The Impact of Air Quality and Meteorology on COVID-19 Cases at Kuala Lumpur and Selangor, Malaysia and Prediction Using Machine Learning

Abstract

Emissions from motor vehicles and industrial sources have contributed to air pollution worldwide. The effect of chronic exposure to air pollution is associated with the severity of the COVID-19 infection. This ecological investigation explored the relationship between meteorological parameters, air pollutants, and COVID-19 cases among residents in Selangor and Kuala Lumpur between 18 March and 1 June in the years 2019 and 2020. The air pollutants considered in this study comprised particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO), whereas wind direction (WD), ambient temperature (AT), relative humidity (RH), solar radiation (SR), and wind speed (WS) were analyzed for meteorological information. On average, air pollutants demonstrated lower concentrations than in 2019 for both locations except PM2.5 in Kuala Lumpur. The cumulative COVID-19 cases were negatively correlated with SR and WS but positively correlated with O3, NO2, RH, PM10, and PM2.5. Overall, RH (r = 0.494; p < 0.001) and PM2.5 (r = −0.396, p < 0.001) were identified as the most significant parameters that correlated positively and negatively with the total cases of COVID-19 in Kuala Lumpur and Selangor, respectively. Boosted Trees (BT) prediction showed that the optimal combination for achieving the lowest Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and a higher R-squared (R2) correlation between actual and predicted COVID-19 cases was achieved with a learning rate of 0.2, a minimum leaf size of 7, and 30 learners. The model yielded an R2 value of 0.81, a RMSE of 0.44, a MSE of 0.19, and a MAE of 0.35. Using the BT predictive model, the number of COVID-19 cases in Selangor was projected with an R2 value of 0.77. This study aligns with the existing notion of connecting meteorological factors and chronic exposure to airborne pollutants with the incidence of COVID-19. Integrated governance for holistic approaches would be needed for air quality management post-COVID-19 in Malaysia.

Univariate and Multivariate Long Short Term Memory (LSTM) Model to Predict Covid-19 Cases in Malaysia Using Integrated Meteorological Data

“…Deep learning specifically the Long Short Term Memory (LSTM) algorithm has been successfully used to anticipate dengue and influenza outbreaks by Leonenko et al, (2017) [7]. Furthermore, previous research has examined whether relative humidity and absolute humidity are critical in transmitting COVID-19.…”