Mosquito surveillance and disease outbreak risk models to inform mosquito-control operations in Europe.


Journal

Current opinion in insect science
ISSN: 2214-5753
Titre abrégé: Curr Opin Insect Sci
Pays: Netherlands
ID NLM: 101635599

Informations de publication

Date de publication:
06 2020
Historique:
received: 20 12 2019
revised: 09 03 2020
accepted: 25 03 2020
pubmed: 14 5 2020
medline: 5 1 2021
entrez: 14 5 2020
Statut: ppublish

Résumé

Surveillance programs are needed to guide mosquito-control operations to reduce both nuisance and the spread of mosquito-borne diseases. Understanding the thresholds for action to reduce both nuisance and the risk of arbovirus transmission is becoming critical. To date, mosquito surveillance is mainly implemented to inform about pathogen transmission risks rather than to reduce mosquito nuisance even though lots of control efforts are aimed at the latter. Passive surveillance, such as digital monitoring (validated by entomological trapping), is a powerful tool to record biting rates in real time. High-quality data are essential to model the risk of arbovirus diseases. For invasive pathogens, efforts are needed to predict the arrival of infected hosts linked to the small-scale vector to host contact ratio, while for endemic pathogens efforts are needed to set up region-wide highly structured surveillance measures to understand seasonal re-activation and pathogen transmission in order to carry out effective control operations.

Identifiants

pubmed: 32403040
pii: S2214-5745(20)30046-8
doi: 10.1016/j.cois.2020.03.009
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

101-108

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Beniamino Caputo (B)

Department of Public Health and Infectious Diseases, University of Rome La Sapienza, Piazzale A. Moro 5, 38010, 00185 Rome, Italy. Electronic address: Beniamino.caputo@uniroma1.it.

Mattia Manica (M)

Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all' Adige, Italy.

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Classifications MeSH