The risk of contact between visitors and Borrelia burgdorferi-infected ticks is associated with fine-scale landscape features in a southeastern Canadian nature park
Notice bibliographique
Résumé
BACKGROUND: Infectious diseases are emerging across temperate regions of the world, and, for some, links have been made between landscapes and emergence dynamics. For tick-borne diseases, public parks may be important exposure sites for people living in urbanized areas of North America and Europe. In most cases, we know more about the ecological processes that determine the hazard posed by ticks as disease vectors than we do about how human population exposure varies in urban natural parks. METHODS: In this study, infrared counters were used to monitor visitor use of a public natural park in southern Quebec, Canada. A risk index representing the probability of encounters between humans and infected vectors was constructed. This was done by combining the intensity of visitor trail use and the density of infected nymphs obtained from field surveillance. Patterns of risk were examined using spatial cluster analysis. Digital forest data and park infrastructure data were then integrated using spatially explicit models to test whether encounter risk levels and its components vary with forest fragmentation indicators and proximity to park infrastructure. RESULTS: Results suggest that, even at a very fine scales, certain landscape features and infrastructure can be predictors of risk levels. Both visitors and Borrelia burgdorferi-infected ticks concentrated in areas where forest cover was dominant, so there was a positive association between forest cover and the risk index. However, there were no associations between indicators of forest fragmentation and risk levels. Some high-risk clusters contributed disproportionately to the risk distribution in the park relative to their size. There were also two high-risk periods, one in early summer coinciding with peak nymphal activity, and one in early fall when park visitation was highest. CONCLUSIONS: Here, we demonstrate the importance of integrating indicators of human behaviour visitation with tick distribution data to characterize risk patterns for tick-borne diseases in public natural areas. Indeed, understanding the environmental determinants of human-tick interactions will allow organisations to deploy more effective risk reduction interventions targeted at key locations and times, and improve the management of public health risks associated with tick-borne diseases in public spaces.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».