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Enregistrement W3047788174 · doi:10.1016/s2589-7500(20)30197-7

Communicating in a public health crisis

2020· article· en· W3047788174 sur OpenAlexaff
Hui Wang, Paul D. Cleary, Julian Little, Charles Auffray

Notice bibliographique

RevueThe Lancet Digital Health · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueData-Driven Disease Surveillance
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesNational Center for Advancing Translational Sciences
Mots-clésPreparednessScopusBattlePublic healthContext (archaeology)PandemicPublic relationsPolitical scienceGlobal healthMedicineBusinessCoronavirus disease 2019 (COVID-19)GeographyMEDLINENursingLawDisease

Résumé

récupéré en direct d'OpenAlex

Despite previous pandemics and reports on pandemic preparedness,1Global Preparedness Monitoring BoardA world at risk: annual report on global preparedness for health emergencies.https://apps.who.int/gpmb/assets/annual_report/GPMB_Annual_Report_English.pdfDate: 2019Date accessed: July 30, 2020Google Scholar many countries struggle to prevent and manage public health emergencies.2Kandel N Chungong S Omaar A Xing J Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries.Lancet. 2020; 395: 1047-1053Summary Full Text Full Text PDF PubMed Scopus (341) Google Scholar A key component of an effective pandemic response is communication between governments, health professionals, scientists, the media, and the public.3Cowper A Covid-19: are we getting the communications right?.BMJ. 2020; 368: m919Crossref PubMed Scopus (50) Google Scholar A potential concern is how to maintain public trust in science and high levels of support for control measures, such as contact tracing, especially if they potentially challenge personal privacy.4Bricker D Canadians supportive of wide-ranging measures to battle COVID-19, including some surveillance.https://www.ipsos.com/en-ca/news-and-polls/Canadians-Supportive-Of-Wide-Ranging-Measures-To-Battle-COVID19-Including-Some-SurveillanceDate: April 9, 2020Date accessed: July 30, 2020Google Scholar Despite only having a short time to accumulate, the volume of published evidence on COVID-19 is extensive, making it difficult to manage and verify. Development of systematic reviews, supported by artificial intelligence and crowdsourcing, could support the rapid analysis of evidence-based measures to help communicate the need for control measures to mitigate COVID-19.5Piechotta V Chai KL Valk SJ et al.Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19: a living systematic review.Cochrane Database Syst Rev. 2020; 7CD013600PubMed Google Scholar The COVID-19 pandemic has encouraged a new phase of real-time, peer-to-peer sharing. Data concerning diseases and outbreaks are communicated through multiple channels, providing a view of global health that is fundamentally different from that provided by traditional public health organisations. Use of online information is becoming a dominant method for the surveillance of emerging public health threats. For example, a widely used information source on the numbers of global COVID-19 cases and deaths is an interdisciplinary collaboration between several groups at Johns Hopkins University (The Johns Hopkins Coronavirus Resource Center).6Dong E Du H Gardner L An interactive web-based dashboard to track COVID-19 in real time.Lancet Infect Dis. 2020; 20: 533-534Summary Full Text Full Text PDF PubMed Scopus (6657) Google Scholar Similarly, HealthMap concatenates information from disparate data sources, including online news aggregators, eyewitness reports, expert-curated discussions, and validated official reports, to achieve a unified and comprehensive view of current infectious diseases.7Brownstein JS Freifeld CC Madoff LC Digital disease detection—harnessing the Web for public health surveillance.N Engl J Med. 2009; 360: 2153-2157Crossref PubMed Scopus (533) Google Scholar Global communication for future pandemics requires a novel framework. Although formal international agreements and agencies play an important part in communicating information, non-governmental groups might be able to perform a critical function in the global response to emerging diseases, and we encourage expanded use of consortia to take advantage of the strength of diverse electronic information sources and innovative means to compile and communicate information. Poor health media literacy is common, and likewise a paucity of scientific knowledge has undermined responses to the COVID-19 pandemic. We have witnessed the amplification of unverified information, which has triggered misunderstandings, reactions of fear, and a loss of trust, which can inhibit effective responses to the pandemic. In preparation for the possible resurgence of COVID-19 or the occurrence of new infectious diseases, proactive public health investment in mechanisms for compiling, verifying, and communicating information is of paramount importance to ensuring public health. Emphasis should be placed on understanding specific factors, such as how the interplay between infectious agents and humans facilitates transmission through travelling and social activities in confined environments. During periods of uncertainty, strategies for communicating evolving information need to be developed and assessed. New curricula in systems medicine and effective communication strategies that examine the factors affecting preventive behaviour should be developed and used to train health-care professionals, researchers, teachers, media professionals, and decision makers with active involvement in communicating with the general public. We declare no competing interests. We thank the G20 Riyadh Global Digital Health Summit hosted in Saudi Arabia to leverage the role of digital health in the fight against current and future pandemics. Applications of predictive modelling early in the COVID-19 epidemicOn Jan 30, 2020, WHO declared a Public Health Emergency of International Concern, a month after COVID-19 was identified in Wuhan, China. By this point, several mathematical and computational models had already raised the alarm about the potential for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to cause a global pandemic and the dire consequences for public health should drastic action not be taken. During the emergence of a novel pandemic, predictive modelling is important in public health planning and response. Full-Text PDF Open AccessGlobal health and data-driven policies for emergency responses to infectious disease outbreaksIn 2011, WHO reached a global health milestone when the organisation achieved international agreement on a framework for pandemic influenza preparedness that would facilitate the sharing of influenza virus samples and data, allow vaccine access, and address aspects relevant to low-income and middle-income countries (LMICs).1 Similarly, in 2015, a WHO consultation during the Ebola virus outbreak in west Africa emphasised the need for global norms and for the public availability of data during public health emergencies. Full-Text PDF Open AccessOpportunities and challenges for telehealth within, and beyond, a pandemicThe COVID-19 pandemic is unlike any previous pandemic. The ubiquity of international travel, the ease of transmission of the virus, and symptom variability have resulted in an unprecedented rate of spread. The need for physical distancing has also led to the rapid adoption of telehealth solutions globally. Full-Text PDF Open AccessDigital public health and COVID-19Digital public health refers to the use of technology, new types of data, and new ways of working that come with digitisation of public health and associated data. Data have been central to public health ever since John Snow used maps and case reports to identify the Broad Street pump as a source of cholera in London in 1854.1 Even today, data are just as central to public health, and digital technology provides new ways to collect data through efficient administrative interfaces, sensors, and non-traditional sources such as social media; new ways to link different data sources to generate new insights; and new ways to visualise and analyse data. Full-Text PDF Open Access

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,791
Score d'incertitude au seuil0,400

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,163
Tête enseignante GPT0,373
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations28
Publié2020
Routes d'admission1
Résumé présentoui

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