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Record W2056012414 · doi:10.1111/1365-2664.12050

Predicting the rate of invasion of the agent of Lyme disease <i>Borrelia burgdorferi</i>

2013· article· en· W2056012414 on OpenAlexafffund
Nicholas H. Ogden, L. Robbin Lindsay, Patrick A. Leighton

Bibliographic record

VenueJournal of Applied Ecology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversité de MontréalPublic Health Agency of Canada
FundersPublic Health Agency of Canada
KeywordsBorrelia burgdorferiIxodes scapularisTickNymphLyme diseaseBiologyZoologyLarvaEcologyRange (aeronautics)IxodidaeVirologyImmunology

Abstract

fetched live from OpenAlex

Summary Identifying invading tick populations provides early warning for emerging tickborne diseases that are expanding their geographic range. But how fast do tickborne pathogens invade after ticks become established? Surveillance data for the tick I xodes scapularis and the agent of L yme disease B orrelia burgdorferi in southern C anada, an area where these species currently are invading, revealed a space‐time cluster of ticks of low B . burgdorferi infection prevalence in southern Q uebec signalling the location where tick populations became established beginning in 2004. The cluster disappeared in 2009, indicating a 5‐year gap between tick and B . burgdorferi invasion. Simulations of a model of I . scapularis populations and B . burgdorferi transmission identified numbers of immigrating ticks, rather than host density and diversity, as key determinants of the speed of pathogen invasion after ticks become established. Greater numbers of immigrating infected nymphs would be expected in Central compared with E astern C anada because nymphal and larval ticks in source populations in M idwestern USA are active in spring when migratory birds can carry ticks north. Whereas in northeastern USA , tick populations that are sources for immigrating ticks for E astern C anada have active nymphs, but few larvae are active in spring. Consequently, we hypothesized that a 5‐year gap would occur between tick and B . burgdorferi invasion in E astern C anada, but a much shorter gap would occur in C entral C anada. Consistent with this hypothesis, analysis of surveillance data revealed clusters of ticks with low infection prevalence of ≥5 years duration in locations in E astern C anada where I . scapularis is invading, but a nonsignificant cluster of only 3‐year duration in regions of C entral C anada where I . scapularis is invading. Synthesis and applications . We have identified the speed at which the pathogen B orrelia burgdorferi invades following the invasion of the tick I xodes scapularis , and that the synchrony of larval and nymphal tick activity in spring is a key factor determining the gap between tick and pathogen invasion. This has immediate application in interpreting imminence of L yme disease risk when surveillance identifies emerging tick populations in C anada. It also has general application in predicting of the speed of invasion of emerging tickborne pathogens elsewhere in the world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.202
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2013
Admission routes2
Has abstractyes

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