论文标题
使用时间变化的参数SIRD模型桥接COVID-19数据和流行病学模型
Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model
论文作者
论文摘要
本文扩展了流行病学的规范模型SIRD模型，以实时测量Covid-19大流行的姿势的时间变化参数。模型参数中的时间变化是使用针对与大流行有关的典型每日计数数据设计的广义自回归分数建模结构捕获的。最终的规范允许具有非常低的计算成本的灵活但简约的模型结构。当数据稀缺并且不确定性丰富时，这在大流行病开始时尤为重要。完整的样本结果表明，包括美国，巴西和俄罗斯在内的国家仍然无法遏制美国的大流行，而美国的表现最差。此外，伊朗和韩国可能会体验第二波大流行。一项实时练习表明，所提出的结构在使用滚动窗口的竞争对手之前，在大流行的当前立场上提供了及时，精确的信息。反过来，这将转变为活动病例的准确短期预测。我们进一步修改模型以允许未报告的情况。结果表明，随着测试数量的增加，这些情况的存在对样品结束的估计结果的影响减少了。
This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing.
