Forecasting the impact of environmental stresses on the frequent waves of COVID19
Author | Yu, Zhenhua |
Author | Abdel-Salam, Abdel Salam G. |
Author | Sohail, Ayesha |
Author | Alam, Fatima |
Available date | 2022-08-23T10:34:22Z |
Publication Date | 2021-10-01 |
Publication Name | Nonlinear Dynamics |
Identifier | http://dx.doi.org/10.1007/s11071-021-06777-6 |
Citation | Yu, Z., Abdel-Salam, AS.G., Sohail, A. et al. Forecasting the impact of environmental stresses on the frequent waves of COVID19. Nonlinear Dyn 106, 1509–1523 (2021). https://doi.org/10.1007/s11071-021-06777-6 |
ISSN | 0924090X |
Abstract | A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of “nonlinear autoregressive network with exogenous inputs (NARX)” approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of COVID-19, outdoor air pollution is unlikely to be a significant route of transmission. In this research, ANN time series is used to analyze the data resulting from the COVID-19 first and second waves and the forecasted results show that air pollution affects people in different areas of Italy and make more people sick with covid-19. The model is developed based on the disease transmission data of Italy. |
Language | en |
Publisher | Springer |
Subject | Artificial neural network Environmental stress Epidemic forecasting Machine learning SARS-CoV2 |
Type | Article |
Pagination | 1509-1523 |
Issue Number | 2 |
Volume Number | 106 |
ESSN | 1573-269X |
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COVID-19 Research [832 items ]
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Mathematics, Statistics & Physics [736 items ]