KIWI: A technology for public health event monitoring and early warning 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
OBJECTIVES: To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats. METHODS: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper. RESULTS: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%). DISCUSSION: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities. CONCLUSIONS: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it