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Dweepobotee Brahma
Dweepobotee Brahma is Assistant Professor at the Centre for Mathematical and Computational Economics in the School of AI and Data Science at IIT-Jodhpur. Her research interests lie in the intersection of applied econometrics (including machine learning and causal inference techniques), and development economics and health economics. She studies maternal and child health outcomes, child mortality and morbidity, malnutrition, immunisation, health insurance, and health financing. Brahma uses machine learning techniques to improve targetting of public policies.
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Using machine learning to target neonatal and infant mortality
India accounts for one-fourth of the world’s neonatal mortalities, and this has likely been exacerbated by the Covid-19 pandemic – due to lockdowns and lack of access to critical antenatal and postnatal care. Analysing 2011-12 India Human Development Survey (IHDS)-II data, this article uses machine learning to build predictive models of neonatal and infant mortality incidence, and identify the early warning signs, and consequently those at high-risk of neonatal and infant mortality.
