Affordable System for Rapid Detection and Mitigation of Emerging Diseases
Bibliographic record
Abstract
South and South-East Asian countries are currently in the midst of a new epidemic of Dengue Fever. This paper presents disease surveillance systems in Sri Lanka and India, monitoring a handful of communicable diseases termed as notifiable. These systems typically require 15-30 days to communicate field data to the central Epidemiology Units, to be then manually processed (Prashant & Waidyanatha, 2010). Currently used analyses rely on aggregating counts of notifiable disease cases by district, disease, and week. The Real-Time Biosurveillance Program (RTBP), a multi-partner initiative, aims at addressing those challenges by developing affordable paradigm-changing Information Communication Technology (ICT), implementing and field-testing them in India and Sri Lanka. Key components of the proposed solution include real-time digitization of clinical information at hospitals and clinics with the mHealthSurvey mobile phone software (Kannan et al., 2010), detecting anomalies in large multivariate biosurveillance data using the T-Cube Web Interface spatio-temporal statistical analysis tool (Ray et al., 2008), and disseminating critical information pertaining to the adverse events to healthcare workers using the Sahana Alerting Module (Sampath et al., 2010). This paper provides an overview of the applications and discusses utility of the technologies for real-time detection and mitigation of emerging threats to public health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.043 |
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.
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 source (direct Gemma or distilled Codex), 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".