Authors
Haijian Wang, Yi Zhang, Chunwei Sun, Yingyu Yang, Miaomiao He, Zhixin Wu, Jianguo Tan, Fan He
Published in
Medical and veterinary entomology. Oct 09, 2026. Epub Oct 09, 2026.
Abstract
Weather-based early warning systems for mosquito-borne diseases require quantitative understanding of the non-linear and delayed effects of meteorological factors on vector dynamics, yet these remain poorly characterised for Aedes albopictus in subtropical urban settings. We analysed weekly Mosquito Oviposition Index (MOI) data from 64,340 ovitraps deployed in Shanghai during 2018-2024, using Generalised Additive Models and Distributed Lag Non-Linear Models to quantify lag-specific temperature effects over 4 weeks. Univariate Generalised Additive Models (GAMs) identified six significant meteorological predictors (p < 0.01), but multivariate GAMs retained only precipitation as independently significant (p = 0.008), with minimum temperature marginally so (p = 0.064). DLNM confirmed a significant overall temperature effect (F(9, 18) = 4.727, p = 0.0025), revealing a unimodal pattern: positive cumulative effects at 14-20°C, peaking at ~18-20°C and negative effects above 23°C. Lag dynamics were asymmetrical: cool conditions (13°C) had immediate positive effects (lag 0-1 weeks), whereas warm conditions (25°C) produced delayed negative effects (lag 2-3 weeks). Minimum temperature exerts significant non-linear, delayed effects on Aedes albopictus oviposition in urban Shanghai, with an optimal range of ~18-20°C and distinct lag-specific patterns. These quantitative estimates support the development of temperature-based early warning indicators for vector surveillance.
PMID:
42851153
Bibliographic data and abstract were imported from PubMed on 09 Oct 2026.
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