The doubling time analysis for modified infectious disease Richards model with applications to COVID-19 pandemic
The doubling time analysis for modified infectious disease Richards model with applications to COVID-19 pandemic
复制标题
DOI：
10.3934/mbe.2022150
复制
发表时间：
2022-01-01
影响因子：
2.6
通讯作者：
Zhao, Yichuan
中科院分区：
文献类型：
--
作者：
Smirnova, Alexandra;Pidgeon, Brian;Zhao, Yichuan
在缺乏有关新发传染病传播机制的可靠信息的情况下，简单的现象学模型可以提供一个起点来评估正在发生的突发公共卫生事件的潜在后果，特别是当对该疾病的流行病学特征知之甚少或存在很大不确定性时。在本研究中，我们采用改进的理查兹模型来分析流行病的增长，包括：1）直到流行病高峰为止累计病例翻倍的次数；2）在疫情爆发早期上升阶段连续翻倍时间间隔增加的速率。我们对倍增时间的理论分析与严格的数值模拟和不确定性量化相结合，使用了 COVID-19 大流行的合成和真实数据。倍增时间方法允许利用早期流行病数据来区分最危险的威胁和最不易传播的疾病，其中最危险的威胁的规模在几乎不变的时间间隔内翻倍很多次，而传染性最低的疾病的规模仅翻倍几次，且倍增周期迅速增长。
In the absence of reliable information about transmission mechanisms for emerging infec-tious diseases, simple phenomenological models could provide a starting point to assess the potential outcomes of unfolding public health emergencies, particularly when the epidemiological characteris-tics of the disease are poorly understood or subject to substantial uncertainty. In this study, we employ the modified Richards model to analyze the growth of an epidemic in terms of 1) the number of times cumulative cases double until the epidemic peaks and 2) the rate at which the intervals between consec-utive doubling times increase during the early ascending stage of the outbreak. Our theoretical analysis of doubling times is combined with rigorous numerical simulations and uncertainty quantification us -ing synthetic and real data for COVID-19 pandemic. The doubling-time approach allows to employ early epidemic data to differentiate between the most dangerous threats, which double in size many times over the intervals that are nearly invariant, and the least transmissible diseases, which double in size only a few times with doubling periods rapidly growing.