Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Impact of the COVID-19 pandemic on clean fuel programmes in India and ensuring sustainability for household energy needs.

Clean cooking energy strategies are critical for reducing air pollution, improving health, and achieving related Sustainable Development Goals. The recent COVID-19 lockdowns may impact the transition towards clean cooking fuels. The nationwide lockdown is likely to affect key factors such as energy access, income, transportation, etc., that play a role in decisions influencing household fuel use. The rural population already bears the burden of poverty and may not be able to afford and access clean cooking fuels during the lockdown. They are thus vulnerable to reversion to their traditional cooking methods using solid biomass fuels. The household air pollution caused due to the use of polluting fuels increases their susceptibility to non-communicable diseases, and thus may intensify the risk and severity of COVID-19 infection. Hence, there is an urgent need to expand sustainable energy solutions worldwide. The present study applies the DPSIR modeling framework to establish a set of comprehensive indicators for addressing the transition towards clean cooking fuels during the COVID-19 pandemic. The study also provides insights on various strategies adopted in India in response to the COVID-19 pandemic for maintaining continuity of delivering benefits under a clean cookstove program. The study offers future directions to ensure the transition towards cleaner fuels and sustainability.