2016 International Society for Disease Surveillance Conference New Frontiers in Surveillance: Data Science and Health Security
Notice bibliographique
Résumé
The International Society for Disease Surveillance (ISDS) held itsfifteenth annual conference in Atlanta, GA, from December 6-8, 2016.Since 2001, individuals interested in sharing and learning emergingtrends in surveillance research and practice have found the ISDSAnnual Conference a unique forum to advance their knowledge in thediscipline of disease surveillance. The 15th ISDS conference receiveda total of 233 abstracts from 23 countries. From the submissions, 189(81%) were accepted for presentation at the conference as an oralpresentation (N=96) or poster (N=93).The theme for the 15th annual conference was New Frontiers inSurveillance: Data Science and Health Security. The theme unitedtwo dominant trends in public health surveillance: 1) a growing desireto extract knowledge from increasing volumes of structured andunstructured data available from health information systems; and 2)increased pressure on nations to strengthen their capacity for diseasesurveillance and response to outbreaks when and where they occuracross the globe. In addition to the major themes of the conference,abstracts were accepted in additional tracks that remain important tothe practice of public health around the world: One Health unitinganimal and human health; Methodological advances in appliedepidemiology; Public health informatics; Public health policy; andBiosurveillance practice.As usual, accepted abstracts for the 2016 ISDS Conferencespan the breadth of surveillance practice around the globe. Thereare timely abstracts on the detection and response to vector-bornediseases such as Zika virus and chikungunya across the Americas,as well as abstracts on the surveillance of opioid abuse observedin many parts of the U.S. Other abstracts cover the surveillance ofnon-communicable diseases that are now the leading causes of deathglobally. Additionally, some abstracts focus on capacity buildingwithin low resource settings on multiple continents to enhance globalhealth security. While other abstracts describe the impact of healthinformation technology (or eHealth) policies on surveillance practiceat local, national, or regional levels. And still other abstracts containemerging, novel methods that advance our understanding of howto analyze “Big” data or reduce the messiness associated with realworldsurveillance data. Together these abstracts represent the broad,diverse and interesting nature of surveillance practice. Furthermore,the abstracts represent important work being done in high incomecountries like the U.S., Canada and the U.K. as well as critical workbeing done in low-and-middle income nations such as Nigeria,Pakistan, and Sierra Leone.I wish to thank the dedicated members of the Scientific ProgrammingCommittee (SPC) and ISDS staff who helped to manage the process ofselecting this year’s abstracts for presentation. These individuals aredomain experts across the spectrum of tracks and themes representedin the program, and their service is much appreciated. The SPC helpedto recruit dozens of public health researchers and practitioners whoalso spent time reviewing abstracts. I also thank these volunteersfor contributing to the richness and diversity of this year’s program.Finally, I wish to thank the Track Chairs who reviewed abstracts andrecruited peers to perform reviews, and whom helped me organizepresentations into meaningful sessions for the final conferenceprogram. Their names are listed in the proceedings to recognize theirselfless service to ISDS and the field of public health surveillance.I hope that these proceedings help to advance scientificunderstanding and the practice of surveillance in public health. Pleaseuse the knowledge herein to improve how you practice or evaluatesurveillance in your jurisdiction. Or you may find ways to apply theknowledge elsewhere in population health. However you use it, I askthat you document your lessons or findings and submit to ISDS inthe future to share the outcomes with others. Together we can reducethe burden of disease and improve health outcomes for populationsglobally.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».