发表机构
Universitat Rovira i Virgili; University of Padova; Pacific Northwest National Laboratory（罗维拉-伊尔吉利大学; 帕多瓦大学; 太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出链式SIR模型，通过按再感染次数索引区室，在再感染空间中产生行波，解析波速并量化免疫应答对多种季节性病毒再感染率的降低作用。
AI 中文摘要
允许再感染的传染病模型，如SIS或SIRS框架，通常将所有感染视为可交换且独立的，从而抹去了个体再感染历史的时间结构。在此，我们引入一个极简的“链式SIR”模型，通过按再感染次数索引区室，并允许传播、恢复和衰退率依赖于该次数，来解决这一局限。该动力学沿再感染阶梯产生平流通量，在再感染空间中形成行波，并在人群的终生感染历史中产生连贯结构。我们推导出行波速度的解析表达式，并确定此类队列波存在并持续的一般条件，为解释长期流行模式以及年龄或免疫结构地方性流行病的出现提供了新视角。最后，将速度公式与纵向队列数据进行比较，我们量化了针对几种季节性病毒（包括甲型流感H3N2和呼吸道合胞病毒（RSV））的免疫应答所导致的再感染率降低。
英文摘要
Infectious disease models that allow for reinfection, such as the SIS or SIRS frameworks, typically treat all infections as exchangeable and independent, erasing the temporal structure of individual reinfection histories. Here, we introduce a minimal "chain SIR" model that resolves this limitation by indexing compartments by reinfection count and allowing transmission, recovery, and waning rates to depend on that count. The dynamics generate an advective flux along the reinfection ladder, producing traveling waves in reinfection space and a coherent structure in the population's lifetime infection histories. We derive an analytical expression for the wave speed and identify the general conditions under which such cohort waves exist and persist, offering a new lens to interpret long-term prevalence patterns and the emergence of age- or immunity-structured endemicity. Finally, comparing the speed formula with longitudinal cohort data, we quantify the reduction in reinfection rate attributable to immunological responses for several seasonal viruses, including influenza A/H3N2 and respiratory syncytial virus (RSV).
Comments11 pages, 4 figures; SI 6 pages, 3 figures
