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再感染能告诉我们关于R的什么信息?梅毒传播的建模案例研究。

What could re-infection tell us about R? A modeling case-study of syphilis transmission.

作者信息

Feldman Joshua, Mishra Sharmistha

机构信息

Centre for Urban Health Solutions, St. Michael's Hospital, University of Toronto, Canada.

Department of Medicine, Division of Infectious Disease, University of Toronto, Canada.

出版信息

Infect Dis Model. 2019 Sep 17;4:257-264. doi: 10.1016/j.idm.2019.09.002. eCollection 2019.

Abstract

Many infectious diseases can lead to re-infection. We examined the relationship between the prevalence of repeat infection and the basic reproductive number (R). First we solved a generic, deterministic compartmental model of re-infection to derive an analytic solution for the relationship. We then numerically solved a disease-specific model of syphilis transmission that explicitly tracked re-infection. We derived a generic expression that reflects a non-linear and monotonically increasing relationship between proportion re-infection and R and which is attenuated by entry/exit rates and recovery (i.e. treatment). Numerical simulations from the syphilis model aligned with the analytic relationship. Re-infection proportions could be used to understand how far regions are from epidemic control, and should be included as a routine indicator in infectious disease surveillance.

摘要

许多传染病会导致再次感染。我们研究了重复感染率与基本再生数(R)之间的关系。首先,我们求解了一个通用的、确定性的再感染 compartmental 模型,以得出这种关系的解析解。然后,我们对一个明确跟踪再感染情况的梅毒传播疾病特异性模型进行了数值求解。我们推导出了一个通用表达式,该表达式反映了再感染比例与R之间的非线性且单调递增的关系,并且这种关系会因进出率和康复(即治疗)而减弱。梅毒模型的数值模拟结果与解析关系相符。再感染比例可用于了解各地区距离疫情控制还有多远,并且应作为传染病监测中的一项常规指标纳入其中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b357/6806446/6cb6f271cc94/gr3.jpg

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