媒介体内病毒动力学对主要虫媒病毒——登革热、寨卡和基孔肯雅热——外潜伏期建模方式的挑战
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya
AI总结：
针对登革热、寨卡和基孔肯雅热，研究构建多阶段随机区室模型并用ABC-SMC校准，揭示EIP并非指数分布且仅少数蚊子跨越传播屏障。
摘要：
虫媒病毒对全球人类、动物和植物健康构成重大威胁。为阐明传播、预测其扩散并有效加以控制，机理建模已证明其有用性。然而，大多数模型依赖于关于如何表示外潜伏期（EIP）的假设：在EIP期间发生的媒介体内病毒动力学（IVD）被近似为单一状态。经过平均持续时间后，所有已暴露媒介都会变为传染性媒介。这背后隐藏着两个强假设：（i）EIP在媒介种群中呈指数分布；（ii）病毒在所有已暴露媒介中都能成功跨越感染、播散和传播屏障。为评估这些假设，我们开发了一个随机区室模型，用于表示连续的IVD阶段，这些阶段与是否跨越上述三个屏障相关。我们采用带模型选择的ABC-SMC（近似贝叶斯计算—序贯蒙特卡洛）方法对模型进行校准。我们系统检索了关于感染登革热、基孔肯雅热或寨卡病毒的伊蚊实验感染的文献数据。我们证明了指数假设与登革热和寨卡病毒观测到的EIP分布之间存在差异，并识别出更合适的EIP分布。我们还量化了最终变为传染性媒介的受感染蚊子比例，强调通常只有一小部分能够跨越三个屏障。本研究提供了一个通用建模框架，可应用于具有类似可用数据的其他虫媒病毒。我们的模型还可与种群尺度模型耦合，以辅助未来的虫媒病毒控制。
英文摘要：
Arboviruses represent a significant threat to human, animal, and plant health worldwide. To elucidate transmission, anticipate their spread and efficiently control them, mechanistic modelling has proven its usefulness. However, most models rely on assumptions about how the extrinsic incubation period (EIP) is represented: the intra-vector viral dynamics (IVD), occurring during the EIP, is approximated by a single state. After an average duration, all exposed vectors become infectious. Behind this are hidden two strong hypotheses: (i) EIP is exponentially distributed in the vector population; (ii) viruses successfully cross the infection, dissemination, and transmission barriers in all exposed vectors. To assess these hypotheses, we developed a stochastic compartmental model which represents successive IVD stages, associated to the crossing or not of these three barriers. We calibrated the model using an ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) method with model selection. We systematically searched for literature data on experimental infections of Aedes mosquitoes infected by either dengue, chikungunya, or Zika viruses. We demonstrated the discrepancy between the exponential hypothesis and observed EIP distributions for dengue and Zika viruses and identified more relevant EIP distributions . We also quantified the fraction of infected mosquitoes eventually becoming infectious, highlighting that often only a small fraction crosses the three barriers. This work provides a generic modelling framework applicable to other arboviruses for which similar data are available. Our model can also be coupled to population-scale models to aid future arbovirus control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
