School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Department of Ecology and Evolutionary Biology, Princeton, NJ, USA.
Epidemics. 2023 Mar;42:100664. doi: 10.1016/j.epidem.2022.100664. Epub 2023 Jan 10.
Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).
无症状和有症状的 SARS-CoV-2 感染可能具有不同的传播特征时间尺度。这些时间尺度差异可以塑造疫情动态，并影响人群中对疫情强度、速度和可控性的估计。例如，先前的研究主要集中在疫情的初始指数增长阶段，发现无症状传播与有症状传播的时间尺度差异越大，从疫情病例数据推断的基本繁殖数就越低。在这一工作的基础上，我们使用一系列非线性传染病模型来探讨无症状和有症状传播的时间尺度差异如何导致疫情过程中无症状传播的实际比例发生变化。首先，我们发现，当无症状传播的时间尺度长于有症状传播的时间尺度时，随着总发病率的降低，无症状传播的有效比例增加。此外，当感染者和被感染者之间的感染状态相关（例如，由于对症状有剂量依赖性的影响）时，这些时间尺度对疫情动态的影响会增强。接下来，我们应用这些发现来理解时间尺度差异对具有年龄依赖性聚集混合的人群以及在其中感染症状的概率随年龄增加而增加的人群的影响。我们表明，如果无症状的世代间隔长于相应的有症状世代间隔，那么年龄和症状之间的相关性会导致在疫情下降期间（无论是由于易感人群的消耗还是干预），感染年龄的下降。总之，这些结果表明，需要探索传播动力学中时间尺度差异的作用，以及与行为变化一起，来解释疫情的早期阶段（例如，在估计基本繁殖数方面）和整个疫情期间（例如，将感染年龄的变化与发病率变化的时期联系起来）的特征。
