Spatial-temporal prediction of high-risk areas of Covid-19 disease using Geographically Weighted Regression and Multi Layer Perceptron neural network

Karimi, Mohammad; Ilkhani khosrowshahi, Milad; Kaffash Charandabi, Neda

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Spatial-temporal prediction of high-risk areas of Covid-19 disease using Geographically Weighted Regression and Multi Layer Perceptron neural network

Mohammad Karimi ^*, Milad Ilkhani khosrowshahi, Neda Kaffash Charandabi

Abstract: (36 Views)

One of the most contagious diseases of recent years is Covid 19 disease (Corona), which has spread from Wuhan, China to the rest of the world since late 2019, causing many crises and a profound impact on the world and our daily lives. In most people infected with the disease, it causes respiratory symptoms, the severity of which depends on the person's immune system. The main objectives of this study are to discover the clusters and predict the high risk areas of Covid 19 disease, compare the efficiency of the two proposed methods and determine the effective parameters by city. In this study, Moran index and hot spot analysis index were used to investigate the distribution pattern of disease incidence rates and clusters, respectively, and Pearson correlation coefficient was used to determine effective disease parameters. In this study, statistical data of Covid 19 disease of East Azerbaijan province in the city along with environmental and topographic, health, economic and urban facilities data in the period from February 22, 2020 to November 20, 2020, were collected weekly. According to the results, the incidence of Covid 19 disease during this period has passed two peaks and according to the maps obtained from the two models, in some weeks the GWR model and in some weeks the MLP model was the superior model; also, for the GWR model, the goodness of fit index value is 0.8985 and the normalized root mean square error is 0.0822 and for the MLP model is 0.8226 and 0.1340, respectively, which shows that the GWR method is more appropriate. Sensitivity analysis of different parameters showed that the parameters of the Covid 19 incidence rate of the previous week and wind speed are more important than other modeled parameters in this issue. In this study, effective parameters were extracted separately for each city and a local model was presented that compared to the general state of the model, the local model had better accuracy than the general model of the MLP method.

Article number: 2

Keywords: Covid_19 (corona virus), spatio-temporal distribution, prediction modeling, multilayer perceptron neural network, geographic weight regression, GIS

Type of Study: Research | Subject: GIS

References

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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]

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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]

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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]

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