Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review.

Autor:	Sharma Y; Department of Mathematics and Statistics, University of Victoria, Victoria, Canada., Laison EKE; Département de Médecine Préventive et Sociale, University of Montréal, Montréal, Canada., Philippsen T; Department of Mathematics and Statistics, University of Victoria, Victoria, Canada., Ma J; Department of Mathematics and Statistics, University of Victoria, Victoria, Canada., Kong J; Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada., Ghaemi S; Digital Technologies Research Center, National Research Council of Canada, Toronto, Canada., Liu J; Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatchewan, Canada., Hu F; Department of Mathematics and Statistics, University of Montréal, Montréal, Canada., Nasri B; Département de Médecine Préventive et Sociale, University of Montréal, Montréal, Canada.
Jazyk:	angličtina
Zdroj:	Lancet regional health. Americas [Lancet Reg Health Am] 2024 Mar 07; Vol. 32, pp. 100706. Date of Electronic Publication: 2024 Mar 07 (Print Publication: 2024).
DOI:	10.1016/j.lana.2024.100706
Abstrakt:	Tick-borne diseases (TBD) remain prevalent worldwide, and risk assessment of tick habitat suitability is crucial to prevent or reduce their burden. This scoping review provides a comprehensive survey of models and data used to predict I. scapularis distribution and abundance in North America. We identified 4661 relevant primary research articles published in English between January 1st, 2012, and July 18th, 2022, and selected 41 articles following full-text review. Models used data-driven and mechanistic modelling frameworks informed by diverse tick, hydroclimatic, and ecological variables. Predictions captured tick abundance (n = 14, 34.1%), distribution (n = 22, 53.6%) and both (n = 5, 12.1%). All studies used tick data, and many incorporated both hydroclimatic and ecological variables. Minimal host- and human-specific data were utilized. Biases related to data collection, protocols, and tick data quality affect completeness and representativeness of prediction models. Further research and collaboration are needed to improve prediction accuracy and develop effective strategies to reduce TBD. Competing Interests: The authors declare no competing interests. (© 2024 The Author(s).)
Databáze:	MEDLINE
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