Public Health Surveillance and Infectious Disease Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emerging infectious diseases, such as HIV/AIDS, SARS, and pandemic influenza, and the anthrax attacks of 2001, have demonstrated that we remain vulnerable to health threats caused by infectious diseases. The importance of strengthening global public health surveillance to provide early warning has been the primary recommendation of expert groups for at least the past 2 decades. However, despite improvements in the past decade, public health surveillance capabilities remain limited and fragmented, with uneven global coverage. Recent initiatives provide hope of addressing this issue, and new technological and conceptual advances could, for the first time, place capability for global surveillance within reach. Such advances include the revised International Health Regulations (IHR 2005) and the use of new data sources and methods to improve global coverage, sensitivity, and timeliness, which show promise for providing capabilities to extend and complement the existing infrastructure. One example is syndromic surveillance, using nontraditional and often automated data sources. Over the past 20 years, other initiatives, including ProMED-mail, GPHIN, and HealthMap, have demonstrated new mechanisms for acquiring surveillance data. In 2009 the U.S. Agency for International Development (USAID) began the Emerging Pandemic Threats (EPT) program, which includes the PREDICT project, to build global capacity for surveillance of novel infections that have pandemic potential (originating in wildlife and at the animal-human interface) and to develop a framework for risk assessment. Improved understanding of factors driving infectious disease emergence and new technological capabilities in modeling, diagnostics and pathogen identification, and communications, such as using the increasing global coverage of cellphones for public health surveillance, can further enhance global surveillance.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it