Unfolding the SEcrets of LongEvity: Current Trends and future prospects.
A path through morbidity, disability and mortality in Italy and Europe. [Bando MIUR PRIN 2017]

The Research Project

The rapid social, economic, and technological transformations characterizing our society in the recent years are producing several effects on many complex and dynamic processes of human health. We focus, in particular, on the recent upward trend in longevity, and on its relation with current and future morbidity and disability patterns. The joint analysis of such processes, which plays a key role in many public health systems, requires novel qualitative and quantitative paradigms. In fact, unfolding the secrets of longevity, learning the dynamic interrelations between morbidity, disability and mortality, identifying future trends, and devising effective interventions can only proceed under a collective effort by Demographers, Epidemiologists, Social and Data Scientists.

Our mission is to explore the factors and mechanisms of longevity evolution in the recent years and link them with morbidity trends to foster healthy longevity. We will address this goal through a multidisciplinary team of Demographers, Epidemiologists, Social and Data Scientists, who will take advantage from the availability of several datasets to merge public health and epidemiological theories with a data-driven approach based on innovative models.


An understanding of the complex relations between morbidity, disability and mortality, requires a preliminary effort towards linking and reconstructing the current sources of information for these processes.

Statistical Models

Morbidity, disability and mortality are complex dynamic processes characterized by several underlying patterns of relations, thus requiring advances in the modeling techniques, beyond the available ones.


The rich databases and the new models are expected to shed light on several complex patterns of longevity and disability processes. This will open new avenues toward improving intervention strategies for healthy aging.

Some Features

The main research directions of the project are deeply motivated by social, epidemiological and demographic questions of fundamental interest and immediate application for policy making. Due to this, the project is expected to provide outcomes of relevant impact in different directions.

Data Availability

Well-organized sources of information on morbidity, disability and mortality will be made available in data repositories.

Code and Publications

Codes and publications will be timely shared through the SELECT website, arXiv and our GitHub repository.

Workshops and Meetings

We will constantly share results with the scientific community and policy-maker via workshops and conference talks.

Training and Recruiting

The project will actively involve, train and recruit a network of junior researchers to ensure future advances in this field.

News and Media

A peculiarity of the research group is its multidisciplinarity, which fosters collaboration between units, especially for such cross-cutting themes. This will be further stimulated by organizing regular meetings of the steering committee, workshops and conferences. Moreover, due to the importance of the project, substantial efforts will be devoted at timely sharing the advances with the scientific communities involved and the policy-makers.

14-15 October 2019

Formal kick-off of SELECT project

The formal kick-off of the SELECT project is planned on 14-15 October 2019. In this occasion we will discuss the initial outputs with a main focus on data preparation.

28 May 2020

Intermediate [online] meeting

The first intermediate meeting of the SELECT project is planned on 28 May 2020 [online]. We will present some scientific outputs of the project and we will discuss the COVID-19 emergency.

22 Oct 2021

Intermediate workshop

The intermediate workshop is planned on 22 October 2021. We will discuss some scientific outputs of the project, and provide an opportunity to discuss three macro themes (Covid-19, mortality, morbidity).

We will be hiring soon!

The project will be characterized by an active involvement, training and recruiting a network of junior researchers in Demography, Epidemiology, Social Science and Data Science to ensure future advances.


The outputs of the project will be published in statistical, demographic and epidemiological journals to motivate an increasing collaboration among such fields. The focus will be on the project topics, but we will maintain also an open mindset to a broad set of methods which may be useful for the success of SELECT.

List of Datasets List of Codes Useful References
Articles in refereed journals
[link] Aliverti E. and Russo, M. (2021). Stratified stochastic variational inference for high-dimensional network factor model Journal of Computational and Graphical Statistics.
[link] Aliverti E. and Dunson, D. B. (2021). Composite mixture of log-linear models for categorical data, with applications to psychiatric studies. The Annals of Applied Statistics.
[link][code] Stefanucci M. and Mazzuco, S. (to appear). Analyzing Cause-Specific Mortality Trends using Compositional Functional Data Analysis. Journal of the Royal Statistical Society - Series A.
[link][code] Leger A.E. and Mazzuco, S. (to appear). What can we learn from functional clustering of mortality data? An application to HMD data. European Journal of Population.
[link] Rigon,T., Durante,D (2020). Tractable Bayesian density regression via logit stick-breaking priors. Journal of Statistical Planning and Inference. 211, 131--142.
[link] Legramanti,S., Durante,D., Dunson,D (2020). Bayesian cumulative shrinkage for infinite factorizations. Biometrika. 3, 745--752.
[link] Zanotto, L., Canudas-Romo, V. and Mazzuco, S. (2020). A mixture-function mortality model: illustration of the evolution of premature mortality. European Journal of Population.
[link][code] Canudas-Romo, V., Mazzuco, S. and Aldair, T. (2020). Cause of death decomposition of cohort survival comparisons. International Journal of Epidemiology - Education Corner.
[link] Fazle Rabbi, A.M. and Mazzuco, S. (2020). Mortality forecasting with the Lee-Carter method: Adjusting for smoothing and lifespan disparity. European Journal of Population.
[link] Frank, J., Abel, T., Campostrini, S., Cook, S., Lin, V. K. and McQueen, D. V. (2020). The Social Determinants of Health: Time to Re-Think? International Journal of Environmental Research and Public Health.
[link] Jones,A., Rice,N., Zantomio,F (2020). Acute health shocks and labour market outcomes: evidence from the post crash era. Economics & Human Biology. 36, 100811.
[link] Cavapozzi,D., Zantomio,F (2020). Senior Tourism in Italy: the Role of Disability and Socioeconomic Characteristics. Journal of Population Ageing.
[link] Rizzi,D., Simionato,C., Zantomio,F (2019). Older People Health and Access to Healthcare: A Retrospective Look at Inequality Dynamics over the Past Decade. Politica economica. 3, 335--366.
Books and books chapters
[link] Stefanucci M., Mazzuco, S.Inspecting cause-specific mortality curves by simplicial functional data analysis. In Pollice, A., Salvati, N., and Schirripa Spagnolo, F. [Eds] Book of Short Papers SIS 2020
[link] Mazzuco, S., and Keilman, N. [Eds] (2020). Developments in Demographic Forecasting. Springer Nature.
[link] Mazzuco, S., Keilman, N. (2020). Introduction. In Mazzuco, S. and Keilman, N. [Eds] Developments in Demographic Forecasting.
[link] Aliverti, E., Durante, D. and Scarpa, B. (2020). Projecting proportionate age-specific fertility rates via Bayesian skewed processes. In Mazzuco, S. and Keilman, N. [Eds] Developments in Demographic Forecasting.
[link] Mazzuco, S., Suhrcke M. and Zanotto L. (2021). How to measure premature mortality? A proposal combining “relative” and “absolute” approaches. Manuscript.
[link] Aliverti E. Mazzuco, S. and Scarpa B. (2021). Dynamic modeling of mortality via mixtures of skewed distribution functions. Manuscript.
[link] Aliverti E. and Russo, M. (2021). Dynamic modeling of the Italians’ attitude towards Covid-19. Manuscript.
[link] Legramanti, S., Rigon, T. and Durante, D. (2020). Bayesian testing for exogenous partition structures in stochastic block models. Manuscript.
[link] Legramanti,S., Rigon,T., Durante,D., Dunson,D (2020). Extended stochastic block models. Manuscript
[link] Fasano,A., Durante,D (2020). A class of conjugate priors for multinomial probit models which includes the multivariate normal one. Manuscript
[link] Fasano, A., Durante, D. and Zanella, G. (2020). Asymptotically exact variational Bayes for high-dimensional binary regression models. Manuscript.
[link] Wade, S., Piccarreta, R., Cremaschi, A. and Antoniano, I. (2019). Multivariate density regression for censored, constrained and binary traits. Manuscript.


[link] Stefanucci, M. (2021). Analyzing cause-specific mortality trends using Compositional Functional Data Analysis. PAA 2021 Conference (Online).
[link] Stefanucci, M. (2020). Analyzing cause-specific mortality trends using Compositional Functional Data Analysis. Invited seminar by UCD Working Group on Statistical Learning (Online).
[link] Legramanti, S. (2020). Bayesian cumulative shrinkage for infinite factorizations. Bayes Comp 2020.
[link] Mazzuco, S. (2019). Socioeconomic inequalities in mortality: survey-based estimates for European countries using SHARE and EU-SILC data. Demographic Aspects of Human Wellbeing - Wittgenstein Centre Conference 2019.
[link] Durante, D. (2019). Asymptotically exact variational Bayes for high-dimensional binary regression models. CMStatistics 2019.
[link] Piccarreta, R. (2019). Multivariate density regression for censored, constrained, and binary traits. IISA 2019 Conference.


[link] SELECT intermediate workshop (22 October 2021). University Ca' Foscari Venezia. Venezia, Italy.
[link] SELECT online intermediate meeting (28 May 2020). University of Padova. Padova, Italy.
[link] SELECT kick-off meeting (14-15 October 2019). Ca' Foscari University. Venice, Italy.




Talks at Conferences


People Involved


Meetings Organized

Project Coordinators

To provide successful outcomes the project requires deep interaction and constant feedbacks, currently limited, between experts from Epidemiology, Social Science, Demography and Data Science. The research team has been created to accomplish this goal.


Project Coordinator
Full Prof. of Social Statistics
Ca' Foscari University


Padova Unit Coordinator
Associate Prof. of Demography
University of Padova


Bocconi Unit Coordinator
Assistant Prof. of Statistics
Bocconi University


Novara Unit Coordinator
Full Prof. of Hygiene
University of Piemonte Orientale

The other members of the project, along with the expertise of each research unit, are listed below.

Ca' Foscari University [Social Science]: Andrea Pastore, Francesca Zantomio, Giovanni Rataj, Simone Antonio Gerzeli, Lucia Zanotto.
University of Padova [Demography]: Bruno Scarpa, Mauro Bernardi, Ahbab Mohammad Fazle Rabbi, Emanuele Aliverti, Marco Stefanucci.
Bocconi University [Data Science]: Raffaella Piccarreta, Alessia Melegaro, Sirio Legramanti.
University of Piemonte Orientale [Epidemiology]: Carmen Aina, Massimiliano Panella, Silvia Caristia, Anilbabu Payedimarri.

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