Departamento de Ecología y Gestión Ambiental, Centro Universitario Regional Este (CURE), Universidad de la República, Uruguay; CICADA, Centro Interdisciplinario de Ciencia de Datos y Aprendizaje Automático, Universidad de la República, Uruguay.
CICADA, Centro Interdisciplinario de Ciencia de Datos y Aprendizaje Automático, Universidad de la República, Uruguay; Laboratorio de Neurociencias, Instituto de Biología, Facultad de Ciencias, Universidad de la República, Uruguay.
J Theor Biol. 2022 Jun 7;542:111109. doi: 10.1016/j.jtbi.2022.111109. Epub 2022 Mar 26.
Contact tracing, case isolation, quarantine, social distancing, and other non-pharmaceutical interventions (NPIs) have been a cornerstone in managing the COVID-19 pandemic. However, their effects on disease dynamics are not fully understood. Saturation of contact tracing caused by the increase of infected individuals has been recognized as a crucial variable by healthcare systems worldwide. Here, we model this saturation process with a mechanistic and a phenomenological model and show that it induces an Allee effect which could determine an infection threshold between two alternative states-containment and outbreak. This transition was considered elsewhere as a response to the strength of NPIs, but here we show that they may be also determined by the number of infected individuals. As a consequence, timing of NPIs implementation and relaxation after containment is critical to their effectiveness. Containment strategies such as vaccination or mobility restriction may interact with contact tracing-induced Allee effect. Each strategy in isolation tends to show diminishing returns, with a less than proportional effect of the intervention on disease containment. However, when combined, their suppressing potential is enhanced. Relaxation of NPIs after disease containment--e.g. because vaccination--have to be performed in attention to avoid crossing the infection threshold required to a novel outbreak. The recognition of a contact tracing-induced Allee effect, its interaction with other NPIs and vaccination, and the existence of tipping points contributes to the understanding of several features of disease dynamics and its response to containment interventions. This knowledge may be of relevance for explaining the dynamics of diseases in different regions and, more importantly, as input for guiding the use of NPIs, vaccination campaigns, and its combination for the management of epidemic outbreaks.
接触者追踪、病例隔离、检疫、社交距离和其他非药物干预（NPIs）一直是管理 COVID-19 大流行的基石。然而，它们对疾病动态的影响尚未完全了解。世界各地的医疗保健系统都认识到，由于感染人数的增加，接触者追踪已经饱和，这是一个关键变量。在这里，我们使用一种机制模型和一种现象模型来模拟这个饱和过程，并表明它会引起一种聚集效应，这种聚集效应可能会决定两种替代状态（控制和爆发）之间的感染阈值。这种转变在其他地方被认为是对 NPIs 强度的反应，但在这里我们表明，它也可能由感染人数决定。因此，NPIs 的实施和控制后的放松的时机对其有效性至关重要。控制策略，如接种疫苗或限制流动性，可能会与接触者追踪引起的聚集效应相互作用。每种策略单独使用时，效果都会逐渐减弱，干预对疾病控制的效果呈不成比例的减少。然而，当它们结合使用时，它们的抑制潜力会增强。在疾病得到控制后，例如由于接种疫苗，NPIs 的放松必须谨慎进行，以避免越过导致新爆发所需的感染阈值。认识到接触者追踪引起的聚集效应，及其与其他 NPIs 和疫苗接种的相互作用，以及存在的临界点，有助于理解疾病动态的几个特征及其对控制干预的反应。这些知识可能有助于解释不同地区疾病的动态，更重要的是，作为指导 NPIs、疫苗接种运动及其组合用于管理流行病爆发的输入。
