The added value of One Health surveillance: data from questing ticks can provide an early signal for anaplasmosis outbreaks in animals and humans
Bibliographic record
Abstract
OBJECTIVE: In 2021, a first outbreak of anaplasmosis occurred in animals and humans in southern Québec, with 64% of confirmed human cases located in Bromont municipality. Ixodes scapularis ticks and Peromyscus mouse ear biopsies collected in Bromont from 2019 to 2021 were analyzed for Anaplasma phagocytophilum (Ap) with the objective of determining whether an early environmental signal could have been detected before the outbreak. METHODS: Samples were collected for a concurrent study aiming to reduce Lyme disease risk. Between 2019 and 2021, up to 14 experimental sites were sampled for ticks and capture of small mammals took place on three sites in 2021. Samples were screened for Ap using multiplex real-time PCR, and genetic strains were identified using a single-nucleotide polymorphism assay. RESULTS: Analyses showed an increase of 5.7% in Ap prevalence in ticks (CI95: 1.5-9.9) between 2019 and 2020, i.e., one year before the outbreak. A majority of Ap-positive ticks were infected with the zoonotic strain (68.8%; CI95: 50.0-83.9) during the study period. In 2021, 2 of 59 captured Peromycus mice were positive for Ap, for a prevalence of 3.4% (CI95: 0.4-11.7). CONCLUSION: We conclude that data collected in Bromont could have provided an early signal for an anaplasmosis risk increasing in the targeted region. This is a reminder that integrated surveillance of tick-borne diseases through structured One Health programs, i.e. systematically integrating data from humans, animals and the environment, can provide useful and timely information for better preparedness and response in 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".