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生存的变化可能对已形成和新出现的传染病的生态动态产生有意义的影响。然而，解决流行病生态中间接传播和环境传播的重要性仍然是一个持续的挑战。它需要准确测量病原体在各种宿主中的自由生存，并量化宿主和宿主之间的相互作用产生新感染的程度。这些问题对于新出现的病原体尤其突出，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，我们开发了一个机械数学模型，允许直接（宿主到宿主）和间接（环境）传播，然后将该模型与来自受新兴病毒（SARS-CoV-2）影响的 17 个国家的经验数据进行拟合。 ）。从生态角度来看，我们的模型强调了环境传播在传染病爆发期间驱动复杂的非线性动态的潜力。总而言之，我们建议将间接传播的替代模型与 SARS-CoV-2 的真实爆发数据进行拟合可能是有用的，因为它强调了间接机制可能在传染病的动态中发挥着未被充分认识的作用，并对公共卫生产生影响。