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When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to have always a firm grasp on what is the current state of the pandemic, and to envision how the number of infections and possible deaths is going to evolve over the next weeks. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic, specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Silvana Castaldi,1,2,* Ester Luconi,3,* Bruno Alessandro Rivieccio,4,* Patrizia Boracchi,3 Giuseppe Marano,3 Elena Pariani,2 Luisa Romano,2 Francesco Auxilia,1 Federica Nicolussi,5 Alessandra Micheletti,6 Giancarlo Manzi,5 Silvia Salini,5 Massimo Galli,7,* Elia Biganzoli3,* 1Department of Biomedical Sciences for Health, University of Milan, Milan, Italy; 2Fondazione IRCCS Ca’ Granda Ospedale Maggiore di Milano, Milan, Italy; 3Department of Clinical Sciences and Community Health & DSRC, University of Milan, Milan, Italy; 4Niguarda Hospital, AREU, Regional Emergency Service Agency, Milan, Italy; 5Department of Economics, Management and Quantitative Methods & DSRC, University of Milan, Milan, Italy; 6Department of Environmental Science and Policy, University of Milan, Milan, Italy; 7Department of Biomedical and Clinical Sciences “L. Sacco”, University of Milan, Milan, Italy*These authors contributed equally to this workCorrespondence: Silvana CastaldiDepartment Biomedical Sciences for Health, University of Milan, Via Pascal, 36, Milan, 20133, ItalyTel +390255038342Fax +390255033144Email silvana.castaldi@unimi.itIntroduction: The coronavirus disease 2019 (COVID-19) epidemic is an infectious disease which was declared a pandemic and hit countries worldwide from the beginning of the year 2020. Despite the emergency vigilance plans, health systems in all countries experienced a different ratio of lethality, amount of admissions to intensive care units and quarantine management of positive patients. The aim of this study is to investigate whether some epidemiological estimates could have been useful in understanding the capacity of the Italian Regional Health Services to manage the COVID-19 epidemic.Methods: We have compared data between two different Italian regions in the Northern part of Italy (Lombardy and Veneto) and the national data to determine whether different health strategies might be significant in explaining dissimilar patterns of the COVID-19 epidemic in Italy. Data have been extracted from a public database and were available only in an aggregated form.Results: The regions in question displayed two different health policies to face the COVID-19 epidemic: while Veneto’s health service was largely territorially oriented, Lombardy’s strategy was more hospital-centered.Discussion: The key to facing epidemics like this one consists in identifying solutions outside of hospitals. This however requires there be well-trained general practitioners and enough healthcare personnel working outside hospitals.Keywords: COVID-19, epidemic, epidemiological estimates, health systems, region, Italy

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT’s survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.

In this paper we develop a predictive model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. This integration is mainly concerned with COVID-19 cause specific death data which are not available at NUTS-3 level from open official data data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic, specifically the number of susceptible, infected, deceased and recovered people. Predictive model performance is evaluated using comparison with real data.

This paper presents an dashboard developed to analyse the outbreak of the Covid-19 infection in Italy considering daily NUTS-3 data on positive cases provided by the Italian Ministry of Health and on deaths derived from Italian regional authorities' official press conferences. Descriptive time series plots are provided together with a map describing the spatial distribution of province cumulative cases and rates. A section on a proposed time-dependent adjusted SIRD model for NUTS-3 regions is also provided in the dashboard.

Table E1 Description of participating ICUs. Table E2 Multivariate generalized linear model of main outcome. Table E3 Reasons for protocol violation. Table E4 Neuroactive drug doses and hospital charges. Table E5 Daily clinical measurements. Figure E1 Kaplanâ Meier plot for ICU survival estimates. (DOC 3129 kb)

Ex post evaluation of Cohesion Policy programmes 2007-2013, focusing on the European Regional Development Fund (ERDF) and the Cohesion Fund (CF).

This paper applies a novel technique of opinion analysis over social media data with the aim of proposing a new indicator of perceived and subjective well-being. This new index, namely SWBI, examines several dimension of individual and social life. The indicator has been compared to some other existing indexes of well-being and health conditions in Italy: the BES (Benessere Equo Sostenibile), the incidence rate of influenza and the abundance of PM10 in urban environments. SWBI is a daily measure available at province level. BES data, currently available only for 2013 and 2014, are annual and available at regional level. Flu data are weekly and distributed as regional data and PM10 are collected daily for different cities. Due to the fact that the time scale and space granularity of the different indexes varies, we apply a novel statistical technique to discover nowcasting features and the classical latent analysis to study the relationships among them. A preliminary analysis suggest that the environmental and health conditions anticipate several dimensions of the perception of well-being as measured by SWBI. Moreover, the set of indicators included in the BES represent a latent dimension of well-being which shares similarities with the latent dimension represented by SWBI., 26 pages, 5 figure

We propose the use of a three-level random intercept model to measure the degree of environmental policy performance of different countries and to study its determinants. Inspired by the literature on multilevel latent models and Item Response Theory (IRT), this framework treats policy commitment as a latent variable which is estimated conditional on the difficulty of the policy portfolio implemented by each country. We contribute to the study and scoring of environmental and energy policies in three main ways. First, the model results in a ranking of countries which is conditional on the complexity of their chosen policy portfolio. Second, we provide a unified framework in which to construct a policy indicator and to study its determinants through a latent regression approach. The resulting country ranking can thus be cleaned from the effect of economic and institutional observables which affect policy design and implementation. Third, the model estimates parameters which can be used to describe and compare policy portfolios across countries. We apply this methodology to the case of energy effciency policies in the industrial sectors of 29 EU countries between 2004 and 2011. We conclude by highlighting the future possible applications of this approach, which are not confined to the realm of environmental and energy policy.

We propose the use of a three-level random intercept model to measure the degree of environmental policy performance of different countries and to study its determinants. Inspired by the literature on multilevel latent models and Item Response Theory (IRT), this framework treats policy commitment as a latent variable which is estimated conditional on the difficulty of the policy portfolio implemented by each country. We contribute to the study and scoring of environmental and energy policies in three main ways. First, the model results in a ranking of countries which is conditional on the complexity of their chosen policy portfolio. Second, we provide a unified framework in which to construct a policy indicator and to study its determinants through a latent regression approach. The resulting country ranking can thus be cleaned from the effect of economic and institutional observables which affect policy design and implementation. Third, the model estimates parameters which can be used to describe and compare policy portfolios across countries. We apply this methodology to the case of energy efficiency policies in the industrial sectors of 29 EU countries between 2004 and 2011. We conclude by highlighting the future possible applications of this approach, which are not confined to the realm of environmental and energy policy.

This paper provides a comparative analysis of statistical methods to evaluate the consumer perception about the quality of Services of General Interest. The evaluation of the service quality perceived by users is usually based on Customer Satisfaction Survey data and an ex-post evaluation is then performed. Another approach, consisting in evaluating Consumers preferences, supplies an ex-ante information on Service Quality. Here, the ex-post approach is considered, two non-standard techniques - the Rasch Model and the Nonlinear Principal Component Analysis - are presented and the potential of both methods is discussed. These methods are applied on the Eurobarometer Survey data to assess the consumer satisfaction among European countries and in different years.