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Record W2610787465 · doi:10.5210/ojphi.v9i1.7791

2016 International Society for Disease Surveillance Conference New Frontiers in Surveillance: Data Science and Health Security

2017· article· en· W2610787465 on OpenAlexaboutno aff
Brian E. Dixon

Bibliographic record

VenueOnline Journal of Public Health Informatics · 2017
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
Fundersnot available
KeywordsDisease surveillancePublic healthPublic health surveillanceGlobeGlobal healthPolitical scienceData sharingCommunicable diseaseMedicinePublic relationsAlternative medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.077
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.085
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0090.006
Science and technology studies0.0030.004
Scholarly communication0.0190.010
Open science0.0040.014
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0710.043

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.146
GPT teacher head0.423
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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