Investigation of spatio-temporal clusters of positive leptospirosis polymerase chain reaction test results in dogs in the United States, 2009 to 2016
Bibliographic record
Abstract
BACKGROUND: Leptospirosis is a zoonotic disease of concern and an investigation of recent spatio-temporal trends of leptospirosis in dogs in the United States is needed. Leptospira PCR testing has become increasingly used in veterinary clinical medicine and these data might provide information on recent trends of disease occurrence. OBJECTIVES: To identify and describe clusters of PCR-positive Leptospira test results in dogs in the United States. ANIMALS: Leptospira real-time PCR test results from dogs (n = 40 118) in the United States from IDEXX Laboratories, Inc., between 2009 and 2016 were included in the analysis. METHODS: In this retrospective study, spatio-temporal clusters for a real-time PCR-positive test were identified using the space-time permutation scan statistic and the centroid of the zip code reported for each test. A maximum spatial window of 20% of the population at risk, and a maximum temporal window of 6 months were used. RESULTS: Seven statistically significant space-time clusters of Leptospira real-time PCR-positive test results were identified across the United States: 1 each located within the states of Arizona (2016), California (2014-2015), Florida (2010), South Carolina (2015), and 1 each located within the south-central region (2015), midwest region (2014), and northeast region (2011). Clusters ranged from 3 to 108 dogs and were identified during all years under study, except 2009, 2012, and 2013. CONCLUSIONS AND CLINICAL IMPORTANCE: The spatial and temporal components of leptospirosis in dogs in this study are similar to those in previous work. However, clusters were identified in new areas, demonstrating the complex epidemiology of this disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".