ArticleNonlinear dynamics2021
Forecasting the impact of environmental stresses on the frequent waves of COVID19.
Article in Nonlinear dynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modeling the crossover behavior of the bacterial infection with the COVID-19 epidemics.Results in physics · 2022Article
- COVID-19 Variants and Transfer Learning for the Emerging Stringency Indices.Neural processing letters · 2022Article
- Forecasting of the efficiency of monoclonal therapy in the treatment of CoViD-19 induced by the Omicron variant of SARS-CoV2.Results in physics · 2022Article
- The impact of vaccination on the spread of COVID-19: Studying by a mathematical model.Physica A · 2022Article
- Piecewise differentiation of the fractional order CAR-T cells-SARS-2 virus model.Results in physics · 2022Article
- Improvement on PDP Evaluation Performance Based on Neural Networks and SGDK-means Algorithm.Soft computing · 2022Article
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Authors and funding
4 authors.
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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.
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