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感染后死亡率的流行病学影响。

Epidemiological impacts of post-infection mortality.

机构信息

Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA.

Department of Integrative Biology, University of California, Berkeley, CA, USA.

出版信息

Proc Biol Sci. 2023 Jul 12;290(2002):20230343. doi: 10.1098/rspb.2023.0343.

Abstract

Infectious diseases may cause some long-term damage to their host, leading to elevated mortality even after recovery. Mortality due to complications from so-called 'long COVID' is a stark illustration of this potential, but the impacts of such post-infection mortality (PIM) on epidemic dynamics are not known. Using an epidemiological model that incorporates PIM, we examine the importance of this effect. We find that in contrast to mortality during infection, PIM can induce epidemic cycling. The effect is due to interference between elevated mortality and reinfection through the previously infected susceptible pool. In particular, robust immunity (via decreased susceptibility to reinfection) reduces the likelihood of cycling; on the other hand, disease-induced mortality can interact with weak PIM to generate periodicity. In the absence of PIM, we prove that the unique endemic equilibrium is stable and therefore our key result is that PIM is an overlooked phenomenon that is likely to be destabilizing. Overall, given potentially widespread effects, our findings highlight the importance of characterizing heterogeneity in susceptibility (via both PIM and robustness of host immunity) for accurate epidemiological predictions. In particular, for diseases without robust immunity, such as SARS-CoV-2, PIM may underlie complex epidemiological dynamics especially in the context of seasonal forcing.

摘要

传染病可能会对宿主造成一些长期损害,导致即使在康复后死亡率仍居高不下。所谓“长新冠”并发症导致的死亡率就鲜明地说明了这种潜在风险,但感染后死亡率(PIM)对疫情动态的影响尚不清楚。本研究利用一种纳入 PIM 的流行病学模型来考察这种影响的重要性。结果发现,与感染期间的死亡率不同,PIM 可诱导疫情周期性循环。这种效应归因于死亡率升高与通过先前感染的易感人群再次感染之间的相互干扰。具体而言,强健的免疫(通过降低再次感染的易感性)降低了循环发生的可能性;另一方面,疾病引起的死亡率可与较弱的 PIM 相互作用产生周期性。在不存在 PIM 的情况下,我们证明了唯一的地方病平衡点是稳定的,因此我们的主要结果是 PIM 是一个被忽视的现象,很可能会导致不稳定。总的来说,鉴于其潜在的广泛影响,我们的研究结果强调了对易感性(通过 PIM 和宿主免疫力的稳健性)进行异质性特征分析对于准确的流行病学预测的重要性。特别是对于没有强健免疫的疾病,如 SARS-CoV-2,PIM 可能是复杂流行病学动态的基础,尤其是在季节性驱动因素的背景下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec7a/10336371/98852e9d6b99/rspb20230343f01.jpg

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