Imperial College London


Faculty of MedicineSchool of Public Health

Lecturer in Biostatistics



+44 (0)20 7594 1177monica.pirani




526Norfolk PlaceSt Mary's Campus





My research is based upon the development of Bayesian statistical methods with primary application to environmental and health data.

Current methodological research focuses on spatial and spatio-temporal statistics, time-series analysis, methods for the integration of multi-scale sources of data and models for understanding the health consequences of exposure to (multiple) pollutants and changes in climate



Pirani M, Mason A, Hansell A, et al., 2020, A flexible hierarchical framework for improving inference in area-referenced environmental health studies, Biometrical Journal: Journal of Mathematical Methods in Biosciences, Vol:62, ISSN:0323-3847, Pages:1650-1669

Blangiardo M, Cameletti M, Pirani M, et al., 2020, Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic, Plos One, Vol:15, ISSN:1932-6203

Wang Y, Pirani M, Hansell A, et al., 2019, Using ecological propensity score to adjust for missing confounders in small area studies, Biostatistics, Vol:20, ISSN:1465-4644, Pages:1-16

Blangiardo M, Pirani M, Kanapka L, et al., 2019, A hierarchical modelling approach to assess multi pollutant effects in time-series studies, Pl O S One, Vol:14, ISSN:1932-6203

Pirani M, Panton A, Purdie DA, et al., 2016, Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux, Science of the Total Environment, Vol:572, ISSN:0048-9697, Pages:1449-1460

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