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模拟法国本土致倦库蚊数量:对监测的影响。

Modeling Culicoides abundance in mainland France: implications for surveillance.

机构信息

CIRAD, UMR ASTRE, 34398, Montpellier, France.

ASTRE, CIRAD, INRA, Université de Montpellier, Montpellier, France.

出版信息

Parasit Vectors. 2019 Aug 6;12(1):391. doi: 10.1186/s13071-019-3642-1.

Abstract

BACKGROUND

Biting midges of the genus Culicoides Latreille (Diptera: Ceratopogonidae) are involved in the transmission of several viruses affecting humans and livestock, particularly bluetongue (BTV). Over the last decade, Culicoides surveillance has been conducted discontinuously and at various temporal and spatial scales in mainland France following the BTV epizootics in 2008-2009 and its reemergence and continuous circulation since 2015. The ability to predict seasonal dynamics and spatial abundance of Culicoides spp. is a key element in identifying periods and areas at high risk of transmission in order to strengthen surveillance for early detection and to establish seasonally disease-free zones. The objective of this study was to model the abundance of Culicoides spp. using surveillance data.

METHODS

A mixed-effect Poisson model, adjusted for overdispersion and taking into account temperature data at each trap location, was used to model the weekly relative abundance of Culicoides spp. over a year in 24 vector zones, based on surveillance data collected during 2009-2012. Vector zones are the spatial units used for Culicoides surveillance since 2016 in mainland France.

RESULTS

The curves of the predicted annual abundance of Culicoides spp. in vector zones showed three different shapes: unimodal, bimodal or plateau, reflecting the temporal variability of the observed counts between zones. For each vector zone, the model enabled to identify periods of vector activity ranging from 25 to 51 weeks.

CONCLUSIONS

Although the data were collected for surveillance purposes, our modeling approach integrating vector data with daily temperatures, which are known to be major drivers of Culicoides spp. activity, provided areas-specific predictions of Culicoides spp. abundance. Our findings provide decisions makers with essential information to identify risk periods in each vector zone and guide the allocation of resources for surveillance and control. Knowledge of Culicoides spp. dynamics is also of primary importance for modeling the risk of establishment and spread of midge-borne diseases in mainland France.

摘要

背景

库蠓属(双翅目:蠓科)的蠓叮咬与几种影响人类和牲畜的病毒的传播有关,特别是蓝舌病(BTV)。在 2008-2009 年 BTV 爆发及其重新出现和持续循环以来的过去十年中,法国大陆一直在断断续续地、在不同的时间和空间尺度上进行库蠓监测。预测库蠓属季节性动态和空间丰度的能力是确定高传播风险时期和地区的关键要素,以便加强监测以早期发现,并建立无病季节区。本研究的目的是使用监测数据来模拟库蠓的丰度。

方法

使用混合效应泊松模型,对过度分散进行调整,并考虑到每个陷阱位置的温度数据,根据 2009-2012 年收集的监测数据,对 24 个矢量区一年内库蠓属的每周相对丰度进行建模。自 2016 年以来,法国大陆一直在使用矢量区作为库蠓监测的空间单位。

结果

矢量区预测的年度库蠓属丰度曲线显示出三种不同的形状:单峰、双峰或高原,反映了各区域之间观察到的计数的时间变化。对于每个矢量区,该模型能够确定 25 至 51 周的矢量活动期。

结论

尽管数据是为监测目的而收集的,但我们的建模方法将矢量数据与已知是库蠓属活动的主要驱动因素的每日温度相结合,为特定区域提供了库蠓属丰度的预测。我们的研究结果为决策者提供了确定每个矢量区风险期的必要信息,并指导资源分配用于监测和控制。了解库蠓属的动态也是在法国大陆建立和传播蠓媒疾病风险模型的关键。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e92b/6683357/0c3a9ad33aee/13071_2019_3642_Fig1_HTML.jpg

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