Density predictive model for an outbreak in adult female Aedes albopictus (Diptera: Culicidae) in Japan.

Autor:	Watanabe S; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan., Nakane K; Okazaki City Public Health Center, Okazaki 444-8545, Japan., Kitahara Y; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan., Nishihara N; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan., Nomura H; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan., Fukase F; Okazaki City Public Health Center, Okazaki 444-8545, Japan., Uno K; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan., Kondo T; Faculty of Medical Sciences, Shubun University, Ichinomiya 491-0938, Japan., Ueyama J; Department of Biomolecular Sciences, Field of Omics Health Sciences, Nagoya University Graduate School of Medicine, Nagoya 461-8673, Japan.
Jazyk:	angličtina
Zdroj:	Journal of medical entomology [J Med Entomol] 2024 Mar 13; Vol. 61 (2), pp. 418-426.
DOI:	10.1093/jme/tjad166
Abstrakt:	We developed a predictive model for activities and outbreaks of female Aedes albopictus Skuse, using meteorological data. The number of Ae. albopictus collected from human bait-sweep net collection (h-BNC) surveillance, conducted by the local government between 2010 and 2019 in Japan, was adopted as a mosquito-activity indicator. The best model was composed of the backward cumulative and backward moving mean of meteorological data (parameters that were measured daily include mean, maximum, and minimum temperature, mean humidity, amount of precipitation, maximum wind speed, and sunshine hours). The root mean squared error (RMSE) and the coefficient of determination (R2) of the best model for the test set, which was not included in the training dataset, were 1.33 and 0.74, respectively. The best model was applied to predict the number of Ae. albopictus obtained from our own h-BNC surveillance in Okazaki City, Japan. RMSE and R2 of the results were 1.17 and 0.92, respectively. The present model, using publicly available meteorological values, can predict the collection number of adult Ae. albopictus using h-BNC surveillance thereby providing information to control mosquito activities and outbreaks. Therefore, it may be possible to mitigate the risk of mosquito-borne infections and secondary adverse effects of mosquito bites, such as infectious impetigo and deterioration of the quality of life. (© The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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