I have a Master degree in Statistics from the Catholic University in Milan, Italy and a PhD in Statistics with concentration in Epidemiology from the University of Milan-Bicocca, Italy.
I am interested in the development and application of quantitative methods to health research, with a specific focus on linking official registers data to external environmental and socio-economic auxiliary information, to inform sampling strategies and modelling.
My research interests include finite population sampling - particularly with reference to epidemiological, public health and environmental applications - and data mining and modelling of electronic health records and questionnaire data. I have been working with more traditional statistical methods (item response theory models, generalised linear mixed models, discrete-margins copulas) and more recent statistical/machine learning paradigms.
I am willing to supervise PhD students on the following areas:
finite population sampling theory and applications (adaptive sampling, health surveys, environmental sampling)
spatio-temporal modelling of health data (breast cancer screening compliance, environmental and social determinants of health)
development of statistical methods for the analysis of chronic diseases for public health purposes.
Andreis F, Conti PL & Mecatti F (2019) On the role of weights rounding in applications of resampling based on pseudopopulations. Statistica Neerlandica, 73 (2), pp. 160-175. https://doi.org/10.1111/stan.12145
Andreis F, Furfaro E & Mecatti F (2018) Methodological perspectives for surveying rare and clustered population: towards a sequentially adaptive approach. In: Perna C, Pratesi M & Ruiz-Gazen A (eds.) Studies in Theoretical and Applied Statistics. SIS 2016. Springer Proceedings in Mathematics & Statistics, 227. 48th Scientific Meeting of the Italian Statistical Society, SIS 2016, Salerno, Italy, 08.06.2016-10.06.2016. Cham, Switzerland: Springer, pp. 15-24. https://doi.org/10.1007/978-3-319-73906-9_2
Andreis F & Bonetti M (2018) A proposal for a two-step sampling design to oversample units responding to prescribed characteristics. Environmental and Ecological Statistics, 25 (1), pp. 139-154. https://doi.org/10.1007/s10651-017-0396-9