Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06511, USA.
Department of Ecology and Evolutionary Biology, Brown University, Providence, 02912, USA.
Sci Rep. 2020 Nov 27;10(1):20786. doi: 10.1038/s41598-020-77048-4.
Variation in free-living microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of indirect and environmental transmission in the ecology of epidemics remains a persistent challenge. It requires accurately measuring the free-living survival of pathogens across reservoirs of various kinds and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, nonlinear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting alternative models with indirect transmission to real outbreak data from SARS-CoV-2 can be useful, as it highlights that indirect mechanisms may play an underappreciated role in the dynamics of infectious diseases, with implications for public health.
自由生活的微寄生虫的存活变化会对已建立和新出现的传染病的生态动态产生有意义的影响。然而，解决间接和环境传播在传染病生态学中的重要性仍然是一个持续存在的挑战。这需要准确测量各种宿主库中病原体的自由生活存活，并量化宿主和宿主库之间的相互作用产生新感染的程度。这些问题对于新兴病原体尤其重要，因为稀疏和嘈杂的数据可能会混淆不同感染途径的相对贡献。在这项研究中，我们开发了一种机械的、数学的模型，该模型允许直接（宿主到宿主）和间接（环境）传播，然后将该模型拟合到受新兴病毒（SARS-CoV-2）影响的 17 个国家的实证数据。从生态学的角度来看，我们的模型突出了环境传播在传染病爆发期间产生复杂、非线性动力学的潜力。总之，我们提出，用具有间接传播的替代模型来拟合来自 SARS-CoV-2 的真实爆发数据可能是有用的，因为它突出了间接机制在传染病动力学中可能扮演着被低估的角色，这对公共卫生具有重要意义。
