Measuring the impact of sea surface temperature on the human incidence of Vibrio sp. infection in British Columbia, Canada, 1992–2017
Bibliographic record
Abstract
BACKGROUND: Vibrio growth in the environment is related to sea surface temperature (SST). The incidence of human Vibrio illness increased sharply in British Columbia (BC) between 2008 and 2015 for unknown reasons, culminating in the largest outbreak of shellfish-associated Vibrio parahaemolyticus (Vp) in Canadian history in 2015. Our objective was to assess the relationship between SST and Vibrio illness in BC, Canada during 1992-2017 and assess the role of SST and other environmental factors in the 2015 Vp outbreak. METHODS: Cases of Vibrio infection reported to the BC Centre for Disease Control during 1992-2017 were used. SST data were obtained from NOAA and NASA. We assessed changes in incidence trend of annual Vibrio cases during 1992-2017 using a Poisson regression. We assessed the correlation between annual Vibrio cases and the average annual maximum SST using a Spearman rank-order correlation. We modeled the association between weekly Vp case counts, SST and other environmental factors during 2007-2017 using a Poisson regression. RESULTS: There was a significant increase in Vibrio cases between 2008 and 2015 (annual slope = 0.163, P < 0.001). Increased Vibrio incidence was observed in most El Niño years. There was a significant correlation between annual Vibrio cases and maximum SST from 1992 to 2017 (r = 0.46, P = 0.018). Our model captured observed seasonal variation in shellfish-associated Vp in most years, but underestimated the 2015 Vp outbreak. CONCLUSIONS: Vibrio incidence has been increasing concurrently with increasing SST in BC during 2008-2015. The 2015 Vp outbreak was not fully explained by climatic factors and may in part have been associated with other factors. Vp subtyping would be useful in the future to understand the combined effects of SST changes and strain emergence.
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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 | 0.000 |
| 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 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".