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Enregistrement W2028569422 · doi:10.1093/infdis/jit604

Retrospective Studies: Excellent Tools to Complement Surveillance

2013· letter· en· W2028569422 sur OpenAlexaffabout
Xiangguo Qiu, Gary Kobinger

Notice bibliographique

RevueThe Journal of Infectious Diseases · 2013
Typeletter
Langueen
DomaineMedicine
ThématiqueViral Infections and Vectors
Établissements canadiensUniversity of ManitobaPublic Health Agency of Canada
Organismes subventionnairesnon disponible
Mots-clésPublic healthInfectious disease (medical specialty)DiseaseMedicinePopulationEnvironmental healthPublic health surveillanceDisease surveillanceEmerging infectious diseaseTransmission (telecommunications)Intensive care medicineRisk analysis (engineering)Computer scienceTelecommunicationsPathology

Résumé

récupéré en direct d'OpenAlex

(See the major article by Takahashi et al on pages 816–27.) Emerging infectious diseases pose an important challenge to public health globally. The identification of an etiologic pathogen responsible for an emerging infection in humans can be a major task. The time required to isolate and characterize a new infectious agent sufficiently to adopt control measures and, when possible, to select and make available curative and preventive treatments can be considerable and take years. If the novel pathogen exhibits rapid pathogenesis, causing serious illness and death in a significant percentage of infected individuals, in addition to efficient transmission and spread, serious consequences for the human population can be readily observed. The race between the spread of a high-consequence pathogen and the deployment of adequate public health measures can hardly be satisfactory from a public perspective, because it is a reactive chain of events to the initial spread of the infectious agent. Predicting the emergence of infectious diseases months or years in advance would be a perfect solution but for now has remained a theoretical concept. Improved surveillance systems to detect emerging infectious diseases and new technologies using algorithms specifically developed to isolate and identify causative infectious agents have been emphasized to minimize public health emergencies related to emerging infections. Enhanced surveillance and detection is one of the most significant improvements to regional and global public health of the past several decades. For example, several regional or national networks, such as the Global Public Health Intelligence Network in Canada and the Global Disease Detection program of the Centers for Disease Control and Prevention network in the United States, are informing and cooperating with international networks, such as the Global Outbreak Alert and Response Network developed by the World Health Organization [1–3]. Unfortunately, surveillance and detection methods are region specific, and many regions are currently not being actively monitored. Regions under surveillance often make use of different technologies and approaches that are not yet standardized, thus creating many potential holes in the global network intended to detect new pathogens before they cause damage. More work will be required before these newly developed systems are ideal, but the recognition of their importance and usefulness is a critical step toward enhanced preparedness and response and ultimately prevention. Viruses are responsible for a significant proportion of emerging infectious diseases, the numbers of which may be on an increasing trend overall [4, 5]. It is clear that surveillance and identification of unknown pathogens has greatly improved in the past decade, which has resulted in the concomitant rise in the number of new pathogens being discovered each year. Again, this increase has occurred despite the fact that the detection and identification of previously unknown pathogens currently depends on heterogeneous surveillance systems of variable sensitivity and precision. Regardless, heightened surveillance, detection, and identification of emerging pathogens are currently active and prolific fields of research. Whether there exists a real or apparent increase in the number of viruses emerging in the past 50 years, compared with the number that emerged 50 or 100 years earlier, there is certainly an increasing number of viruses being detected and identified in recent years. A PubMed search using the term “emerging viruses” revealed that the number of scientific articles published on the subject of emerging viruses has jumped from <10 per year in the early 1990s to nearly 70 in the early 2000s and up to 220 per year during 2011–2012, reflecting a growing interest from the scientific community and public health officials. Many new viruses potentially capable of endangering public health have been identified in approximately the past 10 years: SARS coronavirus, Middle East respiratory syndrome coronavirus, Lujo virus, and severe fever with thrombocytopenia syndrome virus (SFTSV) are just a few examples of a rapidly expanding list. SFTSV is the subject of a study by Takahashi et al [6] in this issue of the Journal. SFTSV is a Phlebovirus in the family Bunyaviridae that was first isolated in China and reported in 2011 to be a novel bunyavirus [7]. The virus is likely transmitted by ticks and was reported to be unique to China, with only 1 case reported elsewhere (in Dubai, in a patient from an area of North Korea that borders a region of China where SFTSV was epidemic) [8]. Human-to-human transmission has been described and attributed to close contact with infected blood [9–12]. SFTSV causes a febrile illness with a case-fatality rate initially reported to be around 30% and later revised to an average of 17% [7, 13]. Consequently, SFTSV infection can lead to serious disease that approximates the severity of other high-consequence pathogens. The study by Takahashi et al describes the first case of SFTSV isolated from the serum of a female resident showing SFTS-like disease in Japan and retrospective diagnoses of further cases dating back to 2005. The authors studied the relationship between the Japanese and the Chinese viruses by phylogenetic analysis. According to the study, SFTSV has been endemic to Japan for some time, with the earliest known case dating back to around the same time as the first documented case in China (2005 in Japan and 2006 in China). In addition, this study indicates a wider spread of the virus than previously thought and raises the possibility that it may be in regions beyond China and Japan. The recent identification of the related Heartland virus in 2012 in the United States [14], which is closely related to SFTSV, supports this hypothesis. The determination of the common lineage will provide more details on the timeline of the spread between China and Japan and may offer a glimpse into the possible determinants of virus spread. This retrospective study demonstrates the value of reevaluating past unknown cases to answer current questions and better define the extent of public health threats. It is an important publication that will bring more attention to this newly emerging virus and highlight the importance of effective surveillance and detection to public health. Overall, this study further supports improved monitoring and detection to discover viruses, like SFTSV, that may have gone unnoticed not so long ago. The number of undiscovered viruses was recently estimated at 320 000 [15]; today's technologies and surveillance protocols are actively identifying some of these viruses. The scientific community and public health systems may have reached a point where they are now identifying and characterizing previously unknown pathogens from fewer cases, owing to enhanced detection methods. Better surveillance and detection systems are widely recognized as a critical aspect of better preparedness and response to public health events involving the spread of infectious agents. Very few countries have active surveillance systems, which can detect unknown pathogens via algorithms. The detection of new viruses following systematic processes is a rapidly growing yet young scientific field. Interestingly, the present work by Takahashi et al stems from a retrospective study of a single initial case and was not facilitated by a centralized clinical database. These results bode well for the future and suggest that more important findings of the sort are to come, possibly at an accelerated pace, in the mostly unexplored world of unknown pathogens. Potential conflicts of interest. All authors: No reported conflicts. The authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,104
score de la tête « metaresearch » (Gemma)0,336
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,896
Score d'incertitude au seuil0,551

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1040,336
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0240,012
Études des sciences et des technologies0,0010,002
Communication savante0,0050,007
Science ouverte0,0050,009
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0340,008

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 Opus0,045
Tête enseignante GPT0,327
Écart entre enseignants0,282 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineMéthodes
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

Citations0
Publié2013
Routes d'admission2
Résumé présentoui

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Même revueThe Journal of Infectious DiseasesMême sujetViral Infections and VectorsTravaux en français237 207