论文标题
推断Covid-19的传播：随时间变化的报告率在流行病学建模中的作用
Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
论文作者
论文摘要
流行病学模型的作用对于在公共卫生紧急情况下（例如COVID-19大流行）通知公共卫生官员至关重要。但是，传统的流行病学模型无法捕获缓解策略的时变作用，也不说明活动案例的报告不足，因此在模型参数的估计中引入了偏见。为了推断更准确的参数估计并减少这些估计值的不确定性，我们将SIR和SEIR流行病学模型扩展了两个时间变化的参数，这些参数捕获了传输速率以及向卫生官员报告主动病例的速率。使用两个实际数据集的COVID-19案例，我们通过我们的SIR和SEIR模型进行贝叶斯推断，并具有随时间变化的传输和报告速率，以及通过其标准速率以恒定速率进行的；我们的方法提供了更现实的解释参数估计值，并且不确定性降低了为期一周的预测。此外，我们发现在我们考虑的数据中的活动案例数量中，我们发现大流行的初始阶段比以前报道的更为广泛。
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and one-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.
