatial Engineering Journal, 8(2), 11-23, (in persian).

46. Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). "Spatial analysis and GIS in the study of COVID-19. A review," Science of The Total Environment, 739, 140033. [DOI:10.1016/j.scitotenv.2020.140033]

47. Giuliani, D., Dickson, M. M., Espa, G., & Santi, F. (2020). "Modelling and predicting the spatio-temporal spread of COVID-19 in Italy," BMC infectious diseases, 20(1), 1-10. [DOI:10.1186/s12879-020-05415-7]

48. Pourghasemi, H. R., Pouyan, S., Farajzadeh, Z., Sadhasivam, N., Heidari, B., Babaei, S., & Tiefenbacher, J. P. (2020). "Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models," Plos one, 15(7), e0236238. [DOI:10.1371/journal.pone.0236238]

49. Ramírez, I. J., & Lee, J. (2020). "COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis," International journal of environmental research and public health, 17(11), 3856. [DOI:10.3390/ijerph17113856]

50. Melin, P., Monica, J. C., Sanchez, D., & Castillo, O. (2020). "Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps," Chaos, Solitons & Fractals, 138, 109917. [DOI:10.1016/j.chaos.2020.109917]

51. Mollalo, A., Vahedi, B., & Rivera, K. M. (2020). "GIS-based spatial modeling of COVID-19 incidence rate in the continental United States," Science of the total environment, 728, 138884. [DOI:10.1016/j.scitotenv.2020.138884]

52. Mollalo, A., Rivera, K. M., & Vahedi, B. (2020). "Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States," International journal of environmental research and public health, 17(12), 4204. [DOI:10.3390/ijerph17124204]

53. Saba, A. I., & Elsheikh, A. H. (2020). "Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks," Process safety and environmental protection, 141, 1-8. [DOI:10.1016/j.psep.2020.05.029]

54. Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020). "Neural network based country wise risk prediction of COVID-19," Applied Sciences, 10(18), 6448. [DOI:10.3390/app10186448]

55. Charandabi, N. K., & Gholami, A. (2021). "COVID-19 Spatiotemporal Hotspots and Prediction Based on Wavelet and Neural Network," In COVID-19 Pandemic, Geospatial Information, and Community Resilience (pp. 211-226). CRC Press. [DOI:10.1201/9781003181590-19]

56. Samany, N. N., Toomanian, A., Maher, A., Hanani, K., & Zali, A. R. (2021). "The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment-case study: Tehran city," Land use policy, 109, 105725. [DOI:10.1016/j.landusepol.2021.105725]

57. Borghi, P. H., Zakordonets, O., & Teixeira, J. P. (2021). "A COVID-19 time series forecasting model based on MLP ANN," Procedia Computer Science, 181, 940-947. [DOI:10.1016/j.procs.2021.01.250]

58. Kafieh, R., Arian, R., Saeedizadeh, N., Amini, Z., Serej, N. D., Minaee, S., ... & Haghjooy Javanmard, S. (2021). "Covid-19 in iran: Forecasting pandemic using deep learning," Computational and mathematical methods in medicine, 2021. [DOI:10.1155/2021/6927985]

59. Shao, Q., Xu, Y., & Wu, H. (2021). "Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression," Computational and Mathematical Methods in Medicine, 2021. [DOI:10.1155/2021/7196492]

60. Shariati, M. et al. (2020) "Spatial analysis of COVID-19 and exploration of its environmental and Socio-demographic risk factors using spatial statistical methods: A case study of Iran," Health in Emergencies & Disasters Quarterly, 5(3), pp. 145-154. [DOI:10.32598/hdq.5.3.358.1]

61. Talkhi, N. et al. (2021) "Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods," Biomedical signal processing and control, 66(102494), p. 102494. [DOI:10.1016/j.bspc.2021.102494]

62. Mansour, S. et al. (2021) "Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR)," Sustainable cities and society, 65(102627), p. 102627. [DOI:10.1016/j.scs.2020.102627]

63. Scott, L. M., & Janikas, M. V. (2010). "Spatial statistics in ArcGIS," In Handbook of applied spatial analysis (pp. 27-41). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-03647-7_2]

64. Kyriakidis, P. C., & Journel, A. G. (1999). "Geostatistical space-time models: a review," Mathematical geology, 31(6), 651-684. [DOI:10.1023/A:1007528426688]

65. Shojaei, S. R. H., Waghei, Y., & Mohammadzadeh, M. (2018). "Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran," Journal of Applied Statistics, 45(8), 1476-1483. [DOI:10.1080/02664763.2017.1375468]

66. Mitchel, A. (2005). "The ESRI Guide to GIS analysis, Volume 2: Spartial measurements and statistics," ESRI Guide to GIS analysis.

67. Saxena, R., Nagpal, B. N., Das, M. K., Srivastava, A., Gupta, S. K., Kumar, A., ... & Baraik, V. K. (2012). "A spatial statistical approach to analyze malaria situation at micro level for priority control in Ranchi district, Jharkhand," The Indian journal of medical research, 136(5), 776.

68. Zhou, H., Deng, Z., Xia, Y., & Fu, M. (2016). "A new sampling method in particle filter based on Pearson correlation coefficient," Neurocomputing, 216, 208-215. [DOI:10.1016/j.neucom.2016.07.036]

69. Wheeler, D. C., & Páez, A. (2010). "Geographically weighted regression," In Handbook of applied spatial analysis (pp. 461-486). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-03647-7_22]

70. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). "Geographically weighted regression: the analysis of spatially varying relationships," John Wiley & Sons.

71. Mohammadinia, A., Saeidian, B., Pradhan, B., & Ghaemi, Z. (2019). "Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches," BMC infectious diseases, 19(1), 1-18. [DOI:10.1186/s12879-019-4580-4]

72. Mas, J. F., & Flores, J. J. (2008). "The application of artificial neural networks to the analysis of remotely sensed data," International Journal of Remote Sensing, 29(3), 617-663. [DOI:10.1080/01431160701352154]

73. Ahmed, A. R. (2021). "Dynamics of Infectivity and Fatality of COVID-19 Pandemic," Int J Cur Res Rev|, Vol, 13(05), 109. [DOI:10.31782/IJCRR.2021.13516]

74. Lin, J. M., & Billa, L. (2021). "Spatial prediction of flood-prone areas using geographically weighted regression," Environmental Advances, 6, 100118. [DOI:10.1016/j.envadv.2021.100118]

75. Steel, R. G. (1960). "Principles and procedures of statistics: with special reference to the biological sciences," (No. 04; QA276, S82.).

76. Glantz, S. A., Slinker, B. K., & Neilands, T. B. (1990). "Primer of Applied Regression and Analysis of Variance," McGraw-Hill. Inc., New York.

77. Draper, N. R., & Smith, H. (1998). "Applied regression analysis," (Vol. 326). John Wiley & Sons. [DOI:10.1002/9781118625590]

78. Willmott, C. J., & Matsuura, K. (2005). "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance," Climate research, 30(1), 79-82. [DOI:10.3354/cr030079]

79. Hyndman, R. J., & Koehler, A. B. (2005). "Another look at measures of forecast accuracy," Monash University.

80. Hyndman, R. J., & Koehler, A. B. (2006). "Another look at measures of forecast accuracy," International journal of forecasting, 22(4), 679-688. [DOI:10.1016/j.ijforecast.2006.03.001]

81. Pontius, R. G., Thontteh, O., & Chen, H. (2008). "Components of information for multiple resolution comparison between maps that share a real variable," Environmental and Ecological Statistics, 15(2), 111-142. [DOI:10.1007/s10651-007-0043-y]

82. Willmott, C. J., & Matsuura, K. (2006). "On the use of dimensioned measures of error to evaluate the performance of spatial interpolators," International Journal of Geographical Information Science, 20(1), 89-102. [DOI:10.1080/13658810500286976]

83. Tiktak, A., van der Linden, A. M., & van der Pas, L. J. (1998). "Application of the pesticide transport assessment model to a field study in a humic sandy soil in Vredepeel, The Netherlands," Pesticide science, 52(4), 321-336. https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T [DOI:10.1002/(SICI)1096-9063(199804)52:43.0.CO;2-T]

84. https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T https://doi.org/10.1002/(SICI)1096-9063(199804)52:4<321::AID-PS734>3.0.CO;2-T [DOI:10.1002/(SICI)1096-9063(199804)52:43.0.CO;2-T]