1
|
Yin X, Aiken JM, Harris R, Bamber JL. A Bayesian spatio-temporal model of COVID-19 spread in England. Sci Rep 2024; 14:10335. [PMID: 38710934 DOI: 10.1038/s41598-024-60964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.
Collapse
Affiliation(s)
- Xueqing Yin
- School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK.
| | - John M Aiken
- Expert Analytics, 0179, Oslo, Norway
- Njord Centre, Departments of Physics and Geosciences, University of Oslo, 0371, Oslo, Norway
| | - Richard Harris
- School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
| | - Jonathan L Bamber
- School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
- Department of Aerospace and Geodesy, Technical University of Munich, 80333, Munich, Germany
| |
Collapse
|
2
|
Norton A, Rakowska S, Galloway T, Wilson K, Rosella L, Adams M. Are at-risk sociodemographic attributes stable across COVID-19 transmission waves? Spat Spatiotemporal Epidemiol 2023; 45:100586. [PMID: 37301601 DOI: 10.1016/j.sste.2023.100586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/03/2023] [Accepted: 04/07/2023] [Indexed: 06/12/2023]
Abstract
COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.
Collapse
Affiliation(s)
- Amanda Norton
- Department of Geography, Geomatics & Environment, University of Toronto Mississauga, DV3284, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
| | - Scarlett Rakowska
- Department of Geography, Geomatics & Environment, University of Toronto Mississauga, DV3284, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
| | - Tracey Galloway
- Department of Anthropology, University of Toronto Mississauga, HSC354, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
| | - Kathleen Wilson
- Department of Geography, Geomatics & Environment, University of Toronto Mississauga, DV3284, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
| | - Laura Rosella
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, 155 College St, Health Sciences Bldg., 6th floor, Toronto, ON M5T 3M7, Canada
| | - Matthew Adams
- Department of Geography, Geomatics & Environment, University of Toronto Mississauga, DV3284, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada.
| |
Collapse
|
3
|
De Witte D, Abad AA, Molenberghs G, Verbeke G, Sanchez L, Mas-Bermejo P, Neyens T. A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba. Spat Spatiotemporal Epidemiol 2023; 45:100588. [PMID: 37301587 DOI: 10.1016/j.sste.2023.100588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 06/12/2023]
Abstract
To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.
Collapse
Affiliation(s)
| | - Ariel Alonso Abad
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Geert Molenberghs
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Geert Verbeke
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| | - Lizet Sanchez
- Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Center of Molecular Immunology, Havana, 11 600, Cuba
| | - Pedro Mas-Bermejo
- Cuban National Group of Epidemiology and Modeling of the COVID-19 Pandemic, Institute "Pedro Kouri", Havana, 11 600, Cuba
| | - Thomas Neyens
- L-BioStat, KU Leuven, Leuven, 3000, Belgium; I-BioStat, Hasselt University, Diepenbeek, 3590, Belgium
| |
Collapse
|
4
|
MacNab YC. Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting. SPATIAL STATISTICS 2023; 53:100726. [PMID: 36713268 PMCID: PMC9859649 DOI: 10.1016/j.spasta.2023.100726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/02/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Recent disease mapping literature presents adaptively parameterized spatiotemporal (ST) autoregressive (AR) or conditional autoregressive (CAR) models for Bayesian prediction of COVID-19 infection risks. These models were motivated to capture complex spatiotemporal dynamics and heterogeneities of infection risks. In the present paper, we synthesize, generalize, and unify the ST AR and CAR model constructions for models augmented by adaptive Gaussian Markov random fields, with an emphasis on disease forecasting. A general convolution construction is presented, with illustrative models motivated to (i) characterize local risk dependencies and influences over both spatial and temporal dimensions, (ii) model risk heterogeneities and discontinuities, and (iii) predict and forecast areal-level disease risks and occurrences. The broadened constructions allow rich options of intuitive parameterization for disease mapping and spatial regression. Illustrative parameterizations are presented for Bayesian hierarchical models of Poisson, zero-inflated Poisson, and Bernoulli data models, respectively. They are also discussed in the context of quantifying time-varying or time-invariant effects of (omitted) covariates, with application to prediction and forecasting areal-level COVID-19 infection occurrences and probabilities of zero-infection. The model constructions presented herein have much wider scope in offering a flexible framework for modelling complex spatiotemporal data and for estimation, learning, and forecasting purposes.
Collapse
Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| |
Collapse
|
5
|
Briz-Redón Á, Iftimi A, Mateu J, Romero-García C. A mechanistic spatio-temporal modeling of COVID-19 data. Biom J 2023; 65:e2100318. [PMID: 35934898 DOI: 10.1002/bimj.202100318] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/26/2022] [Accepted: 04/02/2022] [Indexed: 01/17/2023]
Abstract
Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.
Collapse
Affiliation(s)
- Álvaro Briz-Redón
- Department of Statistics and Operations Research, University of Valencia, Spain.,Statistics Office, City Council of Valencia, Spain
| | - Adina Iftimi
- Department of Statistics and Operations Research, University of Valencia, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Spain
| | - Carolina Romero-García
- Department of Anesthesia, Critical Care and Pain Unit, General University Hospital, Spain.,Division of Research Methodology, European University of Valencia, Spain
| |
Collapse
|
6
|
Owusu G, Yu H, Huang H. Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories. AIMS Public Health 2022; 9:703-717. [PMID: 36636154 PMCID: PMC9807409 DOI: 10.3934/publichealth.2022049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/17/2022] [Accepted: 09/01/2022] [Indexed: 12/15/2022] Open
Abstract
The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.
Collapse
Affiliation(s)
- Gabriel Owusu
- Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO 80639, USA
| | - Han Yu
- Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO 80639, USA,* Correspondence:
| | - Hong Huang
- School of Information, University of South Florida, Tampa, FL, 33620, USA
| |
Collapse
|
7
|
MacNab YC. Bayesian disease mapping: Past, present, and future. SPATIAL STATISTICS 2022; 50:100593. [PMID: 35075407 PMCID: PMC8769562 DOI: 10.1016/j.spasta.2022.100593] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.
Collapse
Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| |
Collapse
|
8
|
Bucci A, Ippoliti L, Valentini P, Fontanella S. Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe. SPATIAL STATISTICS 2022; 49:100543. [PMID: 34631400 PMCID: PMC8493647 DOI: 10.1016/j.spasta.2021.100543] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 06/13/2023]
Abstract
The impact of the COVID-19 pandemic varied significantly across different countries, with important consequences in the definition of control and response strategies. In this work, to investigate the heterogeneity of this crisis, we analyse the spatial patterns of deaths attributed to COVID-19 in several European countries. To this end, we propose a Bayesian nonparametric approach, based on mixture of Gaussian processes coupled with Dirichlet process, to group the COVID-19 mortality curves. The model provides a flexible framework for the analysis of time series data, allowing the inclusion in the clustering procedure of different features of the series, such as spatial correlations, time varying parameters and measurement errors. We evaluate the proposed methodology on the death counts recorded at NUTS-2 regional level for several European countries in the period from March 2020 to February 2021.
Collapse
Affiliation(s)
- A Bucci
- Department of Economics, University G. d'Annunzio, Chieti-Pescara, Italy
| | - L Ippoliti
- Department of Economics, University G. d'Annunzio, Chieti-Pescara, Italy
| | - P Valentini
- Department of Economics, University G. d'Annunzio, Chieti-Pescara, Italy
| | - S Fontanella
- National Heart and Lung Institute, Imperial College London, UK
| |
Collapse
|
9
|
Cioban S, Mare C. Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative units in Romania. SPATIAL STATISTICS 2022; 49:100558. [PMID: 34909371 PMCID: PMC8662404 DOI: 10.1016/j.spasta.2021.100558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 11/16/2021] [Accepted: 11/25/2021] [Indexed: 05/31/2023]
Abstract
Spatial analyses related to Covid-19 have been so far conducted at county, regional or national level, without a thorough assessment at the continuous local level of administrative-territorial units like cities, towns, or communes. To address this gap, we employ daily data on the infection rate provided for Romanian administrative units from March to May 2021. Using the global and local Moran I spatial autocorrelation coefficients, we identify significant clustering processes in the Covid-19 infection rate. Additional analysis based on spatially smoothed rate maps and spatial regressions prove that this clustering pattern is influenced by the development level of localities, proxied by unemployment rate and Local Human Development Index. Results show the features of the 3rd wave in Romania, characterized by a quadratic trend.
Collapse
Affiliation(s)
- Stefana Cioban
- Babes-Bolyai University, Faculty of Economics and Business Administration, Department of Statistics-Forecasts-Mathematics, 58-60, Teodor Mihali str., 400591, Cluj-Napoca, Romania
- Babeş-Bolyai University, Interdisciplinary Centre for Data Science, 68, Avram Iancu str., 400083, 4th floor, Cluj-Napoca, Romania
| | - Codruta Mare
- Babes-Bolyai University, Faculty of Economics and Business Administration, Department of Statistics-Forecasts-Mathematics, 58-60, Teodor Mihali str., 400591, Cluj-Napoca, Romania
- Babeş-Bolyai University, Interdisciplinary Centre for Data Science, 68, Avram Iancu str., 400083, 4th floor, Cluj-Napoca, Romania
| |
Collapse
|
10
|
Zhang S, Wang M, Yang Z, Zhang B. Do spatiotemporal units matter for exploring the microgeographies of epidemics? APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 142:102692. [PMID: 35399592 PMCID: PMC8982866 DOI: 10.1016/j.apgeog.2022.102692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/04/2022] [Accepted: 03/25/2022] [Indexed: 05/17/2023]
Abstract
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
Collapse
Affiliation(s)
- Sui Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Minghao Wang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Zhao Yang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| |
Collapse
|
11
|
Cerqueti R, Ficcadenti V. Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million. CHAOS, SOLITONS, AND FRACTALS 2022; 158:111975. [PMID: 35291220 PMCID: PMC8913321 DOI: 10.1016/j.chaos.2022.111975] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/31/2022] [Accepted: 03/03/2022] [Indexed: 05/09/2023]
Abstract
This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.
Collapse
Affiliation(s)
- Roy Cerqueti
- Sapienza University of Rome, Department of Social and Economic Sciences, Piazzale Aldo Moro, 5, 00185 Rome, Italy
- London South Bank University, Business School, Borough Road, 103, SE1 0AA London, United Kingdom
- GRANEM, University of Angers, France
| | - Valerio Ficcadenti
- London South Bank University, Business School, Borough Road, 103, SE1 0AA London, United Kingdom
| |
Collapse
|
12
|
D'Urso P, De Giovanni L, Vitale V. A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy. SPATIAL STATISTICS 2022; 47:100586. [PMID: 35036295 PMCID: PMC8744361 DOI: 10.1016/j.spasta.2021.100586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/25/2021] [Accepted: 12/31/2021] [Indexed: 05/12/2023]
Abstract
The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.
Collapse
Affiliation(s)
- Pierpaolo D'Urso
- Department of Social and Economic Sciences, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Livia De Giovanni
- Department of Political Sciences, LUISS University, Viale Romania, 32, 00197 Rome, Italy
| | - Vincenzina Vitale
- Department of Social and Economic Sciences, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| |
Collapse
|