Planetary Health Informatics
Welcome to the Planetary Health Informatics (PHI) Lab. Planetary Health research considers the interconnections between our health and changes to our environment. Our approach to research combines artificial intelligence technology , satellite-data, and international real-world data from a range of global health and environmental sources, aiming to improve our understanding of chronic and communicable disease, monitor the impact of changes to the climate and environment on human health, and address sources of health inequality across the globe.
We work closely with an interdisciplinary group of collaborators, including clinicians, engineers, epidemiologists, conservationists, data scientists and public and patient groups across the UK, Europe, Latin America, South Asia and Africa, co-creating equitable and ethical solutions to issues connected to healthcare and climate change.
We teach a number of health data science courses at NDORMS and University-wide including but not limited to the NDORMS/MSD DPhil module on "Observational health data science: epidemiology, machine learning, and health economics", the NIHR BRC training course on “Data analysis: statistics - designing clinical research and biostatistics", and as faculty members, the "Real World Evidence using the OMOP Common Data Model" summer school.
Selected Publications
Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017
Journal article
Khalid S., (2024), The lancet planetary health
Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity.
Journal article
Pineda-Moncusí M. et al, (2024), Sci data, 11
Changes in air pollution exposure after residential relocation and body mass index in children and adolescents: A natural experiment study.
Journal article
Warkentin S. et al, (2023), Environ pollut, 334
Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain.
Journal article
Pineda-Moncusí M. et al, (2023), Rheumatology (oxford), 62, 3592 - 3600