Cargando…
Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States
Lockdown measures implemented in response to the COVID-19 pandemic produced sudden behavioral changes. We implement counterfactual time series analysis based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of air pollution reduction attained following stat...
Autores principales: | Dey, Tanujit, Tyagi, Pooja, Sabath, M. Benjamin, Kamareddine, Leila, Henneman, Lucas, Braun, Danielle, Dominici, Francesca |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651777/ https://www.ncbi.nlm.nih.gov/pubmed/34876601 http://dx.doi.org/10.1038/s41598-021-02776-0 |
Ejemplares similares
-
Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis
por: Wu, X., et al.
Publicado: (2020) -
A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA
por: Chakraborty, Sounak, et al.
Publicado: (2022) -
Counterfactual Bell-State Analysis
por: Zaman, Fakhar, et al.
Publicado: (2018) -
Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States
por: Woodward, Sophie M., et al.
Publicado: (2023) -
Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States
por: Dey, Tanujit, et al.
Publicado: (2021)