发表机构
Mississippi State University; The University of Alabama in Huntsville; Florida Atlantic University; Texas Christian University（密西西比州立大学; 阿拉巴马大学亨茨维尔分校; 佛罗里达大西洋大学; 德克萨斯基督教大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种多尺度模型，通过症状评分连接免疫与流行病尺度，模拟个体行为改变，发现其延迟并降低疫情高峰，且轨迹与标准SEIR模型定性相似。
AI 中文摘要
对于许多传染病而言，行为改变并非人群对病例数上升的反应，而是由个体感觉自身病情严重程度触发的个人行为。然而，大多数模型未能捕捉疾病严重程度如何驱动这种反应。将免疫学尺度与流行病学尺度联系起来的多尺度模型似乎很适合捕捉这种行为。大多数多尺度模型通过将宿主体内病毒载量与人群传播率联系起来的连接函数来连接两个尺度，这些连接函数是病毒载量的函数。然而，病毒载量常与症状严重程度不一致，因此可能不适合用于指导人群层面的行为。我们提出了一种新颖的多尺度模型，其中有症状个体根据全身症状评分（由体内免疫动态决定的肌肉酸痛、疲劳、头痛和发热感的综合指标）动态调整其传播行为，而无症状个体不改变行为。我们发现，更强的疾病驱动行为改变会延迟并降低流行病高峰。此外，在多种不同的连接函数选择下，所得流行病轨迹在定性上与无行为改变的标准SEIR模型相似。这与行为改变由人群层面反馈（如报告病例或死亡）驱动的模型形成对比，后者可能产生定性不同的特征，如平台期、肩部或延长的流行病消退期。当使用症状评分的指数函数作为连接函数时，我们表明标准SEIR模型能够紧密重现多尺度模型在给定时间点产生的总感染人数。
英文摘要
For many infectious diseases, behavior change is not a population-level reaction to rising case counts, but a personal one, triggered by how sick an individual feels. Yet most models fail to capture how illness severity drives this response. Multi-scale models that link the immunological scale to the epidemiological scale seem well-suited for capturing this behavior. Most multi-scale models link within-host viral load to population-level transmission rates through linking functions that are a function of viral load. However, viral load is often misaligned with symptom severity and thus possibly ill-suited for the purpose of informing behavior at a population level. We present a novel multi-scale model in which symptomatic individuals dynamically modify their transmission based on systemic symptom scores (a composite measure of muscle ache, fatigue, headache, and feverishness informed by within-host immune dynamics) while asymptomatic individuals do not change behavior. We find that stronger illness-driven behavior change delays and lowers the epidemic peak. Moreover, the resulting epidemic trajectory remains qualitatively similar to that of a standard SEIR model without behavior change, across several distinct choices of linking functions connecting the two scales. This stands in contrast to models where behavior change is driven by population-level feedback (e.g., reported cases or deaths), which can produce qualitatively distinct signatures such as plateaus, shoulders, or an elongated epidemic decline. In the case when an exponential function of symptom score is used as a linking function, we show that the standard SEIR model can closely reproduce the total number of infectious individuals at a given point in time generated through the multi-scale level.
