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Enregistrement W2007025273 · doi:10.1016/s2214-109x(14)70356-0

Role of big data in the early detection of Ebola and other emerging infectious diseases

2014· letter· en· W2007025273 sur OpenAlex

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Notice bibliographique

RevueThe Lancet Global Health · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueData-Driven Disease Surveillance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublic healthDisease surveillancePublic health surveillanceOutbreakGlobal healthThe InternetPandemicGovernment (linguistics)Social mediaMedicineInfectious disease (medical specialty)Environmental healthInternet privacyDiseasePolitical scienceComputer scienceWorld Wide WebVirologyCoronavirus disease 2019 (COVID-19)

Résumé

récupéré en direct d'OpenAlex

The lack of adequate disease surveillance systems in Ebola-affected areas has both reduced the ability to respond locally and has increased global risk. There is a need to improve disease surveillance in vulnerable regions, and digital surveillance could present a viable approach. Digital surveillance seeks to gain knowledge of public health issues through the analysis of data in the digital domain (such as internet search metrics, Twitter posts, or online news stories), the distribution of these data, and patterns of access. It has already shown some promise.1Anema A Kluberg S Wilson K et al.Digital surveillance for enhanced detection and response to outbreaks.Lancet Infect Dis. 2014; 14: 1035-1037Summary Full Text Full Text PDF PubMed Scopus (31) Google Scholar In 2002, the Global Public Health Intelligence Network, a news-feed aggregator developed by the Public Health Agency of Canada, provided the first alert of SARS (more than 2 months before publication by WHO) and prompted the confirmation of an emerging disease event by the Chinese Government.2Mykhalovskiy E Weir L The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health.Can J Public Health. 2006; 97: 42-44PubMed Google Scholar A more recently developed system, HealthMap,3Brownstein JS Freifeld CC HealthMap: the development of automated real-time internet surveillance for epidemic intelligence.Eurosurveillance. 2007; 12: E071129 5Google Scholar is currently applying a similar data-aggregation approach to monitor the evolving Ebola outbreak; HealthMap identified news stories reporting a strange fever in Guinea on March 14, 2014—9 days before the release of official case information for the ongoing Ebola outbreak. Currently, the most comprehensively investigated digital surveillance approach is based on monitoring of internet search metrics.4Milinovich GJ Williams GM Clements AC Hu W Internet-based surveillance systems for monitoring emerging infectious diseases.Lancet Infect Dis. 2014; 14: 160-168Summary Full Text Full Text PDF PubMed Scopus (183) Google Scholar These systems work on the premise that people who contract a disease are likely to seek information on their condition on the internet and an estimate of disease in the community can be produced by monitoring the frequency of specific searches. Overall, results for this approach have been promising; the scope of research has, however, been limited to a small number of diseases, particularly influenza, and has mainly focused on industrialised countries. We used Google Trends to assess the volume and location of Google searches for “ebola” between Jan 1, 2014, and Oct 27, 2014. Search volume data were downloaded from Google Trends on Oct 28, 2014. Internet search volume increased markedly from affected regions over the course of the epidemic, and most Google searches during 2014 for “ebola” originated from the regions most affected (Liberia, Guinea, and Sierra Leone; figure). Furthermore, search frequency in these countries was highly correlated with epidemic curves (appendix). Pairwise correlations, by Spearman's Rho, between weekly national case numbers5WHOEbola response roadmap situation report—25 October 2014.http://apps.who.int/iris/bitstream/10665/137185/1/roadmapupdate25Oct14_eng.pdf?ua=1Google Scholar and Google Trends search frequencies for the term “ebola” were 0·54 for Guinea, 0·70 for Liberia, 0·68 for Sierra Leone (all p<0·001; one-tailed). There is clearly some noise in these data and this is not unexpected considering analyses only used a single search term and given the significant public and media interest in the outbreak. Also, whether digital surveillance systems might have facilitated earlier detection or reduced the effect of the ongoing Ebola epidemic remains moot. Overall, however, these results are promising and, generally, support the development of internet-based surveillance systems for developing countries. Shifting patterns of health-seeking behaviour, the digitisation of society, and increased technology uptake present an opportunity to address emerging infectious disease events. Internet use in developing countries is high and continues to grow; as such, sufficient infrastructure exists on which to develop digital infectious disease surveillance and early warning systems in many of these regions. There will be challenges to developing digital surveillance systems for regions that are not currently covered sufficiently by traditional surveillance systems. The potential impact for these regions, however, extends beyond the local scale. These systems will have global relevance and could contribute to improved global health security. We declare no competing interests. Download .pdf (.39 MB) Help with pdf files Supplementary appendix

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.

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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,337
Score d'incertitude au seuil0,939

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,038
Tête enseignante GPT0,336
Écart entre enseignants0,298 · 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