Estimating SARS-COV-2 Exposure Indoors in Delhi Given Outdoor Pollution Metrics Using Machine Learning

Abstract The Global Burden of Disease journal by the Lancet(Ritchie and Roser, 2013) and states that one million deaths have occurred from 1990 to 2017 due to air pollution. In 2018, the WHO estimated a death toll of 3.8 million due to indoor pollution(WHO,2018). In these times of the pandemic, it is quintessential for countries like India, with a huge population and high levels of pollution, to take severe measures for controlling pollution. The 2020 US Policy Report in the Lancet(2020) affirmed that there is a positive correlation between the PM2.5 or PM10 particles concentration and COVID-19 infection as the virus uses the particulate matter as a piggyback. The case study here, is based on the Indian urban locality and aims to analyze and estimate the correlations between PM2.5 particles, the AQI, weather conditions and COVID-19 particles using Machine Learning models. The optimum model is also to be used for predicting the outdoor AQI and Covid-19 infection rates in the suburban localities of northwestern Delhi and the data so obtained, would aid to calculating ,and extrapolating the mortality probability due to Covid-19 infection, indoors, in the metropolitan cities of India, like Delhi.