Dr Parna Mandal is a Research Fellow in Health Data Science at the London School of Hygiene & Tropical Medicine, working on NeoShield, a Wellcome Trust- and Gates Foundation-funded multi-country research and implementation programme addressing neonatal sepsis and antimicrobial resistance in Malawi and Zambia, in partnership with the Malawi-Liverpool-Wellcome Research Programme and the Zambia National Public Health Institute. Her research focuses on developing and evaluating data-driven approaches to improve the detection and management of infections in hospitalised newborns, with the aim of reducing deaths from neonatal sepsis and the burden of antimicrobial resistance. This includes developing ward-level outbreak detection and early-warning systems and clinical decision support for suspected neonatal sepsis, bringing together routinely collected clinical and microbiological data to support timely and practical decision-making. At LSHTM, she is a member of the Antimicrobial Resistance Centre, the MARCH Centre for Maternal, Adolescent, Reproductive & Child Health, and the Centre for Data and Statistical Science for Health.

Before joining LSHTM, Parna was a Research Fellow at the University of Leeds, where she investigated how human mobility, behaviour and urban structure influence cholera transmission and the effectiveness of targeted public health interventions, using agent-based modelling alongside population and geospatial data.

She completed her PhD in Applied Mathematics and Biomedical Engineering at the University of Glasgow (2024), where she investigated optimal drug-delivery strategies for preventing and treating biofilm infections associated with medical devices through mathematical modelling. During her MSc in Mathematics and its Applications at Central European University (2020), she worked on malaria epidemiology and targeted intervention strategies.

Her broader research interests lie in applying quantitative and data-driven methods to infectious disease and health research, particularly where these approaches can support better clinical and public health decision-making.