Selected article for: "sensitivity analysis and time series"

Author: Singh, A.
Title: Ambient air pollution and COVID-19 in National Capital Territory of Delhi, India: a time-series evidence
  • Cord-id: z5l401jl
  • Document date: 2021_6_7
  • ID: z5l401jl
    Snippet: Objectives: This study aimed to explore the short-term health effects of ambient air pollutants PM2.5, PM10, SO2, NO2, O3, and CO on COVID-19 daily new cases and COVID-19 daily new deaths. Study design: A time-series design used in this study. Data were obtained from 1 April2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. Methods: The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases an
    Document: Objectives: This study aimed to explore the short-term health effects of ambient air pollutants PM2.5, PM10, SO2, NO2, O3, and CO on COVID-19 daily new cases and COVID-19 daily new deaths. Study design: A time-series design used in this study. Data were obtained from 1 April2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. Methods: The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases and COVID-19daily new deaths. We also conducted sensitivity analysis using the population mobility variable in terms of lockdowns. Results: The GAMs revealed statistically significant associations of ambient air pollu-tants with COVID-19 daily new cases and COVID-19 daily new deaths. Besides, in sensitivity analysis after controlling for the population mobility, these associations became more prominent. ConclusionsThese findings suggest that governments need to give greater considerations to regions with higher concentrations of PM2.5, PM10, SO2, NO2, O3, and CO, since these areas may experience a more serious COVID-19 pandemic or, in general, any respiratory disease.

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