Abstract

In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait. Stationarity test, cointegration test, Granger causality and stability test, test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants (O3, SO2, NO2, CO, and PM10) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants (O3, PM10, NO2, temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide (SO2), NO2, temperature and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants (O3, SO2, NO2, CO, and PM10) has a significant impact on COVID-19 admitted cases prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.

Cite as

Alsaber, A., Setiya, P., Al-Sultan, A. & Pan, J. 2022, 'Exploring the impact of air pollution on COVID-19 admitted cases: evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait', Japanese Journal of Statistics and Data Science. https://doi.org/10.1007/s42081-022-00165-z

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Last updated: 02 July 2022
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