A descriptive analysis of the spatio-temporal distribution of enteric diseases in New Brunswick, Canada
Bibliographic record
Abstract
BACKGROUND: Enteric diseases affect thousands of Canadians annually and several large outbreaks have occurred due to infection with enteric pathogens. The objectives of this study were to describe the spatial and temporal distributions of reportable Campylobacter, Escherichia coli, Giardia, Salmonella and Shigella from 1994 to 2002 in New Brunswick, Canada. By examining the spatial and temporal distributions of disease incidence, hypotheses as to potential disease risk factors were formulated. METHODS: Time series plots of monthly disease incidence were examined for seasonal and secular trends. Seasonality of disease incidence was evaluated using the temporal scan statistic and seasonal-trend loess (STL) decomposition methods. Secular trends were evaluated using negative binomial regression modeling. The spatial distribution of disease incidence was examined using maps of empirical Bayes smoothed estimates of disease incidence. Spatial clustering was examined by multiple methods, which included Moran's I and the spatial scan statistic. RESULTS: The peak incidence of Giardia infections occurred in the spring months. Salmonella incidence exhibited two peaks, one small peak in the spring and a main peak in the summer. Campylobacter and Escherichia coli O157 disease incidence peaked in the summer months. Moran's I indicated that there was significant positive spatial autocorrelation for the incidence of Campylobacter, Giardia and Salmonella. The spatial scan statistic identified clusters of high disease incidence in the northern areas of the province for Campylobacter, Giardia and Salmonella infections. The incidence of Escherichia coli infections clustered in the south-east and north-east areas of the province, based on the spatial scan statistic results. Shigella infections had the lowest incidence rate and no discernable spatial or temporal patterns were observed. CONCLUSIONS: By using several different spatial and temporal methods a robust picture of the spatial and temporal distributions of enteric disease in New Brunswick was produced. Disease incidence for several reportable enteric pathogens displayed significant geographic clustering indicating that a spatially distributed risk factor may be contributing to disease incidence. Temporal analysis indicated peaks in disease incidence, including previously un-reported peaks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".