Forecasting mortality and DALYs from air pollution in SAARC nations

Abstract

Abstract This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP). Comparing the predictive accuracy of the models, Neural Network outperformed other methods, as indicated by Root Mean Square Error (RMSE) values. Specifically, the study finds that deaths and DALYs due to Ambient Particulate Matter pollution are least prevalent in the Maldives, while India and Pakistan exhibit the highest rates, and deaths and DALYs due to Ambient Ozone pollution are lowest in the Maldives and highest in Bangladesh and Pakistan. Moreover, deaths and DALYs attributed to Household Air Pollution (HAP) are lowest in Pakistan and highest in Nepal. These findings underscore the urgent need for air pollution control measures and informed policymaking in SAARC countries to mitigate the escalating health burden associated with air pollution.

Keywords

• SAARC
• Deaths
• DALYs
• APM
• HAP
• AOP