University of Technology Sydney, Sydney, NSW, 2007, Australia.
Sci Rep. 2022 Apr 7;12(1):5891. doi: 10.1038/s41598-022-09879-2.
The COVID-19 pandemic has posed significant challenges in modeling its complex epidemic transmissions, infection and contagion, which are very different from known epidemics. The challenges in quantifying COVID-19 complexities include effectively modeling its process and data uncertainties. The uncertainties are embedded in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more apparent in the first 2 months of the COVID-19 pandemic, when the relevant knowledge, case reporting and testing were all limited. Here we introduce a novel hybrid approach SUDR by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments to a Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model. First, SUDR (1) characterizes and distinguishes Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections. Second, SUDR characterizes the probabilistic density of infections by capturing exogenous processes like clustering contagion interactions, superspreading, and social reinforcement. Lastly, SUDR approximates the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. By sampling from the mean-field posterior distribution with reasonable priors, SUDR handles the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data. The results demonstrate a deeper quantitative understanding of the above uncertainties, in comparison with classic SIR, time-dependent SIR, and probabilistic SIR models.
新冠疫情对建模其复杂的疫情传播、感染和传染带来了巨大挑战，这与已知的疫情非常不同。量化新冠疫情复杂性的挑战包括有效建模其过程和数据不确定性。这些不确定性包含在隐性和高比例未记录的感染、无症状传染、感染的社会强化以及报告数据中的各种质量问题中。这些不确定性在新冠疫情的前两个月更加明显，当时相关知识、病例报告和检测都很有限。在这里，我们引入了一种新颖的混合方法 SUDR，通过将基础的有隔间的传染病易感-感染-恢复（SIR）模型扩展到包含两个隔间的易感-未记录感染-记录感染-恢复（SUDR）模型。首先，SUDR（1）描述并区分了新冠疫情潜伏期和无症状感染期间常见的未记录（U）和记录（D）感染。其次，SUDR 通过捕获集群传染相互作用、超级传播和社会强化等外生过程来描述感染的概率密度。最后，SUDR 通过将贝叶斯推断纳入 SUDR 来近似随时间推移的新冠疫情流行的密度似然。与现有的新冠疫情模型不同，SUDR 描述了未知传播过程中的未记录感染。为了捕捉新冠疫情传染期间的时间传播和社会强化不确定性，传播率通过未记录传染性病例的时变密度函数进行建模。通过从具有合理先验的平均场后验分布中进行采样，SUDR 处理了公共新冠疫情病例数据中广泛存在的随机性、噪声和稀疏性。与经典 SIR、时变 SIR 和概率 SIR 模型相比，结果表明对上述不确定性有更深入的定量理解。
