2016 International Society for Disease Surveillance Conference New Frontiers in Surveillance: Data Science and Health Security
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".