ABSTRACT
Malaysia reported its first COVID-19 case on January 25, 2020, and the cases have continued to grow, necessitating the implementation of additional measures. Hence, determining the factors responsible for the significant increase in COVID-19 cases is the top priority issue for the government to take necessary action and ultimately restrain this virus before the vaccine availability. Researchers had predicted that air pollution had an indirect relationship with COVID-19 in terms of virus infections. As a result, this study focuses on the link between the Air Pollutant Index (API) and COVID-19 infections. The initial data set consists of daily confirmed COVID-19 cases in Malaysia and API readings obtained from the Ministry of Health (MOH) and the Department of the Environment (DOE). The results show that Klang (S22) recorded the highest mean of API which at 62.70 while the lowest is at Limbang (S37) (25.37). Next, due to the implementation of Movement Control Order (MCO) in Malaysia and reducing social movement, 27 stations recorded a good level of API compare to the stations that recorded moderate and unhealthy levels. There is positive relationship between API and COVID-19 at each of the region which are North 0.4% (R2=0.004), Central 2.1% (R2=0.021), South 0.04% (R2=0.0004), East 1.6% (R2=0.016), Sarawak 0.2% (R2=0.002), meanwhile Sabah recorded negative correlation at 4.3% (R2=0.043). To conclude, the API value did not have a strong relationship with the rising number of COVID-19 daily cases © 2022 by Asian Association for Atmospheric Environment
ABSTRACT
Background The novel coronavirus disease (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic, which are subject to potential bias. In this study, we aimed to assess and compare the impact of lockdown among the Punjab, Delhi, and Gujarat states of India using the Auto Regressive Integrated Moving Average (ARIMA) model by comparing forecasted COVID-19 data with real-time data. Methods We analyzed the COVID-19 data of Indian states from the index case until May 17, 2020. Auto Regressive Integrated Moving Average (1,1,3) (0,0,0) model was used to forecast the possible cumulative cases until May 17, from data up to May 3, and compared with real-time data. Recovery rate, case-fatality rate, and test per millions of states were collated. Results The trend of cumulative cases in Punjab was moving downward below the forecasted lower confidence limit (R-2 = 0.9799), whereas the cumulative case trend of Delhi was moving along the forecasted upper confidence limit with the forecasted data until May 3 (R-2 = 0.9971) and the trend of cumulative cases was below the forecasted upper confidence limit (R-2 = 0.9992) in Gujarat. Conclusions In Gujarat and Delhi, the lockdown was not effective in controlling the rise in COVID-19 cases even after the 56th day of lockdown, whereas the Punjab state succeeded in preventing havoc of COVID-19. In lieu of lockdown, using facemasks and improving ventilation in closed workspace settings, crowded spaces, and close-contact settings are more pragmatic than keeping away from others in India.