SHAR and effective SIR models: from dengue fever toy models to a COVID-19 fully parametrized SHARUCD framework

Q2 Mathematics
M. Aguiar, N. Stollenwerk
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引用次数: 16

Abstract

We review basic models of severe/hospitalized and mild/asymptomatic infection spreading (with classes of susceptibles S, hopsitalized H, asymptomatic A and recovered R, hence SHAR-models) and develop the notion of comparing different models on the same data set as exemplified in the comparison of SHAR models with effective SIR models, where only the H-class of the SHAR model is taken into account in the SIR model. This is done via the so-called Bayes factor. A simpler pair of models with analytical expressions up to the Bayes factor will be briefly mentioned as well. The notions developed with respect to dengue fever epidemiology will then be used to analyze recently becoming available data on coronavirus disease 2019, COVID-19, where models can be fully parametrized including hospital admission and more extensions like intensive care unit (ICU) admission and deceased, always with a close look on as simple as possible models but not simpler, as exercised in Ocham’s razor and analyzed by e.g. the Bayes factor. We present the resulting models of SHAR-type with additional classes of ICU admissions U, and deceased D, and for data analysis of cumulative disease data, also accounting the cumulative classes C, in the so-called SHARUCD framework. Besides a first basic version, SHARUCD model 1, we investigate also in detail a refined version, SHARUCD model 2, which could be achieved by a closer analysis of available data only obtained after the exponential growth phase of the epidemic, when lockdown control measures showed effects. Namely, the ICU admissions turned out to be more in synchrony with the hospitalized than with e.g. the deceased cases, such that we could adjust the transitions so that ICU admissions are modeled like hospitalizations in model 2, and not like recovery or disease induced death as assumed in model 1, explaining much better the empirical data, specially after the effects of the lockdown became visible. Special attention will be given here, for the first time, to the initial phase of the COVID-19 epidemics, before all variables entered into the exponential phase, and its interplay between asymptomatic and severe hospitalized cases, always in close check with the SIR-limiting case. Such improved understanding of the initial phase will help in the future analysis of re-emergent outbreaks of COVID-19, likely to happen in the next or a subsequent respiratory disease season in autumn or winter.
shaucd和有效SIR模型:从登革热玩具模型到COVID-19全参数化SHARUCD框架
我们回顾了严重/住院和轻度/无症状感染传播的基本模型(包括易感人群S、跳跃H、无症状A和康复R,因此是SHAR模型),并提出了在同一数据集上比较不同模型的概念,如SHAR模型与有效SIR模型的比较所示,其中在SIR模型中仅考虑SHAR模型的H类。这是通过所谓的贝叶斯因子实现的。还将简要介绍一对具有高达贝叶斯因子的分析表达式的更简单的模型。登革热流行病学的概念将用于分析最近获得的关于2019冠状病毒病新冠肺炎的数据,其中模型可以完全参数化,包括住院和更多扩展,如重症监护室(ICU)入院和死亡,始终密切关注尽可能简单的模型,但不能更简单,在Ocham™s剃刀,并通过例如贝叶斯因子进行分析。我们在所谓的SHARUCD框架中,提出了SHAR型的结果模型,其中包括ICU入院的额外类别U和死亡的D,以及对累积疾病数据的数据分析,也包括累积类别C。除了第一个基本版本SHARUCD模型1外,我们还详细研究了一个改进版本SHARUCD模型2,该模型可以通过对仅在疫情指数增长阶段后获得的可用数据进行更仔细的分析来实现,当时封锁控制措施显示出效果。也就是说,ICU入院与住院患者的同步性比与死亡病例的同步性更强,因此我们可以调整转换,使ICU入院与模型2中的住院患者相似,而不像模型1中假设的康复或疾病导致的死亡,更好地解释了经验数据,特别是在封锁的影响变得明显之后。这里将首次特别关注新冠肺炎疫情的初始阶段,在所有变量进入指数阶段之前,以及无症状和严重住院病例之间的相互作用,始终与SIR-轻度病例密切相关。这种对初始阶段的更好理解将有助于未来分析新冠肺炎的再合并疫情,这些疫情可能发生在秋季或冬季的下一个或随后的呼吸道疾病季节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Communication in Biomathematical Sciences
Communication in Biomathematical Sciences Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (miscellaneous)
CiteScore
3.60
自引率
0.00%
发文量
7
审稿时长
24 weeks
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