UK Centre for Ecology & Hydrology, Benson Lane, Wallingford, Oxfordshire, UK.
Ecology and Evolutionary Biology, School of Biological Sciences, University of Reading, Reading, UK.
Nat Commun. 2024 Sep 7;15(1):7823. doi: 10.1038/s41467-024-52144-5.
The incidence of vector-borne disease is on the rise globally, with burdens increasing in endemic countries and outbreaks occurring in new locations. Effective mitigation and intervention strategies require models that accurately predict both spatial and temporal changes in disease dynamics, but this remains challenging due to the complex and interactive relationships between environmental variation and the vector traits that govern the transmission of vector-borne diseases. Predictions of disease risk in the literature typically assume that vector traits vary instantaneously and independently of population density, and therefore do not capture the delayed response of these same traits to past biotic and abiotic environments. We argue here that to produce accurate predictions of disease risk it is necessary to account for environmentally driven and delayed instances of phenotypic plasticity. To show this, we develop a stage and phenotypically structured model for the invasive mosquito vector, Aedes albopictus, and dengue, the second most prevalent human vector-borne disease worldwide. We find that environmental variation drives a dynamic phenotypic structure in the mosquito population, which accurately predicts global patterns of mosquito trait-abundance dynamics. In turn, this interacts with disease transmission to capture historic dengue outbreaks. By comparing the model to a suite of simpler models, we reveal that it is the delayed phenotypic structure that is critical for accurate prediction. Consequently, the incorporation of vector trait relationships into transmission models is critical to improvement of early warning systems that inform mitigation and control strategies.
虫媒传染病的发病率在全球呈上升趋势，在流行地区的负担不断增加，新的地区也出现了疫情。有效的缓解和干预策略需要能够准确预测疾病动态的空间和时间变化的模型，但由于环境变化与控制虫媒传染病传播的媒介特征之间复杂的相互关系，这仍然具有挑战性。文献中对疾病风险的预测通常假设媒介特征会即时且独立于种群密度而变化，因此无法捕捉到这些特征对过去生物和非生物环境的延迟反应。我们在这里认为，要准确预测疾病风险，有必要考虑到由环境驱动的和延迟的表型可塑性。为了证明这一点，我们为入侵性蚊子媒介白纹伊蚊和登革热（世界上第二大常见的人类虫媒传染病）开发了一个阶段和表型结构模型。我们发现，环境变化驱动了蚊子种群的动态表型结构，这种结构可以准确预测蚊子特征丰度动态的全球模式。反过来，这又与疾病传播相互作用，从而捕捉到历史上的登革热疫情。通过将该模型与一系列更简单的模型进行比较，我们发现正是延迟的表型结构对准确预测至关重要。因此，将媒介特征关系纳入传播模型对于改进预警系统以告知缓解和控制策略至关重要。
