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      Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic

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          Abstract

          Background

          With the COVID-19 pandemic’s outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an “infodemic,” ensuring the quality of information is a crucial vector of public health. Investigating whether and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked which sources informed Wikipedia’s COVID-19–related articles before and during the pandemic’s first wave (January–May 2020).

          Results

          We found that coronavirus-related articles referenced trusted media outlets and high-quality academic sources. Regarding academic sources, Wikipedia was found to be highly selective in terms of what science was cited. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English-language COVID-19 articles from 2001–2020 revealed a nuanced trade-off between quality and timeliness. It further showed how pre-existing articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this “scientific infrastructure” facilitated contextualization and regulated the influx of new information. Last, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 drug development.

          Conclusions

          Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its “citizen encyclopedists,” it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts.

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          Most cited references52

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          A pneumonia outbreak associated with a new coronavirus of probable bat origin

          Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats 1–4 . Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans 5–7 . Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of SARSr-CoV. In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV.
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            Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

            The newly emergent human virus SARS-CoV-2 is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact-tracing needed to stop the epidemic. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.
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              Clinical benefit of remdesivir in rhesus macaques infected with SARS-CoV-2

              Summary Effective therapeutics to treat COVID-19 are urgently needed. While many investigational, approved, and repurposed drugs have been suggested, preclinical data from animal models can guide the search for effective treatments by ruling out treatments without in vivo efficacy. Remdesivir (GS-5734) is a nucleotide analog prodrug with broad antiviral activity 1,2 , that is currently investigated in COVID-19 clinical trials and recently received Emergency Use Authorization from the US Food and Drug Administration 3,4 . In animal models, remdesivir treatment was effective against MERS-CoV and SARS-CoV infection. 2,5,6 In vitro, remdesivir inhibited replication of SARS-CoV-2. 7,8 Here, we investigated the efficacy of remdesivir treatment in arhesus macaque model of SARS-CoV-2 infection 9 . In contrast to vehicle-treated animals, animals treated with remdesivir did not show signs of respiratory disease and had reduced pulmonary infiltrates on radiographs and reduced virus titers in bronchoalveolar lavages 12hrs after the first treatment administration. Virus shedding from the upper respiratory tract was not reduced by remdesivir treatment. At necropsy, lung viral loads of remdesivir-treated animals were lower and there was a reduction in damage to the lungs. Thus, therapeutic remdesivir treatment initiated early during infection had a clinical benefit in SARS-CoV-2-infected rhesus macaques. Although the rhesus macaque model does not represent the severe disease observed in a proportion of COVID-19 patients, our data support early remdesivir treatment initiation in COVID-19 patients to prevent progression to pneumonia.
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                Author and article information

                Contributors
                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                12 January 2022
                January 2022
                12 January 2022
                : 11
                : 1
                : giab095
                Affiliations
                Center for Research and Interdisciplinarity (CRI), Université de Paris , INSERM U1284, 8 bis Rue Charles V, 75004 Paris, France
                The Cohn Institute for the History and Philosophy of Science and Ideas, Humanities Faculty, Tel Aviv University, Ramat Aviv , Tel Aviv 6997801, Israel
                Center for Research and Interdisciplinarity (CRI), Université de Paris , INSERM U1284, 8 bis Rue Charles V, 75004 Paris, France
                Department of Biomolecular Sciences, Weizmann Institute of Science , Rehovot 76100, Israel
                Department of Biomolecular Sciences, Weizmann Institute of Science , Rehovot 76100, Israel
                Department of Biomedical Engineering , Julius Silver Building, Technion-IIT, Technion City, Haifa 32000, Israel
                Author notes
                Correspondence address. Jonathan Aryeh Sobel. Department of Biomedical Engineering, Julius Silver Building, Technion-IIT, Technion City, Haifa 32000 Israel. E-mail: jsobel@ 123456campus.technion.ac.il

                Omer Benjakob. Sderot Yerushalaim 98 apartment 3 Tel Aviv, Tel Aviv 6809033, Israel.

                Contributed equally.

                Author information
                https://orcid.org/0000-0002-7179-3509
                https://orcid.org/0000-0001-5544-3552
                https://orcid.org/0000-0002-5111-4070
                Article
                giab095
                10.1093/gigascience/giab095
                8756189
                35022700
                90405b8a-53c8-483d-a2a4-72b5c8cf5b50
                © The Author(s) 2022. Published by Oxford University Press GigaScience.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 May 2021
                : 15 September 2021
                : 10 December 2021
                Page count
                Pages: 13
                Funding
                Funded by: Azrieli Foundation, DOI 10.13039/501100005155;
                Categories
                Research
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI02254

                covid-19,wikipedia,infodemic,sources,bibliometrics,citizen science,open science

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