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  1. Article ; Online: Behavioral Changes Associated With COVID-19 Vaccination: Cross-National Online Survey.

    De Gaetano, Alessandro / Bajardi, Paolo / Gozzi, Nicolò / Perra, Nicola / Perrotta, Daniela / Paolotti, Daniela

    Journal of medical Internet research

    2023  Volume 25, Page(s) e47563

    Abstract: Background: During the initial phases of the vaccination campaign worldwide, nonpharmaceutical interventions (NPIs) remained pivotal in the fight against the COVID-19 pandemic. In this context, it is important to understand how the arrival of vaccines ... ...

    Abstract Background: During the initial phases of the vaccination campaign worldwide, nonpharmaceutical interventions (NPIs) remained pivotal in the fight against the COVID-19 pandemic. In this context, it is important to understand how the arrival of vaccines affected the adoption of NPIs. Indeed, some individuals might have seen the start of mass vaccination campaigns as the end of the emergency and, as a result, relaxed their COVID-safe behaviors, facilitating the spread of the virus in a delicate epidemic phase such as the initial rollout.
    Objective: The aim of this study was to collect information about the possible relaxation of protective behaviors following key events of the vaccination campaign in four countries and to analyze possible associations of these behavioral tendencies with the sociodemographic characteristics of participants.
    Methods: We developed an online survey named "COVID-19 Prevention and Behavior Survey" that was conducted between November 26 and December 22, 2021. Participants were recruited using targeted ads on Facebook in four different countries: Brazil, Italy, South Africa, and the United Kingdom. We measured the onset of relaxation of protective measures in response to key events of the vaccination campaign, namely personal vaccination and vaccination of the most vulnerable population. Through calculation of odds ratios (ORs) and regression analysis, we assessed the strength of association between compliance with NPIs and sociodemographic characteristics of participants.
    Results: We received 2263 questionnaires from the four countries. Participants reported the most significant changes in social activities such as going to a restaurant or the cinema and visiting relatives and friends. This is in good agreement with validated psychological models of health-related behavioral change such as the Health Belief Model, according to which activities with higher costs and perceived barriers (eg, social activities) are more prone to early relaxation. Multivariate analysis using a generalized linear model showed that the two main determinants of the drop of social NPIs were (1) having previously tested positive for COVID-19 (after the second vaccine dose: OR 2.46, 95% CI 1.73-3.49) and (2) living with people at risk (after the second vaccine dose: OR 1.57, 95% CI 1.22-2.03).
    Conclusions: This work shows that particular caution has to be taken during vaccination campaigns. Indeed, people might relax their safe behaviors regardless of the dynamics of the epidemic. For this reason, it is crucial to maintain high compliance with NPIs to avoid hindering the beneficial effects of the vaccine.
    MeSH term(s) Humans ; COVID-19 Vaccines/therapeutic use ; Pandemics/prevention & control ; COVID-19/epidemiology ; COVID-19/prevention & control ; Vaccination ; Social Behavior
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2023-10-31
    Publishing country Canada
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/47563
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book: La internacionalización de la universidad

    Perrotta, Daniela

    debates globales, acciones regionales

    (Colección educación)

    2016  

    Author's details Daniela Perrotta
    Series title Colección educación
    Keywords Education and globalization ; Education, Higher/International cooperation ; Regionalism and education
    Language Spanish
    Size 112 pages, illustrations, 21 cm
    Publisher IEC, Instituto de Estudios y Capacitación, Federación Nacional de Docentes Universitarios$n ; Ediciones UNGS, Universidad Nacional de General Sarmiento
    Publishing place Ciudad Autónoma de Buenos Aires ; Los Polvorines, Prov. de Buenos Aires, Argentina
    Document type Book
    Note Includes bibliographical references (pages 103-110)
    ISBN 9789876302302 ; 9876302302
    Database ECONomics Information System

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  3. Article ; Online: Towards a data-driven characterization of behavioral changes induced by the seasonal flu.

    Gozzi, Nicolò / Perrotta, Daniela / Paolotti, Daniela / Perra, Nicola

    PLoS computational biology

    2020  Volume 16, Issue 5, Page(s) e1007879

    Abstract: In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the ... ...

    Abstract In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.
    MeSH term(s) Algorithms ; Disease Susceptibility ; Female ; Humans ; Influenza, Human/psychology ; Italy ; Male ; Seasons ; Surveys and Questionnaires
    Keywords covid19
    Language English
    Publishing date 2020-05-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1007879
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Parents' Trigger Tool for Children with Medical Complexity - PAT-CMC: Development of a recognition tool for clinical deterioration at home.

    Genna, Catia / Thekkan, Kiara Ros / Geremia, Caterina / Di Furia, Michela / Cecchetti, Corrado / Rufini, Emilia / Salata, Michele / Perrotta, Daniela / Dall'Oglio, Immacolata / Tiozzo, Emanuela / Raponi, Massimiliano / Gawronski, Orsola

    Journal of advanced nursing

    2024  

    Abstract: Aim: To develop a trigger tool for parents and lay caregivers of children with medical complexity (CMC) at home and to validate its content.: Design: This was a multi-method study, using qualitative data, a Delphi method and a concept mapping ... ...

    Abstract Aim: To develop a trigger tool for parents and lay caregivers of children with medical complexity (CMC) at home and to validate its content.
    Design: This was a multi-method study, using qualitative data, a Delphi method and a concept mapping approach.
    Methods: A three-round electronic Delphi was performed from December 2021 to April 2022 with a panel of 23 expert parents and 30 healthcare providers, supplemented by a preliminary qualitative exploration of children's signs of deterioration and three consensus meetings to develop the PArents' Trigger Tool for Children with Medical Complexity (PAT-CMC). Cognitive interviews with parents were performed to assess the comprehensiveness and comprehensibility of the tool. The COREQ checklist, the COSMIN guidelines and the CREDES guidelines guided the reporting respectively of the qualitative study, the development and content validity of the trigger tool and the Delphi study.
    Results: The PAT-CMC was developed and its content validated to recognize clinical deterioration at home. The tool consists of 7 main clusters of items: Breathing, Heart, Devices, Behaviour, Neuro-Muscular, Nutrition/Hydration and Other Concerns. A total of 23 triggers of deterioration were included and related to two recommendations for escalation of care, using a traffic light coding system.
    Conclusion: Priority indicators of clinical deterioration of CMC were identified and integrated into a validated trigger tool designed for parents or other lay caregivers at home, to recognize signs of acute severe illness and initiate healthcare interventions.
    Impact: The PAT-CMC was developed to guide families in recognizing signs of deterioration in CMC and has potential for initiating an early escalation of care. This tool may also be useful to support education provided by healthcare providers to families before hospital discharge.
    Patient or public contribution: Parents of CMC were directly involved in the selection of relevant indicators of children's clinical deterioration and the development of the trigger tool. They were not involved in the design, conducting, reporting or dissemination plans of this research.
    Language English
    Publishing date 2024-04-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 197634-5
    ISSN 1365-2648 ; 0309-2402
    ISSN (online) 1365-2648
    ISSN 0309-2402
    DOI 10.1111/jan.16201
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia.

    Perrotta, Daniela / Frias-Martinez, Enrique / Pastore Y Piontti, Ana / Zhang, Qian / Luengo-Oroz, Miguel / Paolotti, Daniela / Tizzoni, Michele / Vespignani, Alessandro

    PLoS neglected tropical diseases

    2022  Volume 16, Issue 7, Page(s) e0010565

    Abstract: Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), ... ...

    Abstract Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson's r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.
    MeSH term(s) Animals ; Colombia/epidemiology ; Epidemics ; Humans ; Mosquito Vectors ; Zika Virus ; Zika Virus Infection/epidemiology
    Language English
    Publishing date 2022-07-20
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2429704-5
    ISSN 1935-2735 ; 1935-2735
    ISSN (online) 1935-2735
    ISSN 1935-2735
    DOI 10.1371/journal.pntd.0010565
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Towards a data-driven characterization of behavioral changes induced by the seasonal flu

    Gozzi, Nicolò / Perrotta, Daniela / Paolotti, Daniela / Perra, Nicola

    PLoS Comput Biol

    Abstract: In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the ... ...

    Abstract In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #638069
    Database COVID19

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  7. Book ; Online: Towards a data-driven characterization of behavioral changes induced by the seasonal flu

    Gozzi, Nicolò / Perrotta, Daniela / Paolotti, Daniela / Perra, Nicola

    2020  

    Abstract: In this work, we aim to determine the main factors driving behavioral change during the seasonal flu. To this end, we analyze a unique dataset comprised of 599 surveys completed by 434 Italian users of Influweb, a Web platform for participatory ... ...

    Abstract In this work, we aim to determine the main factors driving behavioral change during the seasonal flu. To this end, we analyze a unique dataset comprised of 599 surveys completed by 434 Italian users of Influweb, a Web platform for participatory surveillance, during the 2017-18 and 2018-19 seasons. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26 %), ii) only moderately (36 %), iii) significant (38 %) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, $66\%$ of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task. Interestingly, the last two match the theoretical constructs suggested by the Health-Belief Model. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.
    Keywords Physics - Physics and Society ; Computer Science - Social and Information Networks
    Subject code 300
    Publishing date 2020-02-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Towards a data-driven characterization of behavioral changes induced by the seasonal flu

    Gozzi, Nicolò / Perrotta, Daniela / Paolotti, Daniela / Perra, Nicola

    PLoS Computational Biology, 16(5):e1007879

    2020  

    Abstract: In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the ... ...

    Abstract In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases. AUTHOR SUMMARY: Human behavior and infectious diseases are linked by a feedback loop. While individuals might change their behavior as a response to an epidemic, such changes might influence the spreading itself. So far, our understanding and characterization of behavioral changes induced by diseases has been strongly limited by the lack of empirical data. As result, the vast majority of research has been focused on theoretical, what if, scenarios. In this work, we collected a unique dataset comprised of 599 surveys submitted by 434 users to the participatory surveillance platform Influweb over the 2017 − 18 and 2018 − 19 flu seasons. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of self-initiated behavioral changes implemented by each participant. Our analysis, conducted adopting machine learning algorithms, show that both past experience of illness and personal beliefs about the disease are fundamental drivers of behavioral change. These findings are in good agreement with the constructs of the Health Belief Model and provide, to the best of our knowledge, the first data driven characterization of behavioral changes during the seasonal flu.
    Keywords COVID-19 ; Behaviour ; Machine learning ; Influenza ; Behavioral and social aspects of health ; Social distancing ; Machine learning algorithms ; Pandemics ; covid19
    Language English
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy.

    Perrotta, Daniela / Bella, Antonino / Rizzo, Caterina / Paolotti, Daniela

    PloS one

    2017  Volume 12, Issue 1, Page(s) e0169801

    Abstract: The monitoring of seasonal influenza yearly epidemics remains one of the main activity of national syndromic surveillance systems. The development of internet-based surveillance tools has brought an innovative approach to seasonal influenza surveillance ... ...

    Abstract The monitoring of seasonal influenza yearly epidemics remains one of the main activity of national syndromic surveillance systems. The development of internet-based surveillance tools has brought an innovative approach to seasonal influenza surveillance by directly involving self-selected volunteers among the general population reporting their health status on a weekly basis throughout the flu season. In this paper, we explore how Influweb, an internet-based monitoring system for influenza surveillance, deployed in Italy since 2008 has performed during three years from 2012 to 2015 in comparison with data collected during the same period by the Italian sentinel doctors surveillance system.
    MeSH term(s) Adult ; Female ; Humans ; Influenza, Human/epidemiology ; Internet ; Italy ; Male ; Middle Aged ; Sentinel Surveillance ; Telemedicine/methods
    Language English
    Publishing date 2017
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0169801
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Differential impact of physical distancing strategies on social contacts relevant for the spread of SARS-CoV-2: evidence from a cross-national online survey, March-April 2020.

    Del Fava, Emanuele / Cimentada, Jorge / Perrotta, Daniela / Grow, André / Rampazzo, Francesco / Gil-Clavel, Sofia / Zagheni, Emilio

    BMJ open

    2021  Volume 11, Issue 10, Page(s) e050651

    Abstract: Objectives: We investigate changes in social contact patterns following the gradual introduction of non-pharmaceutical interventions and their implications for infection transmission in the early phase of the pandemic.: Design, setting and ... ...

    Abstract Objectives: We investigate changes in social contact patterns following the gradual introduction of non-pharmaceutical interventions and their implications for infection transmission in the early phase of the pandemic.
    Design, setting and participants: We conducted an online survey based on targeted Facebook advertising campaigns across eight countries (Belgium, France, Germany, Italy, the Netherlands, Spain, UK and USA), achieving a sample of 51 233 questionnaires in the period 13 March-12 April 2020. Poststratification weights based on census information were produced to correct for selection bias.
    Outcome measures: Participants provided data on social contact numbers, adoption of protective behaviours and perceived level of threat. These data were combined to derive a weekly index of infection transmission, the net reproduction number [Formula: see text] .
    Results: Evidence from the USA and UK showed that the number of daily contacts mainly decreased after governments issued the first physical distancing guidelines. In mid-April, daily social contact numbers had decreased between 61% in Germany and 87% in Italy with respect to pre-COVID-19 levels, mostly due to a contraction in contacts outside the home. Such reductions, which were uniform across age groups, were compatible with an [Formula: see text] equal or smaller than one in all countries, except Germany. This indicates lower levels of infection transmission, especially in a period of gradual increase in the adoption rate of the face mask outside the home.
    Conclusions: We provided a comparable set of statistics on social contact patterns during the COVID-19 pandemic for eight high-income countries, disaggregated by week and other demographic factors, which could be leveraged by the scientific community for developing more realistic epidemic models of COVID-19.
    MeSH term(s) COVID-19 ; Humans ; Masks ; Pandemics/prevention & control ; Physical Distancing ; SARS-CoV-2
    Language English
    Publishing date 2021-10-21
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2599832-8
    ISSN 2044-6055 ; 2044-6055
    ISSN (online) 2044-6055
    ISSN 2044-6055
    DOI 10.1136/bmjopen-2021-050651
    Database MEDical Literature Analysis and Retrieval System OnLINE

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