Detection of disease outbreaks in pharmaceutical sales: neural networks and threshold algorithms
Bibliographic record
Abstract
Syndromic surveillance involves monitoring data that could indicate disease trends a population, such as gastrointestinal illness and respiratory illness. Different types of data can be used to detect potential outbreaks of disease or biological contaminant based on deviations from historical norms. The system discussed in this paper is intended to detect aberration by identifying changes in sequence data that do not match the norms for a given time and location. Artificial neural networks (ANNs) were used to detect changes in the sales trends for over-the-counter (OTC) pharmaceuticals. Early detection of an outbreak allows public health officials to respond faster to potential outbreak situations. Our research examines the application of a multilayer perceptron using back-propagation learning and a moving window of the daily OTC sales values as inputs. The network is trained to identify changes in the sales trends which can be an indicator of a change in the population's health. The sales data exhibits a large amount of variability and the ANN must be trained to process this without prematurely signalling that a change has occurred. The network is trained using multiple years (hundred's) of simulated sales data containing simulated outbreaks. The success of the ANN is determined by its accuracy and by the amount of time (number of days into the outbreak) that the system takes to correctly signal that an anomalous trend is occurring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".