Forecasting the impact of environmental stresses on the frequent waves of COVID19

• Published in: Nonlinear dynamics Vol. 106; no. 2; pp. 1509 - 1523
• Main Authors: Yu, Zhenhua, Abdel-Salam, Abdel-Salam G., Sohail, Ayesha, Alam, Fatima
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2021; Springer Nature B.V
• Subjects: Air pollution; Automotive Engineering; Classical Mechanics; Control; Coronaviruses; COVID-19; Data analysis; Disease control; Disease transmission; Droplets; Dynamical Systems; Engineering; Environmental conditions; Epidemiology; Mathematical models; Mechanical Engineering; Original Paper; Particulate emissions; Respiratory diseases; Severe acute respiratory syndrome coronavirus 2; Stresses; Time dependence; Time series; Vibration; Italy; SARS-CoV2; Artificial neural network; Epidemic forecasting; Machine learning; Environmental stress
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-021-06777-6

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.