May 22, 2023


May 23, 2023

My research aims at developing statistical methodology and innovative data analytics, anchored in the Bayesian hierarchical modelling paradigm and machine learning, to improve statistical inference in environment and health studies. I lead the environmental and health statistics group at Imperial College. The main projects I work on are described below.

Epidemiological Surveillance

I have been developing and applying spatio-temporal disease mapping and surveillance models for chronic disease epidemiology, focusing on the detection of areas characterised by unusual trends.

For instance, during the COVID-19 pandemic I worked on hierarchical spatio-temporal models for estimating excess mortality and hospital admissions (Konstantinoudis, Cameletti, et al. 2022; Blangiardo et al. 2020)

Spatial excess mortality estimates in 5 countries, from Konstantinoudis, Cameletti, et al. (2022)

Spatio-temporal models

My research employs flexible, spatio-temporal semi- and nonparametric models for large complex data from environmental and epidemiological studies.

  • Recently I have started exploring how wastewater-based epidemiology can be used to predict disease processes which are changing in space and time. We specified a geostatistical model using the network of sites across England and predicted the value at Lower Tier Local Authority level.

Predicted wastewater SARS-CoV2 concentration Li et al. (2023)

  • I am also working on Bayesian non-parametric models to estimate the air pollution sources from compositional data or particle sizes for particulate matter and linking these to health data.

Concentration of particle sizes, 2016-2019

Source profiles in London, 2016-2019

  • I have also developed a framework to integrate individual and aggregated data from multiple sources to better provide confounder adjustment at the area level for environmental epidemiology studies (Wang et al. 2019; Pirani et al. 2020)

DAG of the integrative model

RSS-Turing Data Lab

The work of the Turing-RSS Lab I have been involved with has focused on (i) spatio-temporal modelling of health inequalities during the COVID pandemic (Padellini et al. 2022), (ii) data integration to de-bias prevalence data (Nicholson, Lehmann, et al. 2022)and on (iii) an interoperable concept which proposes a modular way to approach modelling (Nicholson, Blangiardo, et al. 2022)

Time varying effect of ethnicity and deprivation on COVID-19 test positivity and prevalence

Climate Change

I have been involved in several projects related to climate change; in particular

Percentage asthma hospitalisation risk for every 1°C increase in the daily mean summer temperature by sex and region

  • to assess the impact of changing environmental factors on scorpionism using disease mapping and ecological regression models [] and to use join spatio-temporal models to evaluate the effect of environmental factors on Dengue and Zika cases in Brazil.

Natural experiments for policy evaluation

This research programme aims to provide a methodological framework to draw causal inference for longitudinal data, accounting for their hierarchical nature, the existence of time series components and the use of negative outcome controls. We account for the hierarchical nature of the data (in space, such as for electoral wards and/or across organisational units such as schools/universities), which is a key characteristic in many social and epidemiological applications. The flexibility of the approach allow to incorporate trends deviating from linearity (for instance polynomial or splines) and, in line with existing literature, use variable selection techniques such as spike-and-slabs to build a parsimonious regression model for the outcome.


Blangiardo, Marta, Michela Cameletti, Monica Pirani, Gianni Corsetti, Marco Battaglini, and Gianluca Baio. 2020. “Estimating Weekly Excess Mortality at Sub-National Level in Italy During the COVID-19 Pandemic.” PloS One 15 (10): e0240286.
Konstantinoudis, Garyfallos, Michela Cameletti, Virgilio Gómez-Rubio, Inmaculada León Gómez, Monica Pirani, Gianluca Baio, Amparo Larrauri, et al. 2022. “Regional Excess Mortality During the 2020 COVID-19 Pandemic in Five European Countries.” Nature Communications 13 (1): 482.
Konstantinoudis, Garyfallos, Cosetta Minelli, Holly Ching Yu Lam, Elaine Fuertes, Joan Ballester, Bethan Davies, Ana Maria Vicedo-Cabrera, Antonio Gasparrini, and Marta Blangiardo. 2023. “Asthma Hospitalisations and Heat Exposure in England: A Case–Crossover Study During 2002–2019.” Thorax.
Konstantinoudis, Garyfallos, Cosetta Minelli, Ana Maria Vicedo-Cabrera, Joan Ballester, Antonio Gasparrini, and Marta Blangiardo. 2022. “Ambient Heat Exposure and COPD Hospitalisations in England: A Nationwide Case-Crossover Study During 2007–2018.” Thorax 77 (11): 1098–1104.
Li, Guangquan, Hubert Denise, Peter Diggle, Jasmine Grimsley, Chris Holmes, Daniel James, Radka Jersakova, et al. 2023. “A Spatio-Temporal Framework for Modelling Wastewater Concentration During the COVID-19 Pandemic.” Environment International 172: 107765.
Nicholson, George, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert JB Goudie, et al. 2022. “Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality.” Statistical Science: A Review Journal of the Institute of Mathematical Statistics 37 (2): 183.
Nicholson, George, Brieuc Lehmann, Tullia Padellini, Koen B Pouwels, Radka Jersakova, James Lomax, Ruairidh E King, et al. 2022. “Improving Local Prevalence Estimates of SARS-CoV-2 Infections Using a Causal Debiasing Framework.” Nature Microbiology 7 (1): 97–107.
Padellini, Tullia, Radka Jersakova, Peter J Diggle, Chris Holmes, Ruairidh E King, Brieuc CL Lehmann, Ann-Marie Mallon, George Nicholson, Sylvia Richardson, and Marta Blangiardo. 2022. “Time Varying Association Between Deprivation, Ethnicity and SARS-CoV-2 Infections in England: A Population-Based Ecological Study.” The Lancet Regional Health-Europe 15: 100322.
Pirani, Monica, Alexina J Mason, Anna L Hansell, Sylvia Richardson, and Marta Blangiardo. 2020. “A Flexible Hierarchical Framework for Improving Inference in Area-Referenced Environmental Health Studies.” Biometrical Journal 62 (7): 1650–69.
Wang, Yingbo, Monica Pirani, Anna L Hansell, Sylvia Richardson, and Marta Blangiardo. 2019. “Using Ecological Propensity Score to Adjust for Missing Confounders in Small Area Studies.” Biostatistics 20 (1): 1–16.