Abstract:
There are many types of research on strong relations between air pollution and the respiration system. This paper is related to the effect of air pollution on pandemic case numbers. By analyses of the number of patients and mean air pollution (mu g/m(3)) data by using data mining, it is concluded that there was evidence of this relationship with a significant level alpha = 0,10. This paper estimates the number of patients with coronavirus as a function of daily air pollution and corona case numbers data using single ANN, LSTM, and W-ANN, W-LSTM hybrid methods. This paper explains small, meso, and large scale factors and their role on COVID-19 patients. This finding does not demonstrate a direct cause-effect relationship between air pollution and COVID-19 patients. This study shows the importance of pollution on the number of patients with COVID -19 infection. Although our results have significant uncertainties, we can clearly distinguish the contribution of air pollution to COVID-19 patients. When we used wavelet transformation of air pollution data to estimate COVID-19 patient's numbers, R-2 score is increased in both ANN and LSTM between [0.01- 0.10]. Nevertheless, the actual number of patients is influenced by many additional factors such as the country's health system. After error analysis, sMAPE [3.6-5.9] changed, there is sufficient evidence of model results (M3, M4 Hybrid LSTM) and observation. (0.91<0.96, alpha=<0.01). The performance of hybrid model is 10% better than the simple ANN model.
