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      Reasons for Taking the COVID-19 Vaccine by US Social Media Users

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          Abstract

          Political and public health leaders promoting COVID-19 vaccination should identify the most relevant criteria driving the vaccination decision. Social media is increasingly used as a source of vaccination data and as a powerful communication tool to increase vaccination. In December 2020, we performed a cross-sectional social media-based survey addressing personal sentiments toward COVID-19 vaccination in the USA. Our primary research objective is to identify socio-demographic characteristics and the reasons for the 1644 survey participants’ attitudes regarding vaccination. We present clear evidence that, contrary to the prevailing public perceptions, young audiences using social media have mostly a positive attitude towards COVID-19 vaccination (81.5%). These younger individuals want to protect their families and their relatives (96.7%); they see vaccination as an act of civic responsibility (91.9%) and express strong confidence in their healthcare providers (87.7%). Another critical factor is the younger population’s fear of personal COVID-19 infection (88.2%); moreover, the greater the number of children the participants have, the greater is their intent to get the COVID-19 vaccine. These results enable a practical public-messaging pathway to reinforce vaccination campaigns addressing the younger population.

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

            Much of biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
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              Is Open Access

              Vaccine hesitancy: Definition, scope and determinants.

              The SAGE Working Group on Vaccine Hesitancy concluded that vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence. The Working Group retained the term 'vaccine' rather than 'vaccination' hesitancy, although the latter more correctly implies the broader range of immunization concerns, as vaccine hesitancy is the more commonly used term. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand. The Vaccine Hesitancy Determinants Matrix displays the factors influencing the behavioral decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine/vaccination-specific influences.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Vaccines (Basel)
                Vaccines (Basel)
                vaccines
                Vaccines
                MDPI
                2076-393X
                29 March 2021
                April 2021
                : 9
                : 4
                : 315
                Affiliations
                [1 ]Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon 5810201, Israel
                [2 ]Faculty of Digital Technologies in Medicine, Holon Institute of Technology, Holon 5810201, Israel
                [3 ]Department of Information Systems, Questrom Business School, Boston University, Boston, MA 02215, USA; avis@ 123456bu.edu
                [4 ]Health Analytics and Digital Health, Digital Business Institute, Boston University, Boston, MA 02215, USA
                [5 ]School of Medicine, Ariel University, Ariel 40700, Israel; shaias@ 123456ariel.ac.il
                Author notes
                [* ]Correspondence: arrielb@ 123456hit.ac.il
                Author information
                https://orcid.org/0000-0002-9125-8300
                Article
                vaccines-09-00315
                10.3390/vaccines9040315
                8067223
                33805283
                8ae9e47c-ee35-496a-9c3a-13d1bc2dee52
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 March 2021
                : 24 March 2021
                Categories
                Article

                coronavirus,sars-cov-2,social media,online social networking,social factors,immunization programs,vaccination,vaccination refusal,vaccination coverage,vaccination hesitancy,health communication,health policy

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