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Publicando durante uma pandemia: comparação do conhecimento científico durante as crises causadas pela covid-19 e pela gripe suína

Publishing during a pandemic: comparison of scientific knowledge between covid-19 and swine flu

Scientific knowledge has a well-established cycle of generating hypotheses, testing them in experiments with proper discussion, and submitting it to the scientific community analysis through publications. It takes time to establish sample size for biomedical studies, especially concerning the effect of medicines and vaccines. The World Health Organization’s protocol estimates that more than 19 months of experiments are necessary to approve a vaccine, for example. As the world has witnessed, a pandemic with immediate impact on human lives urges scientific methods to speed up finding solutions. Here it was assessed the speed and volume of information generated by the Academia to tackle the COVID-19 compared to the Swine Flu pandemic. Were considered papers published in journals indexed in PubMed, the most comprehensive biomedical scientific database available online. The number of publications about COVID-19 was 11 times higher than the number of publications about Swine Flu in a one-year timeframe. Though the expectation were finding more international collaborations and studies focusing on vaccines for COVID-19, papers were mostly concentrated in China and studying symptoms, managing the pandemic, reviewing knowledge, or establishing clinical trials. For sure, science is working faster every day for solutions in biomedical critical situations. However, the fast volume of information might blurry decisions on public health management. This paper’s results show it is mandatory before using papers to take actions, waiting for the scientific community to first progress on its scientific knowledge cycle and mature discussions on the generated knowledge.@en


O conhecimento científico tem um ciclo bem estabelecido de criação de hipóteses, testando-as em experimentos e submetendo-as à análise da comunidade científica por meio de publicações. Leva-se tempo para atingir suficiência amostral em estudos biomédicos, especialmente sobre o efeito de medicamentos e vacinas. O protocolo da Organização Mundial da Saúde estima que sejam necessários mais de 19 meses de experimentos para aprovar uma vacina, por exemplo. Uma pandemia com impacto imediato em vidas humanas exige que estudos científicos acelerem a busca de soluções. No presente trabalho, avaliamos a velocidade e o volume de informações geradas pela Academia para enfrentar a COVID-19 em comparação com a Gripe Suína. Foram considerados artigos de periódicos indexados na plataforma PubMed. O número de publicações sobre a COVID-19 foi 11 vezes maior que o número de publicações sobre a Gripe Suína no período de um ano. Embora esperássemos mais colaborações e estudos internacionais com foco em vacinas para a COVID-19, os artigos se concentraram na China e no estudo de sintomas, gerenciamento da pandemia, revisões do conhecimento ou em ensaios clínicos. Com certeza, a Ciência está trabalhando mais rápido para soluções em situações biomédicas críticas. No entanto, o grande volume de informações gerado em pouco tempo pode dificultar a tomada de decisões em diversas áreas, incluindo na gestão da saúde. Os resultados deste artigo mostram que antes de usar artigos para realizar ações, os tomadores de decisão devem filtrar as informações recebidas e aguardar que a comunidade científica amadureça as discussões sobre o conhecimento gerado.@pt

. Publishing during a pandemic: comparison of scientific knowledge between covid-19 and swine flu. Em questão, [????].

References

  • AKIN, Levent., GÖZEL, Mustafa Gökhan. Understanding dynamics of pandemics. Turkish Journal of Medical Science, Istanbul, v. 50, n. SI-1, p. 515-519, 2020.
  • ARONSON, Elliot. Back to the future: Retrospective review of Leon Festinger´s “A Theory of Cognitive Dissonance”. The American Journal of Psychology, Illinois, v. 110, n. 1, p. 127-137, 1997.
  • BAUM, Robert., WALLY, Stefan. Strategic decision speed and firm performance. Strategic management journal, Maryland, v. 24, n. 11, p. 11071129, 2003.
  • BOULWARE, David et al. A randomized trial of hydroxychloroquine as postexposure prophylaxis for Covid-19. The New England Journal of Medicine, Massachusetts, v. 383, p. 517-525, 2020.
  • COSTELLO, Mark. Motivating online publication of data. BioScience, Oxford, v. 59, n. 5, p. 418-427, 2009.
  • DANE, Francis. Research Methods. Pacific Grove: Brooks Cole, 1990.
  • DI MASCIO, Daniele et al. Risk factors associated with adverse fetal outcomes in pregnancies affected by Coronavirus disease 2019 (COVID-19): A secondary analysis of the WAPM study on COVID-19. Journal of Perinatal Medicine, Berlin, v. 48, n. 9, p. 950-958, 2020.
  • DUTRA, Frederico Giffoni de Carvalho., BARBOSA, Ricardo Rodrigues. Modelos e etapas para a gestão da informação: uma revisão sistemática de literatura. Em Questão, Porto Alegre, v. 26, n. 2, p. 106-131, 2020.
  • EISEN, Michel et al. Peer Review: Publishing in the time of COVID-19. Elife, Cambridge, v. 9, p. e57162.
  • FEINERER, Ingo., HORNIK, Kurt. Text Mining Package. 2019. Available at: http://tm.r-forge.r-project.org. Access in: 10 Jan. 2021.
  • FELLOWS, Ian. Word Clouds. 2018. Available at: http://blog.fellstat.com/?cat=11. Access in: 10 Jan. 2021.
  • FERNER, Robin., ARONSON, Jeffrey. Chloroquine and hydroxychloroquine in covid-19. BMJ, London, v. 369, p. m1432, 2020.
  • HACKING, Ian. Representing and Intervening. Cambridge: University Press, 1983.
  • WICKHAM, Hadley. httr: Tools for Working with URLs and HTTP. 2019a. Available at: https://github.com/r-lib/httr. Access in: 10 Jan. 2021.
  • WICKHAM, Hadley. Easily Harvest (Scrape) Web Pages. 2019b. Available at: https://github.com/tidyverse/rvest. Access in: 10 Jan. 2021.
  • WICKHAM, Hadley. xml2: Parse XML. 2020. Available at: https://github.com/r-lib/xml2. Access in 10 Jan. 2021.
  • HOBDAY, Alistair., BROWMAN, Howard., BOGRAD, Steven. Publishing and peer reviewing as indicators of the impact of COVID-19 on the productivity of the aquatic science community. ICES Journal of Marine Science, Storebø, v. 77., n. 7-8, p. 2439-2444, 2020.
  • L´VOV, Dmitry et al. A possible association of fatal pneumonia with mutations of pandemic influenza A/H1N1 sw1 virus in the receptor-binding site of the
  • HA1 subunit. Voprosy virusologii, Moscow, v. 55, n. 4, p. 4-9, 2010.
  • LIU, Jia et al. Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro. Cell Discovery, New York, v. 6, p. 16, 2020b.
  • LIU, Ying et al. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, Oxford, v. 27, n. 2, p. taaa021, 2020a.
  • LOEFFLER-WIRTH, Henry., SCHMIDT, Maria., BINDER, Hans. Covid-19 Transmission Trajectories: Monitoring the Pandemic in the Worldwide Context. Viruses, New York, v. 12, n. 7, p. 777, 2020.
  • MAZZUCATO, Mariana. 2014. O estado empreendedor: desmascarando o mito do setor público vs. Privado. São Paulo: Portfólio-Penguin.
  • MCKIBBIN, Warwick., FERNANDO, Roshen. The economic impact of COVID-19. Economics in the Time of COVID-19, Sydney, v. 45, p. 45-116,
  • MEHRA, Mandeep et al. Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis. The Lancet, London, v. 396, n. 10245, p. 18-24, 2020.
  • NORGAARD, Ole., LAZARUS, Jeffrey. Searching PubMed during a pandemic. PloS One, Berkeley, v. 5, n. 4, p. e10039, 2010.
  • PALAYEW, Adam et al. Pandemic publishing poses a new COVID-19 challenge. Nature Human Behaviour, New York, v. 4, n. 7, p. 666-669, 2020.
  • PUBMED. PubMed Overview. 2020. Available at: https://pubmed.ncbi.nlm.nih.gov/about. Access in: 5 Jan. 2021.
  • R CORE DEVELOPMENT TEAM. R: A Language and Environment for Statistical Computing 4.0.2. Vienna: R Foundation for Statistical Computing,
  • RODRIGUES, Rosangela Schwarz., NEUBERT, Patricia da Silva., DE ARAÚJO, Breno Kricheldorf Hermes. The publications of Brazilian authors: access, distribution and publishers. Em Questão, Porto Alegre, v. 26, n. 2, p. 13-31, 2020.
  • ROWLEY, Jennifer. Knowledge management in pursuit of learning: the learning with knowledge cycle. Journal of Information Science, London, v. 27, n. 4, p. 227-237, 2001.
  • SAUNDERS-HASTINGS, Patrick., KREWSKI, Daniel. Reviewing the history of pandemic influenza: understanding patterns of emergence and transmission. Pathogens, Warwick, v. 5, n. 4, p. 66, 2016.
  • SINGH, Akriti et al. COVID-19: From bench to bed side. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, Delhi, v. 14, p. 277-81,
  • SMITH, C. A. P.., HAYNE, Stephen C. Decision making under time pressure: an investigation of decision speed and decision quality of computer-supported groups. Management Communication Quarterly, Thousand Oaks, v. 11, n. 1, p. 97-126, 1997.
  • SMITH, Richard., KEOGH-BROWN, Marcus., BARNETT, Tony. Estimating the economic impact of pandemic influenza: an application of the computable general equilibrium model to the UK. Social science and medicine, Boston, v. 73, n. 2, p. 235-244, 2011.
  • TRIFONOV, Vladimir., KHIABANIAN, Hossein., RABADAN, Raul. Geographic dependence, surveillance, and origins of the 2009 influenza A (H1N1) virus. The New England Journal of Medicine, Massachusetts, v. 361, n. 2, p. 115-119, 2009.
  • WHO, World Health Organization. Procedure for assessing the acceptability, in principle, of vaccines for purchase by United Nations agencies. Geneva: WHO Technical Report Series. 2010a
  • WHO, World Health Organization. Pandemic (H1N1) 2009. 2010b. Available at: https://apps.who.int/iris/handle/10665/205605. Access in: 24 June. 2021.
  • WHO, World Health Organization. Naming the coronavirus disease (COVID19) and the virus that causes it. 2020. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technicalguidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-thatcauses-it. Access in: 15 Jan. 2021.
  • WORLD ECONOMIC FORUM. Largest global economies. 2020. Available at: https://www.weforum.org/agenda/2020/07/largest-global-economies-19922008-2024. Access in: 15 Jan. 2021.
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